Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

NASA Astrophysics Data System (ADS)

Luo, Junhui; Wu, Chao; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun

2018-01-01

At present, the prediction of soft foundation settlement mostly use the exponential curve and hyperbola deferred approximation method, and the correlation between the results is poor. However, the application of neural network in this area has some limitations, and none of the models used in the existing cases adopted the TS fuzzy neural network of which calculation combines the characteristics of fuzzy system and neural network to realize the mutual compatibility methods. At the same time, the developed and optimized calculation program is convenient for engineering designers. Taking the prediction and analysis of soft foundation settlement of gully soft soil in granite area of Guangxi Guihe road as an example, the fuzzy neural network model is established and verified to explore the applicability. The TS fuzzy neural network is used to construct the prediction model of settlement and deformation, and the corresponding time response function is established to calculate and analyze the settlement of soft foundation. The results show that the prediction of short-term settlement of the model is accurate and the final settlement prediction result has certain engineering reference value.

Motion control of planar parallel robot using the fuzzy descriptor system approach.

PubMed

Vermeiren, Laurent; Dequidt, Antoine; Afroun, Mohamed; Guerra, Thierry-Marie

2012-09-01

This work presents the control of a two-degree of freedom parallel robot manipulator. A quasi-LPV approach, through the so-called TS fuzzy model and LMI constraints problems is used. Moreover, in this context a way to derive interesting control laws is to keep the descriptor form of the mechanical system. Therefore, new LMI problems have to be defined that helps to reduce the conservatism of the usual results. Some relaxations are also proposed to leave the pure quadratic stability/stabilization framework. A comparison study between the classical control strategies from robotics and the control design using TS fuzzy descriptor models is carried out to show the interest of the proposed approach. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Stability analysis of nonlinear Roesser-type two-dimensional systems via a homogenous polynomial technique

NASA Astrophysics Data System (ADS)

Zhang, Tie-Yan; Zhao, Yan; Xie, Xiang-Peng

2012-12-01

This paper is concerned with the problem of stability analysis of nonlinear Roesser-type two-dimensional (2D) systems. Firstly, the fuzzy modeling method for the usual one-dimensional (1D) systems is extended to the 2D case so that the underlying nonlinear 2D system can be represented by the 2D Takagi—Sugeno (TS) fuzzy model, which is convenient for implementing the stability analysis. Secondly, a new kind of fuzzy Lyapunov function, which is a homogeneous polynomially parameter dependent on fuzzy membership functions, is developed to conceive less conservative stability conditions for the TS Roesser-type 2D system. In the process of stability analysis, the obtained stability conditions approach exactness in the sense of convergence by applying some novel relaxed techniques. Moreover, the obtained result is formulated in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, a numerical example is also given to demonstrate the effectiveness of the proposed approach.

Robust nonlinear variable selective control for networked systems

NASA Astrophysics Data System (ADS)

Rahmani, Behrooz

2016-10-01

This paper is concerned with the networked control of a class of uncertain nonlinear systems. In this way, Takagi-Sugeno (T-S) fuzzy modelling is used to extend the previously proposed variable selective control (VSC) methodology to nonlinear systems. This extension is based upon the decomposition of the nonlinear system to a set of fuzzy-blended locally linearised subsystems and further application of the VSC methodology to each subsystem. To increase the applicability of the T-S approach for uncertain nonlinear networked control systems, this study considers the asynchronous premise variables in the plant and the controller, and then introduces a robust stability analysis and control synthesis. The resulting optimal switching-fuzzy controller provides a minimum guaranteed cost on an H2 performance index. Simulation studies on three nonlinear benchmark problems demonstrate the effectiveness of the proposed method.

Reliable fuzzy H∞ control for active suspension of in-wheel motor driven electric vehicles with dynamic damping

NASA Astrophysics Data System (ADS)

Shao, Xinxin; Naghdy, Fazel; Du, Haiping

2017-03-01

A fault-tolerant fuzzy H∞ control design approach for active suspension of in-wheel motor driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The controller is designed based on the quarter-car active suspension model with a dynamic-damping-in-wheel-motor-driven-system, in which the suspended motor is operated as a dynamic absorber. The Takagi-Sugeno (T-S) fuzzy model is used to model this suspension with possible sprung mass variation. The parallel-distributed compensation (PDC) scheme is deployed to derive a fault-tolerant fuzzy controller for the T-S fuzzy suspension model. In order to reduce the motor wear caused by the dynamic force transmitted to the in-wheel motor, the dynamic force is taken as an additional controlled output besides the traditional optimization objectives such as sprung mass acceleration, suspension deflection and actuator saturation. The H∞ performance of the proposed controller is derived as linear matrix inequalities (LMIs) comprising three equality constraints which are solved efficiently by means of MATLAB LMI Toolbox. The proposed controller is applied to an electric vehicle suspension and its effectiveness is demonstrated through computer simulation.

Dynamic Fuzzy Model Development for a Drum-type Boiler-turbine Plant Through GK Clustering

NASA Astrophysics Data System (ADS)

Habbi, Ahcène; Zelmat, Mimoun

2008-10-01

This paper discusses a TS fuzzy model identification method for an industrial drum-type boiler plant using the GK fuzzy clustering approach. The fuzzy model is constructed from a set of input-output data that covers a wide operating range of the physical plant. The reference data is generated using a complex first-principle-based mathematical model that describes the key dynamical properties of the boiler-turbine dynamics. The proposed fuzzy model is derived by means of fuzzy clustering method with particular attention on structure flexibility and model interpretability issues. This may provide a basement of a new way to design model based control and diagnosis mechanisms for the complex nonlinear plant.

Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T-S Fuzzy Observer-Based Implementation.

PubMed

Li, Linlin; Ding, Steven X; Qiu, Jianbin; Yang, Ying

2017-02-01

This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an L ∞ / L 2 type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the L ∞ / L 2 observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

PubMed

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.

Analysis, control and design of a non-inverting buck-boost converter: A bump-less two-level T-S fuzzy PI control.

PubMed

Almasi, Omid Naghash; Fereshtehpoor, Vahid; Khooban, Mohammad Hassan; Blaabjerg, Frede

2017-03-01

In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi-Sugeno-Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Control Synthesis of Discrete-Time T-S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach.

PubMed

Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Xue, Yusheng

2016-03-01

This paper deals with the problem of control synthesis of discrete-time Takagi-Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new multiinstant fuzzy control scheme and a new class of fuzzy Lyapunov functions, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions, are proposed for implementing the object of relaxed control synthesis. Then, relaxed stabilization conditions are derived with less conservatism than existing ones. Furthermore, the relaxation quality of obtained stabilization conditions is further ameliorated by developing an efficient slack variable approach, which presents a multipolynomial dependence on the normalized fuzzy weighting functions at the current and past instants of time. Two simulation examples are given to demonstrate the effectiveness and benefits of the results developed in this paper.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

PubMed

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Coordinated control system modelling of ultra-supercritical unit based on a new T-S fuzzy structure.

PubMed

Hou, Guolian; Du, Huan; Yang, Yu; Huang, Congzhi; Zhang, Jianhua

2018-03-01

The thermal power plant, especially the ultra-supercritical unit is featured with severe nonlinearity, strong multivariable coupling. In order to deal with these difficulties, it is of great importance to build an accurate and simple model of the coordinated control system (CCS) in the ultra-supercritical unit. In this paper, an improved T-S fuzzy model identification approach is proposed. First of all, the k-means++ algorithm is employed to identify the premise parameters so as to guarantee the number of fuzzy rules. Then, the local linearized models are determined by using the incremental historical data around the cluster centers, which are obtained via the stochastic gradient descent algorithm with momentum and variable learning rate. Finally, with the proposed method, the CCS model of a 1000 MW USC unit in Tai Zhou power plant is developed. The effectiveness of the proposed approach is validated by the given extensive simulation results, and it can be further employed to design the overall advanced controllers for the CCS in an USC unit. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Deriving and Analyzing Analytical Structures of a Class of Typical Interval Type-2 TS Fuzzy Controllers.

PubMed

Zhou, Haibo; Ying, Hao

2017-09-01

A conventional controller's explicit input-output mathematical relationship, also known as its analytical structure, is always available for analysis and design of a control system. In contrast, virtually all type-2 (T2) fuzzy controllers are treated as black-box controllers in the literature in that their analytical structures are unknown, which inhibits precise and comprehensive understanding and analysis. In this regard, a long-standing fundamental issue remains unresolved: how a T2 fuzzy set's footprint of uncertainty, a key element differentiating a T2 controller from a type-1 (T1) controller, affects a controller's analytical structure. In this paper, we describe an innovative technique for deriving analytical structures of a class of typical interval T2 (IT2) TS fuzzy controllers. This technique makes it possible to analyze the analytical structures of the controllers to reveal the role of footprints of uncertainty in shaping the structures. Specifically, we have mathematically proven that under certain conditions, the larger the footprints, the more the IT2 controllers resemble linear or piecewise linear controllers. When the footprints are at their maximum, the IT2 controllers actually become linear or piecewise linear controllers. That is to say the smaller the footprints, the more nonlinear the controllers. The most nonlinear IT2 controllers are attained at zero footprints, at which point they become T1 controllers. This finding implies that sometimes if strong nonlinearity is most important and desired, one should consider using a smaller footprint or even just a T1 fuzzy controller. This paper exemplifies the importance and value of the analytical structure approach for comprehensive analysis of T2 fuzzy controllers.

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal; Ahmad, Siti Anom; Escudero, Javier

2017-07-01

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.

Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

PubMed

Syed Ali, M; Vadivel, R; Saravanakumar, R

2018-06-01

This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Switching control of an R/C hovercraft: stabilization and smooth switching.

PubMed

Tanaka, K; Iwasaki, M; Wang, H O

2001-01-01

This paper presents stable switching control of an radio-controlled (R/C) hovercraft that is a nonholonomic (nonlinear) system. To exactly represent its nonlinear dynamics, more importantly, to maintain controllability of the system, we newly propose a switching fuzzy model that has locally Takagi-Sugeno (T-S) fuzzy models and switches them according to states, external variables, and/or time. A switching fuzzy controller is constructed by mirroring the rule structure of the switching fuzzy model of an R/C hovercraft. We derive linear matrix inequality (LMI) conditions for ensuring the stability of the closed-loop system consisting of a switching fuzzy model and controller. Furthermore, to guarantee smooth switching of control input at switching boundaries, we also derive a smooth switching condition represented in terms of LMIs. A stable switching fuzzy controller satisfying the smooth switching condition is designed by simultaneously solving both of the LMIs. The simulation and experimental results for the trajectory control of an R/C hovercraft show the validity of the switching fuzzy model and controller design, particularly, the smooth switching condition.

A Novel Adjustment Method for Shearer Traction Speed through Integration of T-S Cloud Inference Network and Improved PSO

PubMed Central

Si, Lei; Wang, Zhongbin; Yang, Yinwei

2014-01-01

In order to efficiently and accurately adjust the shearer traction speed, a novel approach based on Takagi-Sugeno (T-S) cloud inference network (CIN) and improved particle swarm optimization (IPSO) is proposed. The T-S CIN is built through the combination of cloud model and T-S fuzzy neural network. Moreover, the IPSO algorithm employs parameter automation adjustment strategy and velocity resetting to significantly improve the performance of basic PSO algorithm in global search and fine-tuning of the solutions, and the flowchart of proposed approach is designed. Furthermore, some simulation examples are carried out and comparison results indicate that the proposed method is feasible, efficient, and is outperforming others. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system. PMID:25506358

Research on Dynamic Monitoring (1990-2010)of Schistosomiasis Vector- Snail at Xinmin Beach, Gaoyou Lake, Jiangsu Province, China

NASA Astrophysics Data System (ADS)

Liu, Zhaoyan; Li, Chuanrong; Tang, Lingli; Zhou, Xiaonong; Ma, Lingling

2014-11-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact, this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to snail breeding and reproduction, and can also provide the efficient information to determine the location, area, and spread tendency of snail. Based on the T-S (Takagi-Sugeno) fuzzy information theory, a quantitative remote sensing monitoring model of snail has been developed in previous wok. In a case study, this paper will take Xinmin beach, Gaoyou Lake as new research area, carry out 20 years (1990 - 2010) dynamic monitoring, to further validate the effectiveness of the T-S Fuzzy RS snail monitoring model.

Research on Dynamic Monitoring (1990-2010) of Schistosomiasis Vector-Snail at Xinmin Beach, Gaoyou Lake, Jiangsu Province, China

NASA Astrophysics Data System (ADS)

Liu, Zhaoyan; Li, Chuanrong; Tang, Lingli; Zhou, Xiaonong; Ma, Lingling

2014-11-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact, this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to snail breeding and reproduction, and can also provide the efficient information to determine the location, area, and spread tendency of snail. Based on the T-S (Takagi-Sugeno) fuzzy information theory, a quantitative remote sensing monitoring model of snail has been developed in previous wok. In a case study, this paper will take Xinmin beach, Gaoyou Lake as new research area, carry out 20 years (1990 - 2010) dynamic monitoring, to further validate the effectiveness of the T-S Fuzzy RS snail monitoring model.

DC motor speed control using fuzzy logic controller

NASA Astrophysics Data System (ADS)

Ismail, N. L.; Zakaria, K. A.; Nazar, N. S. Moh; Syaripuddin, M.; Mokhtar, A. S. N.; Thanakodi, S.

2018-02-01

The automatic control has played a vital role in the advance of engineering and science. Nowadays in industries, the control of direct current (DC) motor is a common practice thus the implementation of DC motor controller speed is important. The main purpose of motor speed control is to keep the rotation of the motor at the present speed and to drive a system at the demand speed. The main purpose of this project is to control speed of DC Series Wound Motor using Fuzzy Logic Controller (FLC). The expectation of this project is the Fuzzy Logic Controller will get the best performance compared to dc motor without controller in terms of settling time (Ts), rise time (Tr), peak time (Tp) and percent overshoot (%OS).

A new design of robust H∞ sliding mode control for uncertain stochastic T-S fuzzy time-delay systems.

PubMed

Gao, Qing; Feng, Gang; Xi, Zhiyu; Wang, Yong; Qiu, Jianbin

2014-09-01

In this paper, a novel dynamic sliding mode control scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing sliding mode control approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories can be driven onto the sliding surface in finite time almost certainly. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities; moreover, the sliding-mode controller can be obtained simultaneously. Simulation results illustrating the advantages and effectiveness of the proposed approaches are also provided.

Universal fuzzy integral sliding-mode controllers for stochastic nonlinear systems.

PubMed

Gao, Qing; Liu, Lu; Feng, Gang; Wang, Yong

2014-12-01

In this paper, the universal integral sliding-mode controller problem for the general stochastic nonlinear systems modeled by Itô type stochastic differential equations is investigated. One of the main contributions is that a novel dynamic integral sliding mode control (DISMC) scheme is developed for stochastic nonlinear systems based on their stochastic T-S fuzzy approximation models. The key advantage of the proposed DISMC scheme is that two very restrictive assumptions in most existing ISMC approaches to stochastic fuzzy systems have been removed. Based on the stochastic Lyapunov theory, it is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time, and moreover, the stochastic stability of the sliding motion can be guaranteed in terms of linear matrix inequalities. Another main contribution is that the results of universal fuzzy integral sliding-mode controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy integral sliding-mode controllers, are provided, respectively. Simulation results from an inverted pendulum example are presented to illustrate the advantages and effectiveness of the proposed approaches.

Measuring uncertainty by extracting fuzzy rules using rough sets and extracting fuzzy rules under uncertainty and measuring definability using rough sets

NASA Technical Reports Server (NTRS)

Worm, Jeffrey A.; Culas, Donald E.

1991-01-01

Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. This paper examines the concepts of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to provide the possible optimal solution. By incorporating principles from these theories, a decision-making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much we believe these rules is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of its fuzzy attributes is studied.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

PubMed

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Influence of post-starvation extraction time and prey-specific diet in Tityus serrulatus scorpion venom composition and hyaluronidase activity.

PubMed

Pucca, Manuela Berto; Amorim, Fernanda Gobbi; Cerni, Felipe Augusto; Bordon, Karla de Castro Figueiredo; Cardoso, Iara Aimê; Anjolette, Fernando Antonio Pino; Arantes, Eliane Candiani

2014-11-01

The role of diet in venom composition has been a topic of intense research interest. This work presents evidence that the variation in the venom composition from the scorpion Tityus serrulatus (Ts) is closely associated with post-starvation extraction time and prey-specific diet. The scorpions were fed with cockroach, cricket, peanut beetle or giant Tenebrio. The venoms demonstrated a pronounced difference in the total protein and toxins composition, which was evaluated by electrophoresis, reversed-phase chromatography, densitometry, hyaluronidase activity and N-terminal sequencing. Indeed, many toxins and peptides, such as Ts1, Ts2, Ts4, Ts5, Ts6, Ts15, Ts19 frag. II, hypotensins 1 and 3, PAPE peptide and peptide 9797 (first described in Ts venom), were all identified in different proportions in the analyzed Ts venoms. This study is pioneer on assessing the influence of the starvation time and the prey diet on hyaluronidase activity as well as to describe a modification of Tricine-gel-electrophoresis to evaluate this enzyme activity. Altogether, this study reveal a large contribution of the extraction time and diet on Ts venom variability as well as present a background to recommend the cockroach diet to obtain higher protein content and the cricket diet to obtain higher hyaluronidase specific activity. Copyright © 2014 Elsevier Ltd. All rights reserved.

From fuzzy recurrence plots to scalable recurrence networks of time series

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2017-04-01

Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data.

Reachable set estimation for Takagi-Sugeno fuzzy systems against unknown output delays with application to tracking control of AUVs.

PubMed

Zhong, Zhixiong; Zhu, Yanzheng; Ahn, Choon Ki

2018-07-01

In this paper, we address the problem of reachable set estimation for continuous-time Takagi-Sugeno (T-S) fuzzy systems subject to unknown output delays. Based on the reachable set concept, a new controller design method is also discussed for such systems. An effective method is developed to attenuate the negative impact from the unknown output delays, which likely degrade the performance/stability of systems. First, an augmented fuzzy observer is proposed to capacitate a synchronous estimation for the system state and the disturbance term owing to the unknown output delays, which ensures that the reachable set of the estimation error is limited via the intersection operation of ellipsoids. Then, a compensation technique is employed to eliminate the influence on the system performance stemmed from the unknown output delays. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of autonomous underwater vehicles. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

DecisionMaker software and extracting fuzzy rules under uncertainty

NASA Technical Reports Server (NTRS)

Walker, Kevin B.

1992-01-01

Knowledge acquisition under uncertainty is examined. Theories proposed in deKorvin's paper 'Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets' are discussed as they relate to rule calculation algorithms. A data structure for holding an arbitrary number of data fields is described. Limitations of Pascal for loops in the generation of combinations are also discussed. Finally, recursive algorithms for generating all possible combination of attributes and for calculating the intersection of an arbitrary number of fuzzy sets are presented.

Design and implementation of the tree-based fuzzy logic controller.

PubMed

Liu, B D; Huang, C Y

1997-01-01

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.

Knowledge guided information fusion for segmentation of multiple sclerosis lesions in MRI images

NASA Astrophysics Data System (ADS)

Zhu, Chaozhe; Jiang, Tianzi

2003-05-01

In this work, T1-, T2- and PD-weighted MR images of multiple sclerosis (MS) patients, providing information on the properties of tissues from different aspects, are treated as three independent information sources for the detection and segmentation of MS lesions. Based on information fusion theory, a knowledge guided information fusion framework is proposed to accomplish 3-D segmentation of MS lesions. This framework consists of three parts: (1) information extraction, (2) information fusion, and (3) decision. Information provided by different spectral images is extracted and modeled separately in each spectrum using fuzzy sets, aiming at managing the uncertainty and ambiguity in the images due to noise and partial volume effect. In the second part, the possible fuzzy map of MS lesions in each spectral image is constructed from the extracted information under the guidance of experts' knowledge, and then the final fuzzy map of MS lesions is constructed through the fusion of the fuzzy maps obtained from different spectrum. Finally, 3-D segmentation of MS lesions is derived from the final fuzzy map. Experimental results show that this method is fast and accurate.

Measuring uncertainty by extracting fuzzy rules using rough sets

NASA Technical Reports Server (NTRS)

Worm, Jeffrey A.

1991-01-01

Despite the advancements in the computer industry in the past 30 years, there is still one major deficiency. Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. The methods are examined of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to possibly provide the optimal solution. By incorporating principles from these theories, a decision making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much these rules is believed is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of a set of fuzzy attributes is studied.

Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data

PubMed Central

Zhou, Shang-Ming; Lyons, Ronan A.; Brophy, Sinead; Gravenor, Mike B.

2012-01-01

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data. PMID:23272108

Constructing compact Takagi-Sugeno rule systems: identification of complex interactions in epidemiological data.

PubMed

Zhou, Shang-Ming; Lyons, Ronan A; Brophy, Sinead; Gravenor, Mike B

2012-01-01

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better. Copyright © 2017 Elsevier B.V. All rights reserved.

? observer-based decentralised fuzzy control design for nonlinear interconnected systems: an application to vehicle dynamics

NASA Astrophysics Data System (ADS)

Latrach, Chedia; Kchaou, Mourad; Guéguen, Hervé

2017-05-01

In this study, a decentralised output learning control strategy for a class of nonlinear interconnected systems is studied. Based on Takagi-Sugeno fuzzy (TS) model to approximate the considered interconnected nonlinear systems, a decentralised observer-based control scheme is designed to override the external disturbances such that the ? performance is achieved. The appealing attributes of this approach include: (1) the closed-loop system exhibits a robustness against nonlinear interconnections and external disturbance, (2) by one-step procedure, the gain matrices of observer and controller are obtained on a single step. In simulation results, the controller design is evaluated on the steering stability of a car where the nonlinear model describes the side slip, roll and yaw motions of the automotive vehicle equipped with four-wheel-steering and active suspension.

Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

NASA Astrophysics Data System (ADS)

Onishi, Masaki; Yoda, Ikushi

In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

On the problem of zinc extraction from the slags of lead heat

NASA Astrophysics Data System (ADS)

Kozyrev, V. V.; Besser, A. D.; Paretskii, V. M.

2013-12-01

The possibilities of zinc extraction from the slags of lead heat are studied as applied to the ZAO Karat-TsM lead plant to be built for processing ore lead concentrates. The process of zinc extraction into commercial fumes using the technology of slag fuming by natural gas developed in Gintsvetmet is recommended for this purpose. Technological rules are developed for designing a commercial fuming plant, as applied to the conditions of the ZAO Karat-TsM plant.

Fuzzy connectedness and object definition

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Samarasekera, Supun

1995-04-01

Approaches to object information extraction from images should attempt to use the fact that images are fuzzy. In past image segmentation research, the notion of `hanging togetherness' of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n-dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples.

The 3-D image recognition based on fuzzy neural network technology

NASA Technical Reports Server (NTRS)

Hirota, Kaoru; Yamauchi, Kenichi; Murakami, Jun; Tanaka, Kei

1993-01-01

Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented.

Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

NASA Astrophysics Data System (ADS)

Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi

2014-01-01

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.

Propylene epoxidation over biogenic Au/TS-1 catalysts by Cinnamomum camphora extract in the presence of H2 and O2

NASA Astrophysics Data System (ADS)

Du, Mingming; Huang, Jiale; Sun, Daohua; Li, Qingbiao

2016-03-01

The Au/TS-1 catalysts with different Au nanoparticles (NPs) sizes ranging from 3.1 to 8.4 nm but the same Au loading of 0.5 wt% were prepared by Cinnamomum camphora (CC) extract, and were used for propylene epoxidation. The results showed that the interaction between Au and TS-1 support surface is important for propylene epoxidation and much smaller Au NPs (<3 nm) are the dominant active sites. After reaction of 100 h, there is no decreasing in both the activity and the PO selectivity for the Au/TS-1 catalysts, and only 1.8 wt% of the carbonaceous deposits on the surface of the catalyst after reaction, suggesting that the desorption of the product from the modified catalysts surface by residual biomolecules is much easier.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

NASA Astrophysics Data System (ADS)

Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

2010-10-01

The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

Disturbance observer based Takagi-Sugeno fuzzy control for an active seat suspension

NASA Astrophysics Data System (ADS)

Ning, Donghong; Sun, Shuaishuai; Zhang, Fei; Du, Haiping; Li, Weihua; Zhang, Bangji

2017-09-01

In this paper, a disturbance observer based Takagi-Sugeno (TS) fuzzy controller is proposed for an active seat suspension; both simulations and experiments have been performed verifying the performance enhancement and stability of the proposed controller. The controller incorporates closed-loop feedback control using the measured acceleration of the seat and deflection of the suspension; these two variables can be easily measured in practical applications, thus allowing the proposed controller to be robust and adaptable. A disturbance observer that can estimate the disturbance caused by friction, model simplification, and controller output error has also been used to compensate a H∞ state feedback controller. The TS fuzzy control method is applied to enhance the controller's performance by considering the variation of driver's weight during operation. The vibration of a heavy duty vehicle seat is largest in the frequency range between 2 Hz and 4 Hz, in the vertical direction; therefore, it is reasonable to focus on controlling low frequency vibration amplitudes and maintain the seat suspensions passivity at high frequency. Moreover, both the simulation and experimental results show that the active seat suspension with the proposed controller can effectively isolate unwanted vibration amplitudes below 4.5 Hz, when compared with a well-tuned passive seat suspension. The active controller has been further validated under bump and random road tests with both a 55 kg and a 70 kg loads. The bump road test demonstrated the controller has good transient response capabilities. The random road test result has been presented both in the time domain and the frequency domain. When with the above two loads, the controlled seat suspensions root-mean-square (RMS) accelerations were reduced by 45.5% and 49.5%, respectively, compared with a well-tuned passive seat suspension. The proposed active seat suspension controller has great potential and is very practical for application as it can significantly improve heavy duty driver's ride comfort.

Analysing the strength of friction stir welded dissimilar aluminium alloys using Sugeno Fuzzy model

NASA Astrophysics Data System (ADS)

Barath, V. R.; Vaira Vignesh, R.; Padmanaban, R.

2018-02-01

Friction stir welding (FSW) is a promising solid state joining technique for aluminium alloys. In this study, FSW trials were conducted on two dissimilar plates of aluminium alloy AA2024 and AA7075 by varying the tool rotation speed (TRS) and welding speed (WS). Tensile strength (TS) of the joints were measured and a Sugeno - Fuzzy model was developed to interconnect the FSW process parameters with the tensile strength. From the developed model, it was observed that the optimum heat generation at WS of 15 mm.min-1 and TRS of 1050 rpm resulted in dynamic recovery and dynamic recrystallization of the material. This refined the grains in the FSW zone and resulted in peak tensile strength among the tested specimens. Crest parabolic trend was observed in tensile strength with variation of TRS from 900 rpm to 1200 rpm and TTS from 10 mm.min-1 to 20 mm.min-1.

Obtaining ABET Student Outcome Satisfaction from Course Learning Outcome Data Using Fuzzy Logic

ERIC Educational Resources Information Center

Imam, Muhammad Hasan; Tasadduq, Imran Ali; Ahmad, Abdul-Rahim; Aldosari, Fahd

2017-01-01

One of the approaches for obtaining the satisfaction data for ABET "Student Outcomes" (SOs) is to transform Course Learning Outcomes (CLOs) satisfaction data obtained through assessment of CLOs to SO satisfaction data. Considering the fuzzy nature of metrics of CLOs and SOs, a Fuzzy Logic algorithm has been proposed to extract SO…

Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

PubMed

Wang, B H; Lim, J W; Lim, J S

2016-08-30

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

PubMed

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

Formulation of intumescent flame retardant coatings containing natural-based tea saponin.

PubMed

Qian, Wei; Li, Xiang-Zhou; Wu, Zhi-Ping; Liu, Yan-Xin; Fang, Cong-Cong; Meng, Wei

2015-03-18

Natural product tea saponin (TS), extracted from the nutshell of camellia (Camellia oleifera Abel, Theaceae), was introduced into intumescent flame retardant formulations as blowing agent and carbon source. The formulations of the flame retardant system were optimized to get the optimum proportion of TS, and intumescent flame retardant coatings containing tea saponin (TS-IFRCs) were then prepared. It was found that TS can significantly affect the combustion behavior and the thermal stability of TS-IFRCs evaluated by cone calorimetry and simultaneous thermal analyzer, respectively. It was shown that TS, degraded to water vapor and carbon at high temperatures, can combine with other components to form a well-developed char layer. The char layer was supposed to inhibit erosion upon exposure to heat and oxygen and enhance the flame retardancy of TS-IFRCs. In addition, the smoke release of TS-IFRCs was also studied, which provided a low amount of smoke production.

Neuroprotective effects of tanshinone I from Danshen extract in a mouse model of hypoxia-ischemia

PubMed Central

Lee, Jae-Chul; Park, Joon Ha; Park, Ok Kyu; Kim, In Hye; Yan, Bing Chun; Ahn, Ji Hyeon; Kwon, Seung-Hae; Choi, Jung Hoon

2013-01-01

Hypoxia-ischemia leads to serious neuronal damage in some brain regions and is a strong risk factor for stroke. The aim of this study was to investigate the neuroprotective effect of tanshinone I (TsI) derived from Danshen (Radix Salvia miltiorrhiza root extract) against neuronal damage using a mouse model of cerebral hypoxia-ischemia. Brain infarction and neuronal damage were examined using 2,3,5-triphenyltetrazolium chloride (TTC) staining, hematoxylin and eosin histochemistry, and Fluoro-Jade B histofluorescence. Pre-treatment with TsI (10 mg/kg) was associated with a significant reduction in infarct volume 1 day after hypoxia-ischemia was induced. In addition, TsI protected against hypoxia-ischemia-induced neuronal death in the ipsilateral region. Our present findings suggest that TsI has strong potential for neuroprotection against hypoxic-ischemic damage. These results may be used in research into new anti-stroke medications. PMID:24179693

Genetic analysis of an Escherichia coli syndrome.

PubMed

Lennette, E T; Apirion, D

1971-12-01

A mutant strain of Escherichia coli that fails to recover from prolonged (72 hr) starvation also fails to grow at 43 C. Extracts of this mutant strain show an increased ribonuclease II activity as compared to extracts of the parental strain, and stable ribonucleic acid is degraded to a larger extent in this strain during starvation. Ts(+) transductants and revertants were tested for all the above-mentioned phenotypes. All the Ts(+) transductants and revertants tested behaved like the Ts(+) parental strain, which suggests that all the observed phenotypes are caused by a single sts (starvation-temperature sensitivity) mutation. The reversion rate from sts(-) to sts(+) is rather low but is within the range of reversion rates for other single-site mutations. Three-point transduction crosses located this sts mutation between the ilv and rbs genes. The properties of sts(+)/sts(-) merozygotes suggested that the Ts(-) phenotype of this mutation is recessive.

Evaluation of Biological Activity of Mastic Extracts Based on Chemotherapeutic Indices

PubMed Central

SUZUKI, RYUICHIRO; SAKAGAMI, HIROSHI; AMANO, SHIGERU; FUKUCHI, KUNIHIKO; SUNAGA, KATSUYOSHI; KANAMOTO, TAISEI; TERAKUBO, SHIGEMI; NAKASHIMA, HIDEKI; SHIRATAKI, YOSHIAKI; TOMOMURA, MINEKO; MASUDA, YOSHIKO; YOKOSE, SATOSHI; TOMOMURA, AKITO; WATANABE, HIROFUMI; OKAWARA, MASAKI; MATAHIRA, YOSHIHARU

2017-01-01

Background: Most previous mastic investigators have not considered its potent cytotoxicity that may significantly affect the interpretation of obtained data. In the present study, we re-evaluated several biological activities of mastic extracts, based on chemotherapeutic indexes. Materials and Methods: Pulverized mastic gum was extracted with n-hexane and then with ethyl acetate or independently with methanol or n-butanol. Tumor specificity (TS) of the extracts was determined by their cytotoxicity against human malignant and non-malignant cells. Antibacterial activity was determined by their cytotoxicity against bacteria and normal oral cells. Antiviral activity was determined by their protection of viral infection and cytotoxic activity. Cytochrome P-450 (CYP) 3A4 activity was measured by β-hydroxylation of testosterone. Results: Ethyl acetate extract showed slightly higher tumor specificity (TS=2.6) and one order higher antibacterial activity (selectivity index (SI)=0.813) than other extracts (TS=1.4-2.5; SI=0.030-0.063). All extracts showed no anti-human immunodeficiency virus (HIV) activity, but some anti-herpes simplex virus (HSV) activity, which was masked by potent cytotoxicity. They showed strong inhibitory activity against CYP3A4. Conclusion: Ethyl acetate extraction following the removal of cytotoxic and CYP3A4 inhibitory substances by n-hexane can enhance antitumor and antibacterial activity of mastic. PMID:28652425

Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)

NASA Technical Reports Server (NTRS)

Sarmadi, Hengameth

2004-01-01

This work aims to introduce a new concept for incorporating fuzzy sets in hybrid deliberative/reactive paradigm. After a brief review on basic issues of hybrid paradigm the definition of agent-based fuzzy hybrid paradigm, which enables the agents to proceed and extract their behavior through quantitative numerical and qualitative knowledge and to impose their decision making procedure via fuzzy rule bank, is discussed. Next an example performs a more applied platform for the developed approach and finally an overview of the corresponding agents architecture enhances agents logical framework.

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.

PubMed

Lim, Jongwoo; Wang, Bohyun; Lim, Joon S

2016-04-29

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling

NASA Astrophysics Data System (ADS)

Foroutan, E.; Delavar, M. R.; Araabi, B. N.

2012-07-01

Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.

Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy.

PubMed

Sil Kar, Sudeshna; Maity, Santi P

2016-09-01

Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy images with poorly illuminated background is a complicated task. To this aim, an integrated system design platform is suggested in this work for vessel extraction using a sequential bandpass filter followed by fuzzy conditional entropy maximization on matched filter response. At first noise is eliminated from the image under consideration through curvelet based denoising. To include the fine details and the relatively less thick vessel structures, the image is passed through a bank of sequential bandpass filter structure optimized for contrast enhancement. Fuzzy conditional entropy on matched filter response is then maximized to find the set of multiple optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to determine the optimal gain in bandpass filter and the combination of the fuzzy parameters. Using the multiple thresholds, retinal image is classified as the thick, the medium and the thin vessels including neovascularization. Performance evaluated on different publicly available retinal image databases shows that the proposed method is very efficient in identifying the diverse types of vessels. Proposed method is also efficient in extracting the abnormal and the thin blood vessels in pathological retinal images. The average values of true positive rate, false positive rate and accuracy offered by the method is 76.32%, 1.99% and 96.28%, respectively for the DRIVE database and 72.82%, 2.6% and 96.16%, respectively for the STARE database. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the various types of vessels and the neovascularization structures. The combination of curvelet transform and tunable bandpass filter is found to be very much effective in edge enhancement whereas fuzzy conditional entropy efficiently distinguishes vessels of different widths. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

PubMed

Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

2015-07-01

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Pretreatment Hepatoprotective Effect of the Marine Fungus Derived from Sponge on Hepatic Toxicity Induced by Heavy Metals in Rats

PubMed Central

Abdel-Monem, Nehad M.; Abdel-Azeem, Ahmed M.; El-Ashry, El-Sayed H.; Ghareeb, Doaa A.; Nabil-adam, Asmaa

2013-01-01

The aim of this study was to evaluate the pretreatment hepatoprotective effect of the extract of marine-derived fungus Trichurus spiralis Hasselbr (TS) isolated from Hippospongia communis sponge on hepatotoxicity. Twenty-eight male Sprague-Dawley rats were divided into four groups (n = 7). Group I served as −ve control, group II served as the induced group receiving subcutaneously for seven days 0.25 mg heavy metal mixtures, group III received (i.p.) TS extract of dose 40 mg for seven days, and group IV served as the protected group pretreated with TS extract for seven days as a protection dose, and then treated with the heavy metal-mixture. The main pathological changes within the liver after heavy-metal mixtures administrations marked hepatic damage evidenced by foci of lobular necrosis with neutrophilic infiltration, adjacent to dysplastic hepatocytes. ALT and AST measurements show a significant increase in group II by 46.20% and 45.12%, respectively. Total protein, elevated by about 38.9% in induction group compared to the −ve control group, in contrast to albumin, decreased as a consequence of metal administration with significant elevation on bilirubin level. The results prove that TS extract possesses a hepatoprotective property due to its proven antioxidant and free-radical scavenging properties. PMID:23484129

Evaluation of Biological Activity of Mastic Extracts Based on Chemotherapeutic Indices.

PubMed

Suzuki, Ryuichiro; Sakagami, Hiroshi; Amano, Shigeru; Fukuchi, Kunihiko; Sunaga, Katsuyoshi; Kanamoto, Taisei; Terakubo, Shigemi; Nakashima, Hideki; Shirataki, Yoshiaki; Tomomura, Mineko; Masuda, Yoshiko; Yokose, Satoshi; Tomomura, Akito; Watanabe, Hirofumi; Okawara, Masaki; Matahira, Yoshiharu

2017-01-01

Most previous mastic investigators have not considered its potent cytotoxicity that may significantly affect the interpretation of obtained data. In the present study, we re-evaluated several biological activities of mastic extracts, based on chemotherapeutic indexes. Pulverized mastic gum was extracted with n-hexane and then with ethyl acetate or independently with methanol or n-butanol. Tumor specificity (TS) of the extracts was determined by their cytotoxicity against human malignant and non-malignant cells. Antibacterial activity was determined by their cytotoxicity against bacteria and normal oral cells. Antiviral activity was determined by their protection of viral infection and cytotoxic activity. Cytochrome P-450 (CYP) 3A4 activity was measured by β-hydroxylation of testosterone. Ethyl acetate extract showed slightly higher tumor specificity (TS=2.6) and one order higher antibacterial activity (selectivity index (SI)=0.813) than other extracts (TS=1.4-2.5; SI=0.030-0.063). All extracts showed no anti-human immunodeficiency virus (HIV) activity, but some anti-herpes simplex virus (HSV) activity, which was masked by potent cytotoxicity. They showed strong inhibitory activity against CYP3A4. Ethyl acetate extraction following the removal of cytotoxic and CYP3A4 inhibitory substances by n-hexane can enhance antitumor and antibacterial activity of mastic. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Altered synaptic marker abundance in the hippocampal stratum oriens of Ts65Dn mice is associated with exuberant expression of versican

PubMed Central

Howell, Matthew D; Gottschall, Paul E

2012-01-01

DS (Down syndrome), resulting from trisomy of chromosome 21, is the most common cause of genetic mental retardation; however, the molecular mechanisms underlying the cognitive deficits are poorly understood. Growing data indicate that changes in abundance or type of CSPGs (chondroitin sulfate proteoglycans) in the ECM (extracellular matrix) can influence synaptic structure and plasticity. The purpose of this study was to identify changes in synaptic structure in the hippocampus in a model of DS, the Ts65Dn mouse, and to determine the relationship to proteoglycan abundance and/or cleavage and cognitive disability. We measured synaptic proteins by ELISA and changes in lectican expression and processing in the hippocampus of young and old Ts65Dn mice and LMCs (littermate controls). In young (5 months old) Ts65Dn hippocampal extracts, we found a significant increase in the postsynaptic protein PSD-95 (postsynaptic density 95) compared with LMCs. In aged (20 months old) Ts65Dn hippocampus, this increase was localized to hippocampal stratum oriens extracts compared with LMCs. Aged Ts65Dn mice exhibited impaired hippocampal-dependent spatial learning and memory in the RAWM (radial-arm water maze) and a marked increase in levels of the lectican versican V2 in stratum oriens that correlated with the number of errors made in the final RAWM block. Ts65Dn stratum oriens PNNs (perineuronal nets), an extension of the ECM enveloping mostly inhibitory interneurons, were dispersed over a larger area compared with LMC mice. Taken together, these data suggest a possible association with alterations in the ECM and inhibitory neurotransmission in the Ts65Dn hippocampus which could contribute to cognitive deficits. PMID:22225533

Molecular Cloning of a cDNA Encoding for Taenia solium TATA-Box Binding Protein 1 (TsTBP1) and Study of Its Interactions with the TATA-Box of Actin 5 and Typical 2-Cys Peroxiredoxin Genes.

PubMed

Rodríguez-Lima, Oscar; García-Gutierrez, Ponciano; Jiménez, Lucía; Zarain-Herzberg, Ángel; Lazzarini, Roberto; Landa, Abraham

2015-01-01

TATA-box binding protein (TBP) is an essential regulatory transcription factor for the TATA-box and TATA-box-less gene promoters. We report the cloning and characterization of a full-length cDNA that encodes a Taenia solium TATA-box binding protein 1 (TsTBP1). Deduced amino acid composition from its nucleotide sequence revealed that encodes a protein of 238 residues with a predicted molecular weight of 26.7 kDa, and a theoretical pI of 10.6. The NH2-terminal domain shows no conservation when compared with to pig and human TBP1s. However, it shows high conservation in size and amino acid identity with taeniids TBP1s. In contrast, the TsTBP1 COOH-terminal domain is highly conserved among organisms, and contains the amino acids involved in interactions with the TATA-box, as well as with TFIIA and TFIIB. In silico TsTBP1 modeling reveals that the COOH-terminal domain forms the classical saddle structure of the TBP family, with one α-helix at the end, not present in pig and human. Native TsTBP1 was detected in T. solium cysticerci´s nuclear extract by western blot using rabbit antibodies generated against two synthetic peptides located in the NH2 and COOH-terminal domains of TsTBP1. These antibodies, through immunofluorescence technique, identified the TBP1 in the nucleus of cells that form the bladder wall of cysticerci of Taenia crassiceps, an organism close related to T. solium. Electrophoretic mobility shift assays using nuclear extracts from T. solium cysticerci and antibodies against the NH2-terminal domain of TsTBP1 showed the interaction of native TsTBP1 with the TATA-box present in T. solium actin 5 (pAT5) and 2-Cys peroxiredoxin (Ts2-CysPrx) gene promoters; in contrast, when antibodies against the anti-COOH-terminal domain of TsTBP1 were used, they inhibited the binding of TsTBP1 to the TATA-box of the pAT5 promoter gene.

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.

PubMed

Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

2012-08-01

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

Fuzzy geometry, entropy, and image information

NASA Technical Reports Server (NTRS)

Pal, Sankar K.

1991-01-01

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

Image Edge Extraction via Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A. (Inventor); Klinko, Steve (Inventor)

2008-01-01

A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

PubMed

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

PI and fuzzy logic controllers for shunt Active Power Filter--a report.

PubMed

P, Karuppanan; Mahapatra, Kamala Kanta

2012-01-01

This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

Escherichia coli mutants thermosensitive for deoxyribonucleic acid gyrase subunit A: effects on deoxyribonucleic acid replication, transcription, and bacteriophage growth.

PubMed

Kreuzer, K N; Cozzarelli, N R

1979-11-01

Temperature-sensitive nalA mutants of Escherichia coli have been used to investigate the structure and functions of deoxyribonucleic acid (DNA) gyrase. Extracts of one such mutant (nalA43) had thermosensitive DNA gyrase subunit A activity but normal gyrase subunit B activity, proving definitively that nalA is the structural gene for subunit A. Extracts of a second nalA (Ts) mutant (nalA45) had a 50-fold deficiency of gyrase subunit A activity. The residual DNA supertwisting was catalyzed by the mutant DNA gyrase rather than by a novel supertwisting enzyme. The nalA45(Ts) extract was also deficient in the nalidixic acid target, which is defined as the protein necessary to confer drug sensitivity to in vitro DNA replication directed by a nalidixic acid-resistant mutant extract. Thus, gyrase subunit A and the nalidixic acid target are one and the same protein, the nalA gene product. Shift of the nalA43(Ts) mutant to a nonpermissive temperature resulted in a precipitous decline in the rate of [(3)H]thymidine incorporation, demonstrating an obligatory role of the nalA gene product in DNA replication. The rates of incorporation of [(3)H]uridine pulses and continuously administered [(3)H]uracil were quickly reduced approximately twofold upon temperature shift of the nalA43(Ts) mutant, and therefore some but not all transcription requires the nalA gene product. The thermosensitive growth of bacteriophages phiX174 and T4 in the nalA43(Ts) host shows that these phages depend on the host nalA gene product. In contrast, the growth of phage T7 was strongly inhibited by nalidixic acid but essentially unaffected by the nalA43(Ts) mutation. The inhibition of T7 growth by nalidixic acid was, however, eliminated by temperature inactivation of the nal43 gene product. Therefore, nalidixic acid may block T7 growth by a corruption rather than a simple elimination of the nalidixic acid target. Possible mechanisms for such a corruption are considered, and their relevance to the puzzling dominance of drug sensitivity is discussed.

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

PubMed Central

Bai, Xiangzhi

2015-01-01

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion. PMID:26184229

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators.

PubMed

Bai, Xiangzhi

2015-07-15

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

Effect of Microalgal Extracts of Tetraselmis suecica against UVB-Induced Photoaging in Human Skin Fibroblasts.

PubMed

Jo, Wol Soon; Yang, Kwang Mo; Park, Hee Sung; Kim, Gi Yong; Nam, Byung Hyouk; Jeong, Min Ho; Choi, Yoo Jin

2012-12-01

Exposure of cells to ultraviolet B (UVB) radiation can induce production of free radicals and reactive oxygen species (ROS), which damage cellular components. In addition, these agents can stimulate the expression of matrix metalloproteinase (MMP) and decrease collagen synthesis in human skin cells. In this study, we examined the anti-photoaging effects of extracts of Tetraselmis suecica (W-TS). W-TS showed the strongest scavenging activity against 2,2-difenyl-1-picrylhydrazyl (DPPH) and peroxyl radicals, followed by superoxide anions from the xanthine/xanthine oxidase system. We observed that the levels of both intracellular ROS and lipid peroxidation significantly increased in UVB-irradiated human skin fibroblast cells. Furthermore, the activities of enzymatic antioxidants (e.g., superoxide dismutase) and the levels of non-enzymatic antioxidants (e.g., glutathione) significantly decreased in cells. However, W-TS pretreatment, at the maximum tested concentration, significantly decreased intracellular ROS and malondialdehyde (MDA) levels, and increased superoxide dismutase and glutathione levels in the cells. At this same concentration, W-TS did not show cytotoxicity. Type 1 procollagen and MMP-1 released were quantified using RT-PCR techniques. The results showed that W-TS protected type 1 procollagen against UVBinduced depletion in fibroblast cells in a dose-dependent manner via inhibition of UVB-induced MMP-1. Taken together, the results of the study suggest that W-TS effectively inhibits UVB-induced photoaging in skin fibroblasts by its strong anti-oxidant ability.

Effect of Microalgal Extracts of Tetraselmis suecica against UVB-Induced Photoaging in Human Skin Fibroblasts

PubMed Central

Jo, Wol Soon; Yang, Kwang Mo; Park, Hee Sung; Kim, Gi Yong; Nam, Byung Hyouk

2012-01-01

Exposure of cells to ultraviolet B (UVB) radiation can induce production of free radicals and reactive oxygen species (ROS), which damage cellular components. In addition, these agents can stimulate the expression of matrix metalloproteinase (MMP) and decrease collagen synthesis in human skin cells. In this study, we examined the anti-photoaging effects of extracts of Tetraselmis suecica (W-TS). W-TS showed the strongest scavenging activity against 2,2-difenyl-1-picrylhydrazyl (DPPH) and peroxyl radicals, followed by superoxide anions from the xanthine/xanthine oxidase system. We observed that the levels of both intracellular ROS and lipid peroxidation significantly increased in UVB-irradiated human skin fibroblast cells. Furthermore, the activities of enzymatic antioxidants (e.g., superoxide dismutase) and the levels of non-enzymatic antioxidants (e.g., glutathione) significantly decreased in cells. However, W-TS pretreatment, at the maximum tested concentration, significantly decreased intracellular ROS and malondialdehyde (MDA) levels, and increased superoxide dismutase and glutathione levels in the cells. At this same concentration, W-TS did not show cytotoxicity. Type 1 procollagen and MMP-1 released were quantified using RT-PCR techniques. The results showed that W-TS protected type 1 procollagen against UVBinduced depletion in fibroblast cells in a dose-dependent manner via inhibition of UVB-induced MMP-1. Taken together, the results of the study suggest that W-TS effectively inhibits UVB-induced photoaging in skin fibroblasts by its strong anti-oxidant ability. PMID:24278616

Design of fuzzy systems using neurofuzzy networks.

PubMed

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

PubMed

Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

Estimation of tool wear length in finish milling using a fuzzy inference algorithm

NASA Astrophysics Data System (ADS)

Ko, Tae Jo; Cho, Dong Woo

1993-10-01

The geometric accuracy and surface roughness are mainly affected by the flank wear at the minor cutting edge in finish machining. A fuzzy estimator obtained by a fuzzy inference algorithm with a max-min composition rule to evaluate the minor flank wear length in finish milling is introduced. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration of the spindle housing. Linguistic rules for fuzzy estimation are constructed using these features, and then fuzzy inferences are carried out with test data sets under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length, and its mean error is less than 12%.

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

NASA Astrophysics Data System (ADS)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

Molecular Cloning of a cDNA Encoding for Taenia solium TATA-Box Binding Protein 1 (TsTBP1) and Study of Its Interactions with the TATA-Box of Actin 5 and Typical 2-Cys Peroxiredoxin Genes

PubMed Central

Rodríguez-Lima, Oscar; García-Gutierrez, Ponciano; Jiménez, Lucía; Zarain-Herzberg, Ángel; Lazzarini, Roberto; Landa, Abraham

2015-01-01

TATA-box binding protein (TBP) is an essential regulatory transcription factor for the TATA-box and TATA-box-less gene promoters. We report the cloning and characterization of a full-length cDNA that encodes a Taenia solium TATA-box binding protein 1 (TsTBP1). Deduced amino acid composition from its nucleotide sequence revealed that encodes a protein of 238 residues with a predicted molecular weight of 26.7 kDa, and a theoretical pI of 10.6. The NH2-terminal domain shows no conservation when compared with to pig and human TBP1s. However, it shows high conservation in size and amino acid identity with taeniids TBP1s. In contrast, the TsTBP1 COOH-terminal domain is highly conserved among organisms, and contains the amino acids involved in interactions with the TATA-box, as well as with TFIIA and TFIIB. In silico TsTBP1 modeling reveals that the COOH-terminal domain forms the classical saddle structure of the TBP family, with one α-helix at the end, not present in pig and human. Native TsTBP1 was detected in T. solium cysticerci´s nuclear extract by western blot using rabbit antibodies generated against two synthetic peptides located in the NH2 and COOH-terminal domains of TsTBP1. These antibodies, through immunofluorescence technique, identified the TBP1 in the nucleus of cells that form the bladder wall of cysticerci of Taenia crassiceps, an organism close related to T. solium. Electrophoretic mobility shift assays using nuclear extracts from T. solium cysticerci and antibodies against the NH2-terminal domain of TsTBP1 showed the interaction of native TsTBP1 with the TATA-box present in T. solium actin 5 (pAT5) and 2-Cys peroxiredoxin (Ts2-CysPrx) gene promoters; in contrast, when antibodies against the anti-COOH-terminal domain of TsTBP1 were used, they inhibited the binding of TsTBP1 to the TATA-box of the pAT5 promoter gene. PMID:26529408

Analysis of atomic force microscopy data for surface characterization using fuzzy logic

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Mousa, Amjed, E-mail: aalmousa@vt.edu; Niemann, Darrell L.; Niemann, Devin J.

2011-07-15

In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional searchmore » technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundaries compared to other methods.« less

Fuzzy recognition of noncompact musical objects

NASA Astrophysics Data System (ADS)

Cristobal Salas, Alfredo; Tchernykh, Andrei

1997-03-01

This article describes and compares some techniques to extract attributes from black and white images which contain musical objects. The inertia moment, the central moments and the wavelet transform methods are used to describe the images. Two supervised neural networks are applied to classify the images: backpropagation and fuzzy backpropagation. The results are compared.

Toona sinensis Inhibits Murine Leukemia WEHI-3 Cells and Promotes Immune Response In Vivo.

PubMed

Yang, Hsin-Ling; Thiyagarajan, Varadharajan; Liao, Jiunn-Wang; Chu, Yu-Lin; Chang, Chia-Ting; Huang, Pei-Jane; Hsu, Chih-Jung; Hseu, You-Cheng

2017-09-01

Toona sinensis (TS) is one of the most popular vegetarian dishes in Taiwan. It has been shown to exhibit antioxidant, antiangiogenic, antiatherosclerotic, and anticancer properties. In this study, we demonstrated the ability of aqueous leaf extracts from TS to promote immune responses in BALB/c mice and to exhibit anti-leukemia activity in murine WEHI-3 cells. BALB/c mice were injected intravenously with WEHI-3 cells and then treated orally with TS (50 mg/kg). In vivo study showed that TS treatment reduced liver and spleen enlargement in WEHI-3 bearing mice compared with the untreated group. Furthermore, TS also decreased white blood cells (WBC), indicating inhibition of differentiation of the precursor of macrophages in WEHI-3 bearing mice. Treatment of WEHI-3 cells with TS (0-75 μg/mL for 24 hours) significantly reduced cell viability. Furthermore, TS treatment-induced late apoptosis was confirmed by Annexin-V/PI staining. Western blot analyses revealed that treatment of WEHI-3 cells with TS statistically increased the protein expression level of cytochrome c in the cytoplasm and activates caspase-3. Notably, TS treatment caused a dramatic reduction in Bcl-2 and increase in Bax protein levels. TS may disturb the Bcl-2 and Bax protein ratio and induce apoptosis. This reports confirms the antitumor activity of this nutritious vegetable potentially against leukemia.

Toona sinensis Inhibits Murine Leukemia WEHI-3 Cells and Promotes Immune Response In Vivo

PubMed Central

Yang, Hsin-Ling; Thiyagarajan, Varadharajan; Liao, Jiunn-Wang; Chu, Yu-Lin; Chang, Chia-Ting; Huang, Pei-Jane; Hsu, Chih-Jung; Hseu, You-Cheng

2016-01-01

Toona sinensis (TS) is one of the most popular vegetarian dishes in Taiwan. It has been shown to exhibit antioxidant, antiangiogenic, antiatherosclerotic, and anticancer properties. In this study, we demonstrated the ability of aqueous leaf extracts from TS to promote immune responses in BALB/c mice and to exhibit anti-leukemia activity in murine WEHI-3 cells. BALB/c mice were injected intravenously with WEHI-3 cells and then treated orally with TS (50 mg/kg). In vivo study showed that TS treatment reduced liver and spleen enlargement in WEHI-3 bearing mice compared with the untreated group. Furthermore, TS also decreased white blood cells (WBC), indicating inhibition of differentiation of the precursor of macrophages in WEHI-3 bearing mice. Treatment of WEHI-3 cells with TS (0-75 μg/mL for 24 hours) significantly reduced cell viability. Furthermore, TS treatment–induced late apoptosis was confirmed by Annexin-V/PI staining. Western blot analyses revealed that treatment of WEHI-3 cells with TS statistically increased the protein expression level of cytochrome c in the cytoplasm and activates caspase-3. Notably, TS treatment caused a dramatic reduction in Bcl-2 and increase in Bax protein levels. TS may disturb the Bcl-2 and Bax protein ratio and induce apoptosis. This reports confirms the antitumor activity of this nutritious vegetable potentially against leukemia. PMID:27151590

Diagnostic criteria, clinical features, and incidence of thyroid storm based on nationwide surveys.

PubMed

Akamizu, Takashi; Satoh, Tetsurou; Isozaki, Osamu; Suzuki, Atsushi; Wakino, Shu; Iburi, Tadao; Tsuboi, Kumiko; Monden, Tsuyoshi; Kouki, Tsuyoshi; Otani, Hajime; Teramukai, Satoshi; Uehara, Ritei; Nakamura, Yosikazu; Nagai, Masaki; Mori, Masatomo

2012-07-01

Thyroid storm (TS) is life threatening. Its incidence is poorly defined, few series are available, and population-based diagnostic criteria have not been established. We surveyed TS in Japan, defined its characteristics, and formulated diagnostic criteria, FINAL-CRITERIA1 and FINAL-CRITERIA2, for two grades of TS, TS1, and TS2 respectively. We first developed diagnostic criteria based on 99 patients in the literature and 7 of our patients (LIT-CRITERIA1 for TS1 and LIT-CRITERIA2 for TS2). Thyrotoxicosis was a prerequisite for TS1 and TS2 as well as for combinations of the central nervous system manifestations, fever, tachycardia, congestive heart failure (CHF), and gastrointestinal (GI)/hepatic disturbances. We then conducted initial and follow-up surveys from 2004 through 2008, targeting all hospitals in Japan, with an eight-layered random extraction selection process to obtain and verify information on patients who met LIT-CRITERIA1 and LIT-CRITERIA2. We identified 282 patients with TS1 and 74 patients with TS2. Based on these data and information from the Ministry of Health, Labor, and Welfare of Japan, we estimated the incidence of TS in hospitalized patients in Japan to be 0.20 per 100,000 per year. Serum-free thyroxine and free triiodothyroine concentrations were similar among patients with TS in the literature, Japanese patients with TS1 or TS2, and a group of patients with thyrotoxicosis without TS (Tox-NoTS). The mortality rate was 11.0% in TS1, 9.5% in TS2, and 0% in Tox-NoTS patients. Multiple organ failure was the most common cause of death in TS1 and TS2, followed by CHF, respiratory failure, arrhythmia, disseminated intravascular coagulation, GI perforation, hypoxic brain syndrome, and sepsis. Glasgow Coma Scale results and blood urea nitrogen (BUN) were associated with irreversible damages in 22 survivors. The only change in our final diagnostic criteria for TS as compared with our initial criteria related to serum bilirubin concentration >3 mg/dL. TS is still a life-threatening disorder with more than 10% mortality in Japan. We present newly formulated diagnostic criteria for TS and clarify its clinical features, prognosis, and incidence based on nationwide surveys in Japan. This information will help diagnose TS and in understanding the factors contributing to mortality and irreversible complications.

Robust traffic sign detection using fuzzy shape recognizer

NASA Astrophysics Data System (ADS)

Li, Lunbo; Li, Jun; Sun, Jianhong

2009-10-01

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road images were collected under different weather conditions by a digital camera, and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

NASA Technical Reports Server (NTRS)

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Bioconversion of glycerol to 1,3-propanediol in thin stillage-based media by engineered Lactobacillus panis PM1.

PubMed

Kang, Tae Sun; Korber, Darren R; Tanaka, Takuji

2014-04-01

Thin stillage (TS) is a waste residue that remains after bioethanol production, and its disposal reflects the high costs of bioethanol production. Thus, the development of cost-effective ways to process TS is a pending issue in bioethanol plants. The aim of this study was to evaluate the utilization of TS for the production of the valuable chemical, 1,3-propanediol (1,3-PDO), by Lactobacillus panis PM1. Different fermentation parameters, including temperature, pH and strains [wild-type and a recombinant strain expressing a NADPH-dependent aldehyde reductase (YqhD) gene] were tested in batch and fed-batch cultivations. The highest 1,3-PDO concentration (12.85 g/L) and yield (0.84 g/g) were achieved by batch fermentation at pH-4.5/30 °C by the YqhD recombinant strain. Furthermore, pH-controlled batch fermentation reduced the total fermentation period, resulting in the maximal 1,3-PDO concentration of 16.23 g/L and yield of 0.72 g/g in TS without an expensive nutrient or nitrogen (e.g., yeast extract, beef extract, and peptone) supplementation. The addition of two trace elements, Mg(2+) and Mn(2+), in TS increased 1,3-PDO yield (0.74 g/g) without 3-hydroxypropionaldehyde production, the only intermediate of 1,3-PDO biosynthetic pathway in L. panis PM1. Our results suggest that L. panis PM1 can offer a cost-effective process that utilizes the TS to produce a value-added chemical, 1,3-PDO.

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

Image segmentation using fuzzy LVQ clustering networks

NASA Technical Reports Server (NTRS)

Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

1992-01-01

In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

Micro-matrix solid-phase dispersion coupled with MEEKC for quantitative analysis of lignans in Schisandrae Chinensis Fructus using molecular sieve TS-1 as a sorbent.

PubMed

Chu, Chu; Wei, Mengmeng; Wang, Shan; Zheng, Liqiong; He, Zheng; Cao, Jun; Yan, Jizhong

2017-09-15

A simple and effective method was developed for determining lignans in Schisandrae Chinensis Fructus by using a micro-matrix solid phase dispersion (MSPD) technique coupled with microemulsion electrokinetic chromatography (MEEKC). Molecular sieve, TS-1, was applied as a solid supporting material in micro MSPD extraction for the first time. Parameters that affect extraction efficiency, such as type of dispersant, mass ratio of the sample to the dispersant, grinding time, elution solvent and volume were optimized. The optimal extraction conditions involve dispersing 25mg of powdered Schisandrae samples with 50mg of TS-1 by a mortar and pestle. A grinding time of 150s was adopted. The blend was then transferred to a solid-phase extraction cartridge and the target analytes were eluted with 500μL of methanol. Moreover, several parameters affecting MEEKC separation were studied, including the type of oil, SDS concentration, type and concentration of cosurfactant, and concentration of organic modifier. A satisfactory linearity (R>0.9998) was obtained, and the calculated limits of quantitation were less than 2.77μg/mL. Finally, the micro MSPD-MEEKC method was successfully applied to the analysis of lignans in complex Schisandrae fructus samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Diagnostic Criteria, Clinical Features, and Incidence of Thyroid Storm Based on Nationwide Surveys

PubMed Central

Satoh, Tetsurou; Isozaki, Osamu; Suzuki, Atsushi; Wakino, Shu; Iburi, Tadao; Tsuboi, Kumiko; Monden, Tsuyoshi; Kouki, Tsuyoshi; Otani, Hajime; Teramukai, Satoshi; Uehara, Ritei; Nakamura, Yosikazu; Nagai, Masaki; Mori, Masatomo

2012-01-01

Background Thyroid storm (TS) is life threatening. Its incidence is poorly defined, few series are available, and population-based diagnostic criteria have not been established. We surveyed TS in Japan, defined its characteristics, and formulated diagnostic criteria, FINAL-CRITERIA1 and FINAL-CRITERIA2, for two grades of TS, TS1, and TS2 respectively. Methods We first developed diagnostic criteria based on 99 patients in the literature and 7 of our patients (LIT-CRITERIA1 for TS1 and LIT-CRITERIA2 for TS2). Thyrotoxicosis was a prerequisite for TS1 and TS2 as well as for combinations of the central nervous system manifestations, fever, tachycardia, congestive heart failure (CHF), and gastrointestinal (GI)/hepatic disturbances. We then conducted initial and follow-up surveys from 2004 through 2008, targeting all hospitals in Japan, with an eight-layered random extraction selection process to obtain and verify information on patients who met LIT-CRITERIA1 and LIT-CRITERIA2. Results We identified 282 patients with TS1 and 74 patients with TS2. Based on these data and information from the Ministry of Health, Labor, and Welfare of Japan, we estimated the incidence of TS in hospitalized patients in Japan to be 0.20 per 100,000 per year. Serum-free thyroxine and free triiodothyroine concentrations were similar among patients with TS in the literature, Japanese patients with TS1 or TS2, and a group of patients with thyrotoxicosis without TS (Tox-NoTS). The mortality rate was 11.0% in TS1, 9.5% in TS2, and 0% in Tox-NoTS patients. Multiple organ failure was the most common cause of death in TS1 and TS2, followed by CHF, respiratory failure, arrhythmia, disseminated intravascular coagulation, GI perforation, hypoxic brain syndrome, and sepsis. Glasgow Coma Scale results and blood urea nitrogen (BUN) were associated with irreversible damages in 22 survivors. The only change in our final diagnostic criteria for TS as compared with our initial criteria related to serum bilirubin concentration >3 mg/dL. Conclusions TS is still a life-threatening disorder with more than 10% mortality in Japan. We present newly formulated diagnostic criteria for TS and clarify its clinical features, prognosis, and incidence based on nationwide surveys in Japan. This information will help diagnose TS and in understanding the factors contributing to mortality and irreversible complications. PMID:22690898

Biofilms from Klebsiella pneumoniae: Matrix Polysaccharide Structure and Interactions with Antimicrobial Peptides.

PubMed

Benincasa, Monica; Lagatolla, Cristina; Dolzani, Lucilla; Milan, Annalisa; Pacor, Sabrina; Liut, Gianfranco; Tossi, Alessandro; Cescutti, Paola; Rizzo, Roberto

2016-08-10

Biofilm matrices of two Klebsiella pneumoniae clinical isolates, KpTs101 and KpTs113, were investigated for their polysaccharide composition and protective effects against antimicrobial peptides. Both strains were good biofilm producers, with KpTs113 forming flocs with very low adhesive properties to supports. Matrix exopolysaccharides were isolated and their monosaccharide composition and glycosidic linkage types were defined. KpTs101 polysaccharide is neutral and composed only of galactose, in both pyranose and furanose ring configurations. Conversely, KpTs113 polysaccharide is anionic due to glucuronic acid units, and also contains glucose and mannose residues. The susceptibility of the two strains to two bovine cathelicidin antimicrobial peptides, BMAP-27 and Bac7(1-35), was assessed using both planktonic cultures and biofilms. Biofilm matrices exerted a relevant protection against both antimicrobials, which act with quite different mechanisms. Similar protection was also detected when antimicrobial peptides were tested against planktonic bacteria in the presence of the polysaccharides extracted from KpTs101 and KpTs113 biofilms, suggesting sequestering adduct formation with antimicrobials. Circular dichroism experiments on BMAP-27 in the presence of increasing amounts of either polysaccharide confirmed their ability to interact with the peptide and induce an α-helical conformation.

Variable (Tg, Ts) Measurements of Alkane Dissociative Sticking Coefficients

NASA Astrophysics Data System (ADS)

Valadez, Leticia; Dewitt, Kristy; Abbott, Heather; Kolasinski, Kurt; Harrision, Ian

2006-03-01

Dissociative sticking coefficients S(Tg, Ts) for CH4 and C2H6 on Pt(111) have been measured as a function of gas temperature (Tg) and surface temperature (Ts) using an effusive molecular beam. Microcanonical unimolecular rate theory (MURT) was employed to extract transition state characteristics [e.g., E0(CH4) = 52.5±3.5 kJ/mol-1 and E0(C2H6) = 26.5±3 kJ/mol-1]. MURT allows our S(Tg, Ts) values to be directly compared to other supersonic molecular beam and thermal equilibrium sticking measurements. The S(Tg, Ts) depend strongly on Ts, however, only for CH4 is a strong Tg dependence observed. The fairly weak Tg dependence for C2H6 suggests that vibrational mode specific behavior and/or molecular rotations play stronger roles in the dissociative chemisorption of C2H6 than they do for CH4. Interestingly, thermal S(Tg=Ts) predictions based on MURT modeling of our CH4/Pt(111) data are three orders of magnitude higher than recent thermal equilibrium measurements on supported Pt nanocrystallite catalysts [J. M. Wei, E. Iglesia, J. Phys. Chem. B 108, 4094 (2004)].

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boukharouba, Abdelhak; Bennia, Abdelhak

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descentmore » learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.« less

Fuzzy Document Clustering Approach using WordNet Lexical Categories

NASA Astrophysics Data System (ADS)

Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

A survey of fuzzy logic monitoring and control utilisation in medicine.

PubMed

Mahfouf, M; Abbod, M F; Linkens, D A

2001-01-01

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for such intelligent systems driven solutions is that biological systems are so complex that the development of computerised systems within such environments is not always a straightforward exercise. In practice, a precise model may not exist for biological systems or it may be too difficult to model. In most cases fuzzy logic is considered to be an ideal tool as human minds work from approximate data, extract meaningful information and produce crisp solutions. This paper surveys the utilisation of fuzzy logic control and monitoring in medical sciences with an analysis of its possible future penetration.

Influence of training status and eNOS haplotypes on plasma nitrite concentrations in normotensive older adults: a hypothesis-generating study.

PubMed

da Silva, Roberta Fernanda; Sertório, Jonas Tadeu Cau; Lacchini, Riccardo; Trapé, Atila Alexandre; Tanus-Santos, José Eduardo; Rush, James W E; Amaral, Sandra Lia; Zago, Anderson Saranz

2014-12-01

The purpose of this study was to evaluate the relationship between 3 eNOS gene polymorphisms and training status (TS) in affecting plasma nitrite concentration (NO2) in normotensive adults over 50 years old. Resting blood pressure (BP) was measured in all participants (n = 101). Plasma was taken to analyze: lipid profile, nitrite concentration (NO2) and lipid peroxide levels (T-BARS). Also, genomic DNA was extracted from plasma for genotyping NOS3 polymorphisms (-786T>C; 894G>T; and VNTR in intron 4). TS was determined by one-mile walk test and Functional Fitness Test Battery from AAHPERD (TS1-regular TS; TS2-good TS; and TS3-very good TS). BP was not influenced by TS, but NO2 was 15% higher in TS3 (123 ± 27 nM) compared to TS-2 (106 ± 22 nM). No differences were found in plasma NO2 in the haplotype analyses. However, the presence of the C allele (T-786C) and ASP allele (Glu298Asp) was found to enhance the correlation between TS and NO2 levels (r = 0.492 in C/4b/ASP haplotype and r = 0.855 in C/4a/ASP haplotype). This study thus identifies NOS3 polymorphism-dependent sensitivity to the effects of physical training on plasma NO2. Maintenance of good levels of training status, in carriers of C allele for T-786C polymorphism, combined with ASP allele for Glu298Asp polymorphism, may result in an increase in the NO2 plasma concentrations, which may reflect improved NO bioavailability in older adult normotensive individuals.

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

PubMed

Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

2008-07-01

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Development of an Operational TS Dataset Production System for the Data Assimilation System

NASA Astrophysics Data System (ADS)

Kim, Sung Dae; Park, Hyuk Min; Kim, Young Ho; Park, Kwang Soon

2017-04-01

An operational TS (Temperature and Salinity) dataset production system was developed to provide near real-time data to the data assimilation system periodically. It collects the latest 15 days' TS data of the north western pacific area (20°N - 55°N, 110°E - 150°E), applies QC tests to the archived data and supplies them to numerical prediction models of KIOST (Korea Institute of Ocean Science and Technology). The latest real-time TS data are collected from Argo GDAC and GTSPP data server every week. Argo data are downloaded from /latest_data directory of Argo GDAC. Because many duplicated data exist when all profile data are extracted from all Argo netCDF files, DB system is used to avoid duplication. All metadata (float ID, location, observation date and time, etc) of all Argo floats is stored into Database system and a Matlab program was developed to manipulate DB data, to check the duplication and to exclude duplicated data. GTSPP data are downloaded from /realtime directory of GTSPP data service. The latest data except ARGO data are extracted from the original data. Another Matlab program was coded to inspect all collected data using 10 QC tests and produce final dataset which can be used by the assimilation system. Three regional range tests to inspect annual, seasonal and monthly variations are included in the QC procedures. The C program was developed to provide regional ranges to data managers. It can calculate upper limit and lower limit of temperature and salinity at depth from 0 to 1550m. The final TS dataset contains the latest 15 days' TS data in netCDF format. It is updated every week and transmitted to numerical modeler of KIOST for operational use.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

Effect of bacosides, alcoholic extract of Bacopa monniera Linn. (brahmi), on experimental amnesia in mice.

PubMed

Kishore, Kamal; Singh, Manjeet

2005-07-01

To investigate the effect of bacosides (alcoholic extract of brahmi) on scopolamine (3 mg kg(-1), ip), sodium nitrite (75 mg kg(-1), ip) and BN52021 (15 mg kg(-1), ip) induced experimental amnesia in mice, using Morris water maze test, all the agents were administered 30 min before the acquisition trials on each day and repeated for 4 consecutive days, and on 5th day during the retrieval trials. Bacosides on anterograde administration (before training) in mice, significantly decreased the escape latency time (ELT) during the acquisition trials for 4 consecutive days and increased the time spent (TS) in target quadrant during the retrieval trials on 5th day, and on retrograde administration (after training) bacosides were found not to affect TS significantly. Bacosides also significantly decreased the ELT and increased the TS in mice treated anterogradely with scopolamine and sodium nitrite. Bacosides did not exhibit any significant effect on TS of mice treated retrogradely with sodium nitrite. On the other hand, bacosides significantly increased the TS of mice treated retrogradely with BN52021. On the basis of the present results it can be concluded that bacosides facilitate anterograde memory and attenuate anterograde experimental amnesia induced by scopolamine and sodium nitrite possibly by improving acetylcholine level and hypoxic conditions, respectively. Beside this bacosides also reversed BN52021 induced retrograde amnesia, probably due to increase in platelet activating factor (PAF) synthesis by enhancing cerebral glutamate level.

Driving profile modeling and recognition based on soft computing approach.

PubMed

Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

2009-04-01

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

PubMed

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2015-08-01

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

NASA Astrophysics Data System (ADS)

Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran

NASA Astrophysics Data System (ADS)

Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa

2017-02-01

The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.

Three-Dimensional Road Network by Fusion of Polarimetric and Interferometric SAR Data

NASA Technical Reports Server (NTRS)

Gamba, P.; Houshmand, B.

1998-01-01

In this paper a fuzzy classification procedure is applied to polarimetric radar measurements, and street pixels are detected. These data are successively grouped into consistent roads by means of a dynamic programming approach based on the fuzzy membership function values. Further fusion of the 2D road network extracted and 3D TOPSAR measurements provides a powerful way to analyze urban infrastructures.

Biorefinery cascade processing for creating added value on tomato industrial by-products from Tunisia.

PubMed

Kehili, Mouna; Schmidt, Lisa Marie; Reynolds, Wienke; Zammel, Ayachi; Zetzl, Carsten; Smirnova, Irina; Allouche, Noureddine; Sayadi, Sami

2016-01-01

In today's consumer perception of industrial processes and food production, aspects like food quality, human health, environmental safety, and energy security have become the keywords. Therefore, much effort has been extended toward adding value to biowastes of agri-food industries through biorefinery processing approaches. This study focused, for the first time, on the valorization of tomato by-products of a Tunisian industry for the recovery of value-added compounds using biorefinery cascade processing. The process integrated supercritical CO 2 extraction of carotenoids within the oil fractions from tomato seeds (TS) and tomato peels (TP), followed by a batch isolation of protein from the residues. The remaining lignocellulosic matter from both fractions was then submitted to a liquid hot water (LHW) hydrolysis. Supercritical CO 2 experiments extracted 5.79% oleoresin, 410.53 mg lycopene/kg, and 31.38 mg β-carotene/kg from TP and 26.29% oil, 27.84 mg lycopene/kg, and 5.25 mg β-carotene/kg from TS, on dry weights. Protein extraction yields, nearing 30% of the initial protein contents equal to 13.28% in TP and 39.26% in TS, revealed that TP and TS are a rich source of essential amino acids. LHW treatment run at 120-200 °C, 50 bar for 30 min showed that a temperature of 160 °C was the most convenient for cellulose and hemicellulose hydrolysis from TP and TS, while keeping the degradation products low. Results indicated that tomato by-products are not only a green source of lycopene-rich oleoresin and tomato seed oil (TSO) and of protein with good nutritional quality but also a source of lignocellulosic matter with potential for bioethanol production. This study would provide an important reference for the concept and the feasibility of the cascade fractionation of valuable compounds from tomato industrial by-products.Graphical abstractSchema of biorefinery cascade processing of tomato industrial by-products toward isolation of valuable fractions.

Biofilms from Klebsiella pneumoniae: Matrix Polysaccharide Structure and Interactions with Antimicrobial Peptides

PubMed Central

Benincasa, Monica; Lagatolla, Cristina; Dolzani, Lucilla; Milan, Annalisa; Pacor, Sabrina; Liut, Gianfranco; Tossi, Alessandro; Cescutti, Paola; Rizzo, Roberto

2016-01-01

Biofilm matrices of two Klebsiella pneumoniae clinical isolates, KpTs101 and KpTs113, were investigated for their polysaccharide composition and protective effects against antimicrobial peptides. Both strains were good biofilm producers, with KpTs113 forming flocs with very low adhesive properties to supports. Matrix exopolysaccharides were isolated and their monosaccharide composition and glycosidic linkage types were defined. KpTs101 polysaccharide is neutral and composed only of galactose, in both pyranose and furanose ring configurations. Conversely, KpTs113 polysaccharide is anionic due to glucuronic acid units, and also contains glucose and mannose residues. The susceptibility of the two strains to two bovine cathelicidin antimicrobial peptides, BMAP-27 and Bac7(1–35), was assessed using both planktonic cultures and biofilms. Biofilm matrices exerted a relevant protection against both antimicrobials, which act with quite different mechanisms. Similar protection was also detected when antimicrobial peptides were tested against planktonic bacteria in the presence of the polysaccharides extracted from KpTs101 and KpTs113 biofilms, suggesting sequestering adduct formation with antimicrobials. Circular dichroism experiments on BMAP-27 in the presence of increasing amounts of either polysaccharide confirmed their ability to interact with the peptide and induce an α-helical conformation. PMID:27681920

Implementation of Fuzzy Decision to Control Patient Room Facilities using Eye Blink

NASA Astrophysics Data System (ADS)

Zaeni, Ilham A. E.; Wibawa, Aji P.; Aripriharta; Sendari, Siti

2018-04-01

This study proposed the implementation of Fuzzy decision to control patient’s room facilities. In this study, four icons were sequentially displayed on the computer screen. The icons representing four option that can be selected by the patient is including switch the light on/off, switch the fan on/off, moving the bed’s backrest downward, and moving the bed’s backrest upward. The eye blink was extracted from subject’s electroencephalograph (EEG) signals which acquired from the FP1 region. The attention was also extracted from subject’s EEG signals to ensure that subject concentrate to the task. The eye blink and attention level were used for Fuzzy decision inputs, while the output is a decision that states the selection is valid or not. The selected option is the command that appears on the screen when the selection is valid. In this study, subjects were asked to choose each command several times and the accuracy was computed based on the number of correct selection.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.

PubMed

Chen, Jing; Tang, Yuan Yan; Chen, C L Philip; Fang, Bin; Lin, Yuewei; Shang, Zhaowei

2014-12-01

Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

Fluorescence intensity positivity classification of Hep-2 cells images using fuzzy logic

NASA Astrophysics Data System (ADS)

Sazali, Dayang Farzana Abang; Janier, Josefina Barnachea; May, Zazilah Bt.

2014-10-01

Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer "a" and "b" (LAB) color space where the mean value of the lightness and chromaticity layer "a" was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.

Testosterone supplementation and sexual function: a meta-analysis study.

PubMed

Corona, Giovanni; Isidori, Andrea M; Buvat, Jaques; Aversa, Antonio; Rastrelli, Giulia; Hackett, Geoff; Rochira, Vincenzo; Sforza, Alessandra; Lenzi, Andrea; Mannucci, Edoardo; Maggi, Mario

2014-06-01

The role of testosterone supplementation (TS) as a treatment for male sexual dysfunction remains questionable. The aim of this study was to attempt a meta-analysis on the effect of TS on male sexual function and its synergism with the use of phosphodiesterase type 5 inhibitor (PDE5i). An extensive Medline, Embase, and Cochrane search was performed. All randomized controlled trials (RCTs) comparing the effect of TS vs. placebo or the effect of TS as add on to PDE5is on sexual function were included. Data extraction was performed independently by two of the authors (A. M. Isidori and G. Corona), and conflicts resolved by the third investigator (M. Maggi). Out of 1,702 retrieved articles, 41 were included in the study. In particular, 29 compared TS vs. placebo, whereas 12 trials evaluated the effect of TS as add on to PDE5is. TS is able to significantly ameliorate erectile function and to improve other aspects of male sexual response in hypogonadal patients. However, the presence of possible publication bias was detected. After applying "trim and fill" method, the positive effect of TS on erectile function and libido components retained significance only in RCTs partially or completely supported by pharmaceutical companies (confidence interval [0.04-0.53] and [0.12; 0.52], respectively). In addition, we also report that TS could be associated with an improvement in PDE5i outcome. These results were not confirmed in placebo-controlled studies. The majority of studies, however, included mixed eugonadal/hypogonadal subjects, thus imparting uncertainty to the statistical analyses. TS plays positive effects on male sexual function in hypogonadal subjects. The role of TS is uncertain in men who are not clearly hypogonadal. The apparent difference between industry-supported and independent studies could depend on trial design more than on publication bias. New RCTs exploring the effect of TS in selected cases of PDE5i failure that persistently retain low testosterone levels are advisable. © 2014 International Society for Sexual Medicine.

Evidence of a novel aggrecan-degrading activity in cartilage: Studies of mice deficient in both ADAMTS-4 and ADAMTS-5.

PubMed

Rogerson, Fraser M; Stanton, Heather; East, Charlotte J; Golub, Suzanne B; Tutolo, Leonie; Farmer, Pamela J; Fosang, Amanda J

2008-06-01

To characterize aggrecan catabolism and the overall phenotype in mice deficient in both ADAMTS-4 and ADAMTS-5 (TS-4/TS-5 Delta-cat) activity. Femoral head cartilage from the joints of TS-4/TS-5 Delta-cat mice and wild-type mice were cultured in vitro, and aggrecan catabolism was stimulated with either interleukin-1alpha (IL-1alpha) or retinoic acid. Total aggrecan release was measured, and aggrecanase activity was examined by Western blotting using neoepitope antibodies for detecting cleavage at EGE 373-374 ALG, SELE 1279-1280 GRG, FREEE 1467-1468 GLG, and AQE 1572-1573 AGEG. Aggrecan catabolism in vivo was examined by Western blotting of cartilage that had been extracted immediately ex vivo. TS-4/TS-5 Delta-cat mice were viable, fertile, and phenotypically normal. TS-4/TS-5 Delta-cat cartilage explants did not release aggrecan in response to IL-1alpha, and there was no detectable increase in aggrecanase neoepitopes. TS-4/TS-5 Delta-cat cartilage explants released aggrecan in response to retinoic acid. There was no retinoic acid-stimulated cleavage at either EGE 373-374 ALG or AQE 1572-1573 AGEG. There was a low level of cleavage at SELE 1279-1280 GRG and major cleavage at FREEE 1467-1468 GLG. Ex vivo, cleavage at FREEE 1467-1468 GLG was substantially reduced, but still present, in TS-4/TS-5 Delta-cat mouse cartilage compared with wild-type mouse cartilage. An aggrecanase other than ADAMTS-4 and ADAMTS-5 is expressed in mouse cartilage and is up-regulated by retinoic acid but not IL-1alpha. The novel aggrecanase appears to have different substrate specificity from either ADAMTS-4 or ADAMTS-5, cleaving E-G bonds but not E-A bonds. Neither ADAMTS-4 nor ADAMTS-5 is required for normal skeletal development or aggrecan turnover in cartilage.

Selection of Atmospheric Environmental Monitoring Sites based on Geographic Parameters Extraction of GIS and Fuzzy Matter-Element Analysis.

PubMed

Wu, Jianfa; Peng, Dahao; Ma, Jianhao; Zhao, Li; Sun, Ce; Ling, Huanzhang

2015-01-01

To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions KA (xj) and KB (xj), which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.

Characterization of a Thioredoxin-1 Gene from Taenia solium and Its Encoding Product

PubMed Central

Jiménez, Lucía; Rodríguez-Lima, Oscar; Ochoa-Sánchez, Alicia; Landa, Abraham

2015-01-01

Taenia solium thioredoxin-1 gene (TsTrx-1) has a length of 771 bp with three exons and two introns. The core promoter gene presents two putative stress transcription factor binding sites, one putative TATA box, and a transcription start site (TSS). TsTrx-1 mRNA is expressed higher in larvae than in adult. This gene encodes a protein of 107 amino acids that presents the Trx active site (CGPC), the classical secondary structure of the thioredoxin fold, and the highest degree of identity with the Echinococcus granulosus Trx. A recombinant TsTrx-1 (rTsTrx-1) was produced in Escherichia coli with redox activity. Optimal activity for rTsTrx-1 was at pH 6.5 in the range of 15 to 25°C. The enzyme conserved activity for 3 h and lost it in 24 h at 37°C. rTsTrx-1 lost 50% activity after 1 h and lost activity completely in 24 h at temperatures higher than 55°C. Best storage temperature for rTsTrx-1 was at −70°C. It was inhibited by high concentrations of H2O2 and methylglyoxal (MG), but it was inhibited neither by NaCl nor by anti-rTsTrx-1 rabbit antibodies that strongly recognized a ~12 kDa band in extracts from several parasites. These TsTrx-1 properties open the opportunity to study its role in relationship T. solium-hosts. PMID:26090410

Membership-degree preserving discriminant analysis with applications to face recognition.

PubMed

Yang, Zhangjing; Liu, Chuancai; Huang, Pu; Qian, Jianjun

2013-01-01

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

Navigating a Mobile Robot Across Terrain Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

Seraji, Homayoun; Howard, Ayanna; Bon, Bruce

2003-01-01

A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

Mitra, Sunanda

1993-01-01

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Control Synthesis of Discrete-Time T-S Fuzzy Systems: Reducing the Conservatism Whilst Alleviating the Computational Burden.

PubMed

Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Peng, Chen

2017-09-01

The augmented multi-indexed matrix approach acts as a powerful tool in reducing the conservatism of control synthesis of discrete-time Takagi-Sugeno fuzzy systems. However, its computational burden is sometimes too heavy as a tradeoff. Nowadays, reducing the conservatism whilst alleviating the computational burden becomes an ideal but very challenging problem. This paper is toward finding an efficient way to achieve one of satisfactory answers. Different from the augmented multi-indexed matrix approach in the literature, we aim to design a more efficient slack variable approach under a general framework of homogenous matrix polynomials. Thanks to the introduction of a new extended representation for homogeneous matrix polynomials, related matrices with the same coefficient are collected together into one sole set and thus those redundant terms of the augmented multi-indexed matrix approach can be removed, i.e., the computational burden can be alleviated in this paper. More importantly, due to the fact that more useful information is involved into control design, the conservatism of the proposed approach as well is less than the counterpart of the augmented multi-indexed matrix approach. Finally, numerical experiments are given to show the effectiveness of the proposed approach.

A research of road centerline extraction algorithm from high resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yushan; Xu, Tingfa

2017-09-01

Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

Thermosensitivity is reduced during fever induced by Staphylococcus aureus cells walls in rabbits.

PubMed

Tøien, Ø; Mercer, J B

1996-05-01

Thermosensitivity (TS) and threshold core temperature for metabolic cold defence were determined in six conscious rabbits before, and at seven different times after i.v. injection of killed Staphylococcus aureus (8 x 10(7) or 2 x 10(7) cell walls x kg(-1)) by exposure to short periods (5-10 min) of body cooling. Heat was extracted with a chronically implanted intravascular heat exchanger. TS was calculated by regression of metabolic heat production (M) and core temperature, as indicated by hypothalamic temperature. Threshold for cold defence (shivering threshold) was calculated as the core temperature at which the thermosensitivity line crossed preinjection resting M. The shivering thresholds followed the shape of the fever response. TS was significantly reduced (up to 49%) during the time course of fever induced by the highest dose of pyrogen only. At both high and low doses of pyrogen TS correlated negatively with shivering threshold (r = 0.66 and 0.79 respectively) with similar slopes. The reduction in TS during fever was thus associated with the increase in shivering threshold resulting from the pyrogen injection and not by the dose of pyrogen. Model considerations indicate, however, that changes in sensitivity of the thermosensory input to the hypothalamic controller may affect threshold changes but cause negligible TS changes. It is more likely that the reduction in TS is effected in the specific hypothalamic effector pathways.

Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.

PubMed

Kim, Kwang Baek; Park, Hyun Jun; Song, Doo Heon; Han, Sang-suk

2015-01-01

Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.

A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

PubMed Central

Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

2009-01-01

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

PubMed

Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-02-01

The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius). The prediction of the backscattered form function is investigated by a combined approach between fuzzy clustering algorithms and bio-inspired algorithms. Four famous fuzzy clustering algorithms: the fuzzy c-means (FCM), the Gustafson-Kessel algorithm (GK), the fuzzy c-regression model (FCRM) and the Gath-Geva algorithm (GG) are combined with particle swarm optimization and genetic algorithm. The symmetric and antisymmetric circumferential waves A, S 0 , A 1 , S 1 and S 2 are investigated in a reduced frequency (k 1 a) range extends over 0.1

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

NASA Astrophysics Data System (ADS)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

Spectrophotometric Study of the Complex Formation of Anionic Chelates of Cobalt(II) with Monotetrazolium Cations

NASA Astrophysics Data System (ADS)

Divarova, V. V.; Stojnova, K. T.; Racheva, P. V.; Lekova, V. D.

2017-05-01

The complex formation and extraction of anionic chelates of Co(II)-4-(2-thiazolylazo)resorcinol (TAR) with cations of monotetrazolium salts (TS) — (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) and 3-(2-naphthyl)-2,5-diphenyl-2H-tetrazolium chloride (TV) — in the liquid-liquid extraction system Co(II)-TAR-TS-H2O-CHCl3 were studied by spectrophotometric methods. The optimum conditions for the extraction of Co(II) were found. The molar ratio of the components and the form of the anionic chelates of Co(II) in the extracted compounds were determined by independent methods. The association process in the aqueous phase and the extraction process were investigated and quantitatively characterized. The following key constants were calculated: association constant, distribution constant, extraction constant, and recovery factor. The validity of the Beer's law was checked, and some analytical characteristics were calculated. Based on the obtained results and the lower price of the monotetrazolium salt MTT compared with that of TV, the ion-associated complex of Co(II)-TAR-MTT can be implemented for determination of cobalt(II) traces in alloys and biological, medical, and pharmaceutical samples.

Purification and kinetic analysis of cytosolic and mitochondrial thioredoxin glutathione reductase extracted from Taenia solium cysticerci.

PubMed

Plancarte, Agustin; Nava, Gabriela

2015-02-01

Thioredoxin glutathione reductases (TGRs) (EC 1.8.1.9) were purified to homogeneity from the cytosolic (cTsTGR) and mitochondrial (mTsTGR) fractions of Taenia solium, the agent responsible for neurocysticercosis, one of the major central nervous system parasitic diseases in humans. TsTGRs had a relative molecular weight of 132,000, while the corresponding value per subunit obtained under denaturing conditions, was of 62,000. Specific activities for thioredoxin reductase and glutathione reductase substrates for both TGRs explored were in the range or lower than values obtained for other platyhelminths and mammalian TGRs. cTsTGR and mTsTGR also showed hydroperoxide reductase activity using hydroperoxide as substrate. Km(DTNB) and Kcat(DTNB) values for cTsTGR and mTsTGR (88 µM and 1.9 s(-1); 45 µM and 12.6 s(-1), respectively) and Km(GSSG) and Kcat(GSSG) values for cTsTGR and mTsTGR (6.3 µM and 0.96 s(-1); 4 µM and 1.62 s(-1), respectively) were similar to or lower than those reported for mammalian TGRs. Mass spectrometry analysis showed that 12 peptides from cTsTGR and seven from mTsTGR were a match for gi|29825896 thioredoxin glutathione reductase [Echinococcus granulosus], confirming that both enzymes are TGRs. Both T. solium TGRs were inhibited by the gold compound auranofin, a selective inhibitor of thiol-dependent flavoreductases (I₅₀ = 3.25, 2.29 nM for DTNB and GSSG substrates, respectively for cTsTGR; I₅₀ = 5.6, 25.4 nM for mTsTGR toward the same substrates in the described order). Glutathione reductase activity of cTsTGR and mTsTGR exhibited hysteretic behavior with moderate to high concentrations of GSSG; this result was not observed either with thioredoxin, DTNB or NADPH. However, the observed hysteretic kinetics was suppressed with increasing amounts of both parasitic TGRs. These data suggest the existence of an effective substitute which may account for the lack of the detoxification enzymes glutathione reductase and thioredoxin reductase in T. solium, as has been described for very few other platyhelminths. Copyright © 2014 Elsevier Inc. All rights reserved.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

PubMed Central

Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

2017-01-01

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

PubMed

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves

NASA Astrophysics Data System (ADS)

Couchet, Jorge; Font, José María; Manrique, Daniel

In this paper we propose an evolutionary fuzzy neural networks system for extracting knowledge from a set of time series containing medical information. The series represent isokinetic curves obtained from a group of patients exercising the knee joint on an isokinetic dynamometer. The system has two parts: i) it analyses the time series input in order generate a simplified model of an isokinetic curve; ii) it applies a grammar-guided genetic program to obtain a knowledge base represented by a fuzzy neural network. Once the knowledge base has been generated, the system is able to perform knee injuries detection. The results suggest that evolved fuzzy neural networks perform better than non-evolutionary approaches and have a high accuracy rate during both the training and testing phases. Additionally, they are robust, as the system is able to self-adapt to changes in the problem without human intervention.

Visibility enhancement of color images using Type-II fuzzy membership function

NASA Astrophysics Data System (ADS)

Singh, Harmandeep; Khehra, Baljit Singh

2018-04-01

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

EPA Method 8321B (SW-846): Solvent-Extractable Nonvolatile Compounds by High Performance Liquid Chromatography-Thermospray-Mass Spectrometry (HPLC-TS-MS) or Ultraviolet (UV) Detection

EPA Pesticide Factsheets

Method 8321B describes procedures for preparation and analysis of solid, aqueous liquid, drinking water and wipe samples using high performance liquid chromatography and mass spectrometry for extractable non-volatile compounds.

Analytic tests and their relation to jet fuel thermal stability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heneghan, S.P.; Kauffman, R.E.

1995-05-01

The evaluation of jet fuel thermal stability (TS) by simple analytic procedures has long been a goal of fuels chemists. The reason is obvious: if the analytic chemist can determine which types of material cause his test to respond, the refiners will know which materials to remove to improve stability. Complicating this quest is the lack of an acceptable quantitative TS test with which to compare any analytic procedures. To circumvent this problem, we recently compiled the results of TS tests for 12 fuels using six separate test procedures. The results covering a range of flow and temperature conditions showmore » that TS is not as dependent on test conditions as previously thought. Also, comparing the results from these tests with several analytic procedures shows that either a measure of the number of phenols or the total sulfur present in jet fuels is strongly indicative of the TS. The phenols have been measured using a cyclic voltammetry technique and the polar material by gas chromatography (atomic emission detection) following a solid phase extraction on silica gel. The polar material has been identified as mainly phenols (by mass spectrometry identification). Measures of the total acid number or peroxide concentration have little correlation with TS.« less

Object Extraction in Cluttered Environments via a P300-Based IFCE

PubMed Central

He, Huidong; Xian, Bin; Zeng, Ming; Zhou, Huihui; Niu, Linwei; Chen, Genshe

2017-01-01

One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities. PMID:28740505

Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

2017-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

Nanostructured hybrid ZnO thin films for energy conversion

PubMed Central

2011-01-01

We report on hybrid films based on ZnO/organic dye prepared by electrodeposition using tetrasulfonated copper phthalocyanines (TS-CuPc) and Eosin-Y (EoY). Both the morphology and porosity of hybrid ZnO films are highly dependent on the type of dyes used in the synthesis. High photosensitivity was observed for ZnO/EoY films, while a very weak photoresponse was obtained for ZnO/TS-CuPc films. Despite a higher absorption coefficient of TS-CuPc than EoY, in ZnO/EoY hybrid films, the excited photoelectrons between the EoY levels can be extracted through ZnO, and the porosity of ZnO/EoY can also be controlled. PMID:21711909

Characterization of Pectins Extracted from Different Varieties of Pink/Red and White Grapefruits [Citrus Paradisi (Macf.)] by Thermal Treatment and Thermosonication.

PubMed

La Cava, Enzo L; Gerbino, Esteban; Sgroppo, Sonia C; Gómez-Zavaglia, Andrea

2018-06-01

The physical and chemical properties of pectin extracts obtained from different white and pink/red varieties of grapefruit [Citrus paradisi (Macf.)], using both conventional heating (CHE) and thermosonication (TS), were investigated. The content of galacturonic acid (GalA), degree of esterification (%DM), color and antioxidant capacity were analyzed. Fourier-Transform Infrared Spectroscopy (FTIR) associated with multivariate analysis enabled a structural comparison among the pectin extracts, and differential scanning calorimetry (DSC) completed a full landscape of the investigated extracts. Pectin extracts obtained by CHE showed mostly higher GalA than those obtained by TS. All the extracts had a high antioxidant capacity, as determined by 2,2 diphenyl 1-picrylhydrazyl (DPPH * ) and 2,2'-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS * +) assays, and a high correlation with the GalA content. The main differences observed in the FTIR spectra occurred in the 1200 to 900 cm -1 region (differences in GalA). The glass transition temperatures (Tgs) of all extracts were above 85 °C, making them interesting as stabilizing agents for the food industry. A wide database for the characterization of pectin extracts from grapefruits was obtained. The relationship between the extraction method and the source of pectins, with the physicochemical and antioxidant properties provided great support for their application in the food industry. © 2018 Institute of Food Technologists®.

Flame analysis using image processing techniques

NASA Astrophysics Data System (ADS)

Her Jie, Albert Chang; Zamli, Ahmad Faizal Ahmad; Zulazlan Shah Zulkifli, Ahmad; Yee, Joanne Lim Mun; Lim, Mooktzeng

2018-04-01

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.

Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

NASA Astrophysics Data System (ADS)

Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

2010-02-01

Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

NASA Astrophysics Data System (ADS)

Rosdi, Bakhtiar Affendi; Jaafar, Haryati; Ramli, Dzati Athiar

2015-02-01

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively.

Potassium N-Iodo p-Toluenesulfonamide (TsNIK, Iodamine-T): A New Reagent for the Oxidation of Hydrazones to Diazo Compounds

PubMed Central

Nicolle, Simon M; Moody, Christopher J

2014-01-01

A new reagent for the oxidation of hydrazones to diazo compounds is described. N-Iodo p-toluenesulfonamide (TsNIK, iodamine-T) allows the preparation of α-diazoesters, α-diazoamides, α-diazoketones and α-diazophosphonates in good yield and in high purity after a simple extractive work-up. α-Diazoesters were also obtained in high yield from the corresponding ketones through a one-pot process of hydrazone formation/oxidation. PMID:24615944

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

PubMed

Kar, Subrata; Majumder, D Dutta

2017-08-01

Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests

NASA Astrophysics Data System (ADS)

Testa, S.; Soudani, K.; Boschetti, L.; Borgogno Mondino, E.

2018-02-01

Monitoring forest phenology allows us to study the effects of climate change on vegetated land surfaces. Daily and composite time series (TS) of several vegetation indices (VIs) from MODerate resolution Imaging Spectroradiometer (MODIS) data have been widely used in scientific works for phenological studies since the beginning of the MODIS mission. The objective of this work was to use MODIS data to find the best VI/TS combination to estimate start-of-season (SOS) and end-of-season (EOS) dates across 50 temperate deciduous forests. Our research used as inputs 2001-2012 daily reflectance from MOD09GQ/MOD09GA products and 16-day composite VIs from the MOD13Q1 dataset. The 50 pixels centered on the 50 forest plots were extracted from the above-mentioned MODIS imagery; we then generated 5 different types of TS (1 daily from MOD09 and 4 composite from MOD13Q1) and used all of them to implement 6 VIs, obtaining 30 VI/TS combinations. SOS and EOS estimates were determined for each pixel/year and each VI/TS combination. SOS/EOS estimations were then validated against ground phenological observations. Results showed that, in our test areas, composite TS, if actual acquisition date is considered, performed mostly better than daily TS. EVI, WDRVI0.20 and NDVI were more suitable to SOS estimation, while WDRVI0.05 and EVI were more convenient in estimating early and advanced EOS, respectively.

Generation and characterization of monoclonal antibodies specific for 18 kDa antigen from Taenia solium cysticerci.

PubMed

Zhang, Shaohua; Luo, Xuenong; Guo, Aijiang; Zhu, Xueliang; Cai, Xuepeng

2016-07-01

The gene encoding a mature 18 kDa glycoprotein of Taenia solium cysticerci (Ts18) was cloned and bacterially expressed with a His-tagged fusion protein. Monoclonal antibodies (MAbs) against the recombinant Ts18 antigen were generated in vitro by routine murine hybridoma technique of fusing splenocytes, from BALB/c mice immunized with the vesicular fluid of T. solium cysticerci (TsVF), with mouse myeloma cells (SP2/0). The reactivity and specificity of these MAbs were evaluated by indirect ELISA and immunoblotting techniques. Three stable hybridoma clones, namely 3B11, 6C5, and 6G4, were screened using His-Ts18-based ELISA, and these showed two IgG1 isotypes and one IgM isotype. All MAbs reacted with His-Ts18 at molecular weight (MW) 12.8 kDa and the native antigen at MW 18 kDa in TsVF and whole larval extracts (WLE). In a dot blotting test, MAbs 6C5 and 6G4 showed no obvious cross-reactivity with heterologous vesicular fluids from other taeniid species, including Taenia saginata (TsaVF), Taenia pisiformis (TpVF), Taenia hydatigena (ThVF), Taenia multiceps (TmVF), and Echinococcus granulosus (EgVF). Immunofluorescent assays showed that MAb 6C5 specifically reacted with the Ts18 expressed from pEGFP-N1-Ts18-transfected HeLa cells. Immunolocalization analysis, using MAb 6C5 as a probe, indicated that Ts18 was present at high concentrations in the region of the larval sucker and spiral canal. The results indicate that the Ts18 protein is an abundantly secreted parasite protein and MAbs against it might provide a step forward for improving the diagnosis of porcine cysticercosis. Copyright © 2016 Elsevier Inc. All rights reserved.

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

PubMed

Badawi, A M; Derbala, A S; Youssef, A M

1999-08-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

PubMed Central

2012-01-01

Background Across the globe, breast cancer is one of the leading causes of death among women and, currently, Fine Needle Aspirate (FNA) with visual interpretation is the easiest and fastest biopsy technique for the diagnosis of this deadly disease. Unfortunately, the ability of this method to diagnose cancer correctly when the disease is present varies greatly, from 65% to 98%. This article introduces a method to assist in the diagnosis and second opinion of breast cancer from the analysis of descriptors extracted from smears of breast mass obtained by FNA, with the use of computational intelligence resources - in this case, fuzzy logic. Methods For data acquisition of FNA, the Wisconsin Diagnostic Breast Cancer Data (WDBC), from the University of California at Irvine (UCI) Machine Learning Repository, available on the internet through the UCI domain was used. The knowledge acquisition process was carried out by the extraction and analysis of numerical data of the WDBC and by interviews and discussions with medical experts. The PDM-FNA-Fuzzy was developed in four steps: 1) Fuzzification Stage; 2) Rules Base; 3) Inference Stage; and 4) Defuzzification Stage. Performance cross-validation was used in the tests, with three databases with gold pattern clinical cases randomly extracted from the WDBC. The final validation was held by medical specialists in pathology, mastology and general practice, and with gold pattern clinical cases, i.e. with known and clinically confirmed diagnosis. Results The Fuzzy Method developed provides breast cancer pre-diagnosis with 98.59% sensitivity (correct pre-diagnosis of malignancies); and 85.43% specificity (correct pre-diagnosis of benign cases). Due to the high sensitivity presented, these results are considered satisfactory, both by the opinion of medical specialists in the aforementioned areas and by comparison with other studies involving breast cancer diagnosis using FNA. Conclusions This paper presents an intelligent method to assist in the diagnosis and second opinion of breast cancer, using a fuzzy method capable of processing and sorting data extracted from smears of breast mass obtained by FNA, with satisfactory levels of sensitivity and specificity. The main contribution of the proposed method is the reduction of the variation hit of malignant cases when compared to visual interpretation currently applied in the diagnosis by FNA. While the MPD-FNA-Fuzzy features stable sensitivity at 98.59%, visual interpretation diagnosis provides a sensitivity variation from 65% to 98% (this track showing sensitivity levels below those considered satisfactory by medical specialists). Note that this method will be used in an Intelligent Virtual Environment to assist the decision-making (IVEMI), which amplifies its contribution. PMID:23122391

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

NASA Astrophysics Data System (ADS)

Ventosa, Sergi; Schimmel, Martin; Stutzmann, Eleonore

2017-10-01

Stacks of ambient noise correlations are routinely used to extract empirical Green's functions (EGFs) between station pairs. The time-frequency phase-weighted stack (tf-PWS) is a physically intuitive nonlinear denoising method that uses the phase coherence to improve EGF convergence when the performance of conventional linear averaging methods is not sufficient. The high computational cost of a continuous approach to the time-frequency transformation is currently a main limitation in ambient noise studies. We introduce the time-scale phase-weighted stack (ts-PWS) as an alternative extension of the phase-weighted stack that uses complex frames of wavelets to build a time-frequency representation that is much more efficient and fast to compute and that preserve the performance and flexibility of the tf-PWS. In addition, we propose two strategies: the unbiased phase coherence and the two-stage ts-PWS methods to further improve noise attenuation, quality of the extracted signals and convergence speed. We demonstrate that these approaches enable to extract minor- and major-arc Rayleigh waves (up to the sixth Rayleigh wave train) from many years of data from the GEOSCOPE global network. Finally we also show that fundamental spheroidal modes can be extracted from these EGF.

Intelligent Paging Based Mobile User Tracking Using Fuzzy Logic

NASA Astrophysics Data System (ADS)

Saha, Sajal; Dutta, Raju; Debnath, Soumen; Mukhopadhyay, Asish K.

2010-11-01

In general, a mobile user travels in a predefined path that depends mostly on the user's characteristics. Thus, tracking the locations of a mobile user is one of the challenges for location management. In this paper, we introduce a movement pattern learning strategy system to track the user's movements using adaptive fuzzy logic. Our fuzzy inference system extracts patterns from the historical data record of the cell numbers along with the date and time stamp of the users occupying the cell. Implementation of this strategy has been evaluated with the real time user data which proves the efficiency and accuracy of the model. This mechanism not only reduces user location tracking costs, but also significantly decreases the call-loss rates and average paging delays.

Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

NASA Astrophysics Data System (ADS)

Li, Mengmeng; Bijker, Wietske; Stein, Alfred

2015-04-01

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

Comparison of DNA extraction methods for human gut microbial community profiling.

PubMed

Lim, Mi Young; Song, Eun-Ji; Kim, Sang Ho; Lee, Jangwon; Nam, Young-Do

2018-03-01

The human gut harbors a vast range of microbes that have significant impact on health and disease. Therefore, gut microbiome profiling holds promise for use in early diagnosis and precision medicine development. Accurate profiling of the highly complex gut microbiome requires DNA extraction methods that provide sufficient coverage of the original community as well as adequate quality and quantity. We tested nine different DNA extraction methods using three commercial kits (TianLong Stool DNA/RNA Extraction Kit (TS), QIAamp DNA Stool Mini Kit (QS), and QIAamp PowerFecal DNA Kit (QP)) with or without additional bead-beating step using manual or automated methods and compared them in terms of DNA extraction ability from human fecal sample. All methods produced DNA in sufficient concentration and quality for use in sequencing, and the samples were clustered according to the DNA extraction method. Inclusion of bead-beating step especially resulted in higher degrees of microbial diversity and had the greatest effect on gut microbiome composition. Among the samples subjected to bead-beating method, TS kit samples were more similar to QP kit samples than QS kit samples. Our results emphasize the importance of mechanical disruption step for a more comprehensive profiling of the human gut microbiome. Copyright © 2017 The Authors. Published by Elsevier GmbH.. All rights reserved.

What is the clinical course of transient synovitis in children: a systematic review of the literature.

PubMed

Asche, Sylvana S; van Rijn, Rogier M; Bessems, Johannes Hjm; Krul, Marjolein; Bierma-Zeinstra, Sita Ma

2013-11-14

Transient synovitis of the hip (TS) is considered to be a self-limiting disease in childhood. However, because the etiology is unclear and some cases precede Legg-Perthes' disease, data on follow-up are important. Our aim was to summarize the knowledge on the clinical course of TS in children. The study design was a systematic review and a literature search was conducted in Medline and Embase. Studies describing short and/or long-term follow-up of TS in children were included. Case reports, reviews and studies describing traumatic hip pain were excluded. Study quality was scored and data extraction was performed. The main outcome measures were short-term and long-term clinical course, and recurrence of symptoms. A total of 25 studies were included of which 14 were of high quality. At two-week follow-up, almost all children with TS were symptom free. Those with symptoms persisting for over one month were more prone to develop other hip pathology, such as Legg-Perthes' disease. The recurrence rate of TS ranged from 0-26.3%. At long-term follow-up, 0-10% of the children diagnosed with TS developed Legg-Perthes' disease. Hip pain after intensive physical effort and limited range of motion of the hip at long-term follow-up was reported in 12-28% and in 0-18% of the children, respectively. The majority of the studies indicate that children with TS recover within two weeks; recurrence was seen in 0-26% of the cases. Children with TS should be followed at least six months to increase the likelihood of not missing Legg-Perthes' disease.

Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry.

PubMed

Tokunaga, Masanori; Kami, Kenjiro; Ozawa, Soji; Oguma, Junya; Kazuno, Akihito; Miyachi, Hayato; Ohashi, Yoshiaki; Kusuhara, Masatoshi; Terashima, Masanori

2018-06-01

Reports of the metabolomic characteristics of esophageal cancer are limited. In the present study, we thus conducted metabolome analysis of paired tumor tissues (Ts) and non-tumor esophageal tissues (NTs) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). The Ts and surrounding NTs were surgically excised pair-wise from 35 patients with esophageal cancer. Following tissue homogenization and metabolite extraction, a total of 110 compounds were absolutely quantified by CE-TOFMS. We compared the concentrations of the metabolites between Ts and NTs, between pT1 or pT2 (pT1-2) and pT3 or pT4 (pT3-4) stage, and between node-negative (pN-) and node-positive (pN+) samples. Principal component analysis and hierarchical clustering analysis revealed clear metabolomic differences between Ts and NTs. Lactate and citrate levels in Ts were significantly higher (P=0.001) and lower (P<0.001), respectively, than those in NTs, which corroborated with the Warburg effect in Ts. The concentrations of most amino acids apart from glutamine were higher in Ts than in NTs, presumably due to hyperactive glutaminolysis in Ts. The concentrations of malic acid (P=0.015) and citric acid (P=0.008) were significantly lower in pT3-4 than in pT1-2, suggesting the downregulation of tricarboxylic acid (TCA) cycle activity in pT3-4. On the whole, in this study, we demonstrate significantly different metabolomic characteristics between tumor and non-tumor tissues and identified a novel set of metabolites that were strongly associated with the degree of tumor progression. A further understanding of cancer metabolomics may enable the selection of more appropriate treatment strategies, thereby contributing to individualized medicine.

Extracting fuzzy rules under uncertainty and measuring definability using rough sets

NASA Technical Reports Server (NTRS)

Culas, Donald E.

1991-01-01

Although computers have come a long way since their invention, they are basically able to handle only crisp values at the hardware level. Unfortunately, the world we live in consists of problems which fail to fall into this category, i.e., uncertainty is all too common. A problem is looked at which involves uncertainty. To be specific, attributes are dealt with which are fuzzy sets. Under this condition, knowledge is acquired by looking at examples. In each example, a condition as well as a decision is made available. Based on the examples given, two sets of rules are extracted, certain and possible. Furthermore, measures are constructed of how much these rules are believed in, and finally, the decisions are defined as a function of the terms used in the conditions.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung

2010-06-01

Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.

A fuzzy automated object classification by infrared laser camera

NASA Astrophysics Data System (ADS)

Kanazawa, Seigo; Taniguchi, Kazuhiko; Asari, Kazunari; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

2011-06-01

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Fuzzy based finger vein recognition with rotation invariant feature matching

NASA Astrophysics Data System (ADS)

Ezhilmaran, D.; Joseph, Rose Bindu

2017-11-01

Finger vein recognition is a promising biometric with commercial applications which is explored widely in the recent years. In this paper, a finger vein recognition system is proposed using rotation invariant feature descriptors for matching after enhancing the finger vein images with an interval type-2 fuzzy method. SIFT features are extracted and matched using a matching score based on Euclidian distance. Rotation invariance of the proposed method is verified in the experiment and the results are compared with SURF matching and minutiae matching. It is seen that rotation invariance is verified and the poor quality issues are solved efficiently with the designed system of finger vein recognition during the analysis. The experiments underlines the robustness and reliability of the interval type-2 fuzzy enhancement and SIFT feature matching.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

PubMed

Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

2016-12-01

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Recognition of pigment network pattern in dermoscopy images based on fuzzy classification of pixels.

PubMed

Garcia-Arroyo, Jose Luis; Garcia-Zapirain, Begonya

2018-01-01

One of the most relevant dermoscopic patterns is the pigment network. An innovative method of pattern recognition is presented for its detection in dermoscopy images. It consists of two steps. In the first one, by means of a supervised machine learning process and after performing the extraction of different colour and texture features, a fuzzy classification of pixels into the three categories present in the pattern's definition ("net", "hole" and "other") is carried out. This enables the three corresponding fuzzy sets to be created and, as a result, the three probability images that map them out are generated. In the second step, the pigment network pattern is characterised from a parameterisation process -derived from the system specification- and the subsequent extraction of different features calculated from the combinations of image masks extracted from the probability images, corresponding to the alpha-cuts obtained from the fuzzy sets. The method was tested on a database of 875 images -by far the largest used in the state of the art to detect pigment network- extracted from a public Atlas of Dermoscopy, obtaining AUC results of 0.912 and 88%% accuracy, with 90.71%% sensitivity and 83.44%% specificity. The main contribution of this method is the very design of the algorithm, highly innovative, which could also be used to deal with other pattern recognition problems of a similar nature. Other contributions are: 1. The good performance in discriminating between the pattern and the disturbing artefacts -which means that no prior preprocessing is required in this method- and between the pattern and other dermoscopic patterns; 2. It puts forward a new methodological approach for work of this kind, introducing the system specification as a required step prior to algorithm design and development, being this specification the basis for a required parameterisation -in the form of configurable parameters (with their value ranges) and set threshold values- of the algorithm and the subsequent conducting of the experiments. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

NASA Astrophysics Data System (ADS)

Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

2017-02-01

To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.

Cortical Correlates of Fitts’ Law

PubMed Central

Ifft, Peter J.; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2011-01-01

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control. PMID:22275888

Intelligent Traffic Quantification System

NASA Astrophysics Data System (ADS)

Mohanty, Anita; Bhanja, Urmila; Mahapatra, Sudipta

2017-08-01

Currently, city traffic monitoring and controlling is a big issue in almost all cities worldwide. Vehicular ad-hoc Network (VANET) technique is an efficient tool to minimize this problem. Usually, different types of on board sensors are installed in vehicles to generate messages characterized by different vehicle parameters. In this work, an intelligent system based on fuzzy clustering technique is developed to reduce the number of individual messages by extracting important features from the messages of a vehicle. Therefore, the proposed fuzzy clustering technique reduces the traffic load of the network. The technique also reduces congestion and quantifies congestion.

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumidu Wijayasekara; Ondrej Linda; Milos Manic

Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)more » based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.« less

A Protocol for Using Förster Resonance Energy Transfer (FRET)-force Biosensors to Measure Mechanical Forces across the Nuclear LINC Complex.

PubMed

Arsenovic, Paul T; Bathula, Kranthidhar; Conway, Daniel E

2017-04-11

The LINC complex has been hypothesized to be the critical structure that mediates the transfer of mechanical forces from the cytoskeleton to the nucleus. Nesprin-2G is a key component of the LINC complex that connects the actin cytoskeleton to membrane proteins (SUN domain proteins) in the perinuclear space. These membrane proteins connect to lamins inside the nucleus. Recently, a Förster Resonance Energy Transfer (FRET)-force probe was cloned into mini-Nesprin-2G (Nesprin-TS (tension sensor)) and used to measure tension across Nesprin-2G in live NIH3T3 fibroblasts. This paper describes the process of using Nesprin-TS to measure LINC complex forces in NIH3T3 fibroblasts. To extract FRET information from Nesprin-TS, an outline of how to spectrally unmix raw spectral images into acceptor and donor fluorescent channels is also presented. Using open-source software (ImageJ), images are pre-processed and transformed into ratiometric images. Finally, FRET data of Nesprin-TS is presented, along with strategies for how to compare data across different experimental groups.

Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Yading, E-mail: yading.yuan@mssm.edu; Chao, Ming; Sheu, Ren-Dih

Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border.more » The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.« less

Forced heat loss from body surface reduces heat flow to body surface.

PubMed

Berman, A

2010-01-01

Heat stress is commonly relieved by forced evaporation from body surfaces. The mode of heat stress relief by heat extraction from the periphery is not clear, although it reduces rectal temperature. Radiant surface temperature (Ts) of the right half of the body surface was examined by thermovision in 4 lactating Holstein cows (30 kg of milk/d) during 7 repeated cycles of forced evaporation created by 30s of wetting followed by 4.5 min of forced airflow. Wetting was performed by an array of sprinklers (0.76 m(3)/h), and forced airflow (>3m/s velocity) over the right side of the body surface was produced by fans mounted at a height of 3m above the ground. Sprinkling wetted the hind legs, rump, and chest, but not the lower abdomen side, front legs, or neck. The animals were maintained in shade at an air temperature of 28 degrees C and relative humidity of 47%. Coat thickness was 1 to 2mm, so Ts closely represented skin temperature. Mean Ts of 5 x 20cm areas on the upper and lower hind and front legs, rump, chest, abdomen side, and neck were obtained by converting to temperature their respective gray intensity in single frames obtained at 10-s intervals. Little change occurred in Ts during the first wetting (0.1+/-0.6 degrees C), but it decreased rapidly thereafter (1.6+/-0.6 degrees C in the fifth wetting). The Ts also decreased, to a smaller extent, in areas that remained dry (0.7+/-1.0 degrees C). In all body sites, a plateau in Ts was reached by 2 min after wetting. The difference between dry and wet areas in the first cooling cycle was approximately 1.2 degrees C. The Ts of different body areas decreased during consecutive cooling cycles and reached a plateau by 3 cooling cycles in dry sites (front leg, neck, abdomen side), by 5 cooling cycles in the hind leg, and 7 cooling cycles in the rump and chest. The reduction in mean Ts produced by 7 cycles was 4.0 to 6.0 degrees C in wetted areas and 1.6 to 3.7 degrees C in sites that were not wetted. Initial rectal temperature was 38.9+/-0.1 degrees C; it remained unchanged during first 5 cooling cycles, decreased by 0.1 degrees C after 7 cooling cycles, and decreased to 38.4+/-0.06 degrees C after 8 to 10 cooling cycles, with no additional subsequent decrease. The concomitant reduction in Ts in dry and wet areas suggests an immediate vasoconstrictor response associated with heat extraction and later development of a cooler body shell. The reduction in rectal temperature represents a response involving transfer of heat from the body core to the body shell. This response mode requires consideration in settings of heat stress relief. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Water-soluble phenol TS-13 combats acute but not chronic inflammation.

PubMed

Menshchikova, Elena; Tkachev, Victor; Lemza, Anna; Sharkova, Tatyana; Kandalintseva, Natalya; Vavilin, Valentin; Safronova, Olga; Zenkov, Nikolay

2014-09-01

This study was conducted to evaluate the effect of the synthetic water-soluble phenolic antioxidant TS-13 (sodium 3-(4'-methoxyphenyl)propyl thiosulfonate), an inducer of the redox-dependent Keap1/Nrf2/ARE signaling system, in experimental models of acute and chronic inflammation. Acute local inflammation was induced by intraplantar carrageenan injection into rat hind paws, and acute systemic inflammation was modeled by intravenous zymosan injection (in rats) or LPS-induced endotoxic shock (in mice). Chronic inflammation was investigated in rat models of air pouch and collagen-induced arthritis. The effects of TS-13 treatment were estimated by changes in the intensity of inflammation (paw edema, liver infiltration, animal survival, exudation, and clinical score of arthritis) and by the effects on reactive oxygen species (ROS) generation by leukocytes from peripheral blood and inflammatory exudates. We found the significant increase in expression of mRNA, content of protein and activity of a well-characterized Nrf2 target enzyme glutathione S-transferase P1, as well as nuclear extract protein binding to the ARE consensus sequence in liver of mice fed with diet containing TS-13. TS-13 markedly attenuated carrageenan-induced paw edema, reduced blood granulocyte number and volume density of liver infiltrates in the systemic zymosan-induced inflammation model, and increased mice survival after lipopolysaccharide-induced septic shock. However, TS-13 administration did not influence cell and protein exudation into air pouches and suppressed clinical manifestation of collagen-induced polyarthritis only at early stages. Nevertheless, TS-13 inhibited the generation of ROS by leukocytes in all inflammation models. The data suggest that the anti-inflammatory effects of Keap1/Nrf2/ARE system are more prominent against acute innate-mediated inflammation than chronic immune inflammation. This narrows the potential therapeutic efficacy of ARE inducers in inflammation treatment.

Features extraction in anterior and posterior cruciate ligaments analysis.

PubMed

Zarychta, P

2015-12-01

The main aim of this research is finding the feature vectors of the anterior and posterior cruciate ligaments (ACL and PCL). These feature vectors have to clearly define the ligaments structure and make it easier to diagnose them. Extraction of feature vectors is obtained by analysis of both anterior and posterior cruciate ligaments. This procedure is performed after the extraction process of both ligaments. In the first stage in order to reduce the area of analysis a region of interest including cruciate ligaments (CL) is outlined in order to reduce the area of analysis. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges has been implemented. After finding the region of interest (ROI), the fuzzy connectedness procedure is performed. This procedure permits to extract the anterior and posterior cruciate ligament structures. In the last stage, on the basis of the extracted anterior and posterior cruciate ligament structures, 3-dimensional models of the anterior and posterior cruciate ligament are built and the feature vectors created. This methodology has been implemented in MATLAB and tested on clinical T1-weighted magnetic resonance imaging (MRI) slices of the knee joint. The 3D display is based on the Visualization Toolkit (VTK). Copyright © 2015 Elsevier Ltd. All rights reserved.

French Brittany macroalgae screening: composition and methane potential for potential alternative sources of energy and products.

PubMed

Jard, G; Marfaing, H; Carrère, H; Delgenes, J P; Steyer, J P; Dumas, C

2013-09-01

Macroalgae are biomass resources that represent a valuable feedstock to be used entirely for human consumption or for food additives after some extractions (mainly colloids) and/or for energy production. In order to better develop the algal sector, it is important to determine the capacity of macroalgae to produce these added-values molecules for food and/or for energy industries on the basis of their biochemical characteristics. In this study, ten macroalgae obtained from French Brittany coasts (France) were selected. The global biochemical composition (proteins, lipids, carbohydrates, fibers), the presence and characteristics of added-values molecules (alginates, polyphenols) and the biochemical methane potential of these algae were determined. Regarding its biochemical composition, Palmaria palmata is interesting for food (rich in nutrients) and for anaerobic digestion (0.279 LCH4/gVS). Saccharina latissima could be used for alginate extraction (242 g/kgTS, ratio between mannuronic and guluronic acid M/G=1.4) and Sargassum muticum for polyphenol extraction (19.8 g/kgTS). Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Valorization of a treated soil via amendments: fractionation and oral bioaccessibility of Cu, Ni, Pb, and Zn.

PubMed

Zagury, Gerald J; Rincon Bello, Jhony A; Guney, Mert

2016-04-01

The present study aims to transform a treated soil (TS) into a more desirable resource by modifying physico-chemical properties via amendments while reducing toxic metals' mobility and oral bioaccessibility. A hydrocarbon-contaminated soil submitted to treatment (TS) but still containing elevated concentrations of Cu, Ni, Pb, and Zn has been amended with compost, sand, and Al2(SO4)3 to render it usable for horticulture. Characterization and sequential extraction were performed for TS and four amended mixtures (AM1-4). P and K availability and metal bioaccessibility were investigated in TS and AM2. Amendment improved soil properties for all mixtures and yielded a usable product (AM2 20 % TS, 49 % compost, 30 % sand, 1 % Al2(SO4)3) satisfying regulatory requirements except for Pb content. In particular, AM2 had improved organic matter (OM) and cation exchange capacity (CEC), highly increased P and K availability, and reduced total metal concentrations. Furthermore, amendment decreased metal mobile fraction likely to be plant-available (in mg kg(-1), assumed as soluble/exchangeable + carbonates fractions). For AM2, estimated Pb bioavailability decreased from 1.50 × 10(3) mg kg(-1) (TS) to 238 mg kg(-1) (52.4 % (TS) to 34.2 %). Bioaccessible concentrations of Cu, Ni, and Zn (mg kg(-1)) were lower in AM2 than in TS, but there was no significant decrease for Pb. The results suggest that amendment improved soil by modifying its chemistry, resulting in lower metal mobile fraction (in %, for Cu and Zn) and bioaccessibility (in %, for Cu only). Amending soils having residual metal contamination can be an efficient valorization method, indicating potential for reducing treatment cost and environmental burden by rendering disposal/additional treatment unnecessary. Further studies including plant bioavailability are recommended to confirm results.

Tobacco Smoking and Its Association with Illicit Drug Use among Young Men Aged 15-24 Years Living in Urban Slums of Bangladesh

PubMed Central

Kabir, Mohammad Alamgir; Goh, Kim-Leng; Kamal, Sunny Mohammad Mostafa; Khan, Md. Mobarak Hossain

2013-01-01

Background Tobacco smoking (TS) and illicit drug use (IDU) are of public health concerns especially in developing countries, including Bangladesh. This paper aims to (i) identify the determinants of TS and IDU, and (ii) examine the association of TS with IDU among young slum dwellers in Bangladesh. Methodology/Principal Findings Data on a total of 1,576 young slum dwellers aged 15–24 years were extracted for analysis from the 2006 Urban Health Survey (UHS), which covered a nationally representative sample of 13,819 adult men aged 15–59 years from slums, non-slums and district municipalities of six administrative regions in Bangladesh. Methods used include frequency run, Chi-square test of association and multivariable logistic regression. The overall prevalence of TS in the target group was 42.3%, of which 41.4% smoked cigarettes and 3.1% smoked bidis. The regression model for TS showed that age, marital status, education, duration of living in slums, and those with sexually transmitted infections were significantly (p<0.001 to p<0.05) associated with TS. The overall prevalence of IDU was 9.1%, dominated by those who had drug injections (3.2%), and smoked ganja (2.8%) and tari (1.6%). In the regression model for IDU, the significant (p<0.01 to p<0.10) predictors were education, duration of living in slums, and whether infected by sexually transmitted diseases. The multivariable logistic regression (controlling for other variables) revealed significantly (p<0.001) higher likelihood of IDU (OR = 9.59, 95% CI = 5.81–15.82) among users of any form of TS. The likelihood of IDU increased significantly (p<0.001) with increased use of cigarettes. Conclusions/Significance Certain groups of youth are more vulnerable to TS and IDU. Therefore, tobacco and drug control efforts should target these groups to reduce the consequences of risky lifestyles through information, education and communication (IEC) programs. PMID:23935885

Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

NASA Astrophysics Data System (ADS)

Subagadis, Y. H.; Schütze, N.; Grundmann, J.

2014-09-01

The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water-society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA) using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

Trans-skull ultrasonic Doppler system aided by fuzzy logic

NASA Astrophysics Data System (ADS)

Hata, Yutaka; Nakamura, Masato; Yagi, Naomi; Ishikawa, Tomomoto

2012-06-01

This paper describes a trans-skull ultrasonic Doppler system for measuring the blood flow direction in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the fuzzy degree of blood flow by Doppler Effect, thereby it locates blood vessel. This Doppler Effect is examined by the center of gravity shift of the frequency magnitudes. In in-vitro experiment, a cow bone was employed as the skull, and three silicon tubes were done as blood vessels, and bubble in water as blood. We received the ultrasonic waves through a protein, the skull and silicon tubes in order. In the system, fuzzy degrees are determined with respect to the Doppler shift, amplitude of the waves and attenuation of the tissues. The fuzzy degrees of bone and blood direction are calculated by them. The experimental results showed that the system successfully visualized the skull and flow direction, compared with the location and flow direction of the phantom. Thus, it detected the flow direction by Doppler Effect under skull, and automatically extracted the region of skull and blood vessel.

Constructing the Indicators of Assessing Human Vulnerability to Industrial Chemical Accidents: A Consensus-based Fuzzy Delphi and Fuzzy AHP Approach.

PubMed

Fatemi, Farin; Ardalan, Ali; Aguirre, Benigno; Mansouri, Nabiollah; Mohammadfam, Iraj

2017-04-10

Industrial chemical accidents have been increased in developing countries. Assessing the human vulnerability in the residents of industrial areas is necessary for reducing the injuries and causalities of chemical hazards. The aim of this study was to explore the key indicators for the assessment of human vulnerability in the residents living near chemical installations. The indicators were established in the present study based on the Fuzzy Delphi method (FDM) and Fuzzy Analytic Hierarchy Process (FAHP). The reliability of FDM and FAHP was calculated. The indicators of human vulnerability were explored in two sets of social and physical domains. Thirty-five relevant experts participated in this study during March-July 2015. According to experts, the top three indicators of human vulnerability according to the FDM and FAHP were vulnerable groups, population density, and awareness. Detailed sub-vulnerable groups and awareness were developed based on age, chronic or severe diseases, disability, first responders, and residents, respectively. Each indicator and sub-indicator was weighted and ranked and had an acceptable consistency ratio. The importance of social vulnerability indicators are about 7 times more than physical vulnerability indicators. Among the extracted indicators, vulnerable groups had the highest weight and the greatest impact on human vulnerability. however, further research is needed to investigate the applicability of established indicators and generalizability of the results to other studies. Fuzzy Delphi; Fuzzy AHP; Human vulnerability; Chemical hazards.

Constructing the Indicators of Assessing Human Vulnerability to Industrial Chemical Accidents: A Consensus-based Fuzzy Delphi and Fuzzy AHP Approach

PubMed Central

Fatemi, Farin; Ardalan, Ali; Aguirre, Benigno; Mansouri, Nabiollah; Mohammadfam, Iraj

2017-01-01

Introduction: Industrial chemical accidents have been increased in developing countries. Assessing the human vulnerability in the residents of industrial areas is necessary for reducing the injuries and causalities of chemical hazards. The aim of this study was to explore the key indicators for the assessment of human vulnerability in the residents living near chemical installations. Methods: The indicators were established in the present study based on the Fuzzy Delphi method (FDM) and Fuzzy Analytic Hierarchy Process (FAHP). The reliability of FDM and FAHP was calculated. The indicators of human vulnerability were explored in two sets of social and physical domains. Thirty-five relevant experts participated in this study during March-July 2015. Results: According to experts, the top three indicators of human vulnerability according to the FDM and FAHP were vulnerable groups, population density, and awareness. Detailed sub-vulnerable groups and awareness were developed based on age, chronic or severe diseases, disability, first responders, and residents, respectively. Each indicator and sub-indicator was weighted and ranked and had an acceptable consistency ratio. Conclusions: The importance of social vulnerability indicators are about 7 times more than physical vulnerability indicators. Among the extracted indicators, vulnerable groups had the highest weight and the greatest impact on human vulnerability. however, further research is needed to investigate the applicability of established indicators and generalizability of the results to other studies. Key words: Fuzzy Delphi; Fuzzy AHP; Human vulnerability; Chemical hazards PMID:28480124

A new method for generating an invariant iris private key based on the fuzzy vault system.

PubMed

Lee, Youn Joo; Park, Kang Ryoung; Lee, Sung Joo; Bae, Kwanghyuk; Kim, Jaihie

2008-10-01

Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.

Enhancing fuzzy robot navigation systems by mimicking human visual perception of natural terrain traversibility

NASA Technical Reports Server (NTRS)

Tunstel, E.; Howard, A.; Edwards, D.; Carlson, A.

2001-01-01

This paper presents a technique for learning to assess terrain traversability for outdoor mobile robot navigation using human-embedded logic and real-time perception of terrain features extracted from image data.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space.

PubMed

Aydin, Ilhan; Karakose, Mehmet; Akin, Erhan

2014-03-01

Although reconstructed phase space is one of the most powerful methods for analyzing a time series, it can fail in fault diagnosis of an induction motor when the appropriate pre-processing is not performed. Therefore, boundary analysis based a new feature extraction method in phase space is proposed for diagnosis of induction motor faults. The proposed approach requires the measurement of one phase current signal to construct the phase space representation. Each phase space is converted into an image, and the boundary of each image is extracted by a boundary detection algorithm. A fuzzy decision tree has been designed to detect broken rotor bars and broken connector faults. The results indicate that the proposed approach has a higher recognition rate than other methods on the same dataset. © 2013 ISA Published by ISA All rights reserved.

Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

NASA Astrophysics Data System (ADS)

Chang, Ya-Ting; Chang, Li-Chiu; Chang, Fi-John

2005-04-01

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input-output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved.

Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the Estimation of Depth of Anaesthesia.

PubMed

Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P

2018-01-01

The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.

An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases.

PubMed

Sengur, Abdulkadir

2008-03-01

In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis.

PubMed

Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

2013-06-21

A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

PubMed Central

Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

2013-01-01

A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021

Comparison and optimization of detection methods for noroviruses in frozen strawberries containing different amounts of RT-PCR inhibitors.

PubMed

Bartsch, Christina; Szabo, Kathrin; Dinh-Thanh, Mai; Schrader, Christina; Trojnar, Eva; Johne, Reimar

2016-12-01

Frozen berries have been repeatedly identified as vehicles for norovirus (NoV) transmission causing large gastroenteritis outbreaks. However, virus detection in berries is often hampered by the presence of RT-PCR-inhibiting substances. Here, several virus extraction methods for subsequent real-time RT-PCR-based NoV-RNA detection in strawberries were compared and optimized. NoV recovery rates (RRs) between 0.21 ± 0.13% and 10.29 ± 6.03% were found when five different artificially contaminated strawberry batches were analyzed by the ISO/TS15216-2 method indicating the presence of different amounts of RT-PCR inhibitors. A comparison of five different virus extraction methods using artificially contaminated strawberries containing high amounts of RT-PCR inhibitors revealed the best NoV RRs for the ISO/TS15216 method. Further improvement of NoV RRs from 2.83 ± 2.92% to 15.28 ± 9.73% was achieved by the additional use of Sephacryl(®)-based columns for RNA purification. Testing of 22 frozen strawberry samples from a batch involved in a gastroenteritis outbreak resulted in 5 vs. 13 NoV GI-positive and in 9 vs. 20 NoV GII-positive samples using the original ISO/TS15216 method vs. the extended protocol, respectively. It can be concluded that the inclusion of an additional RNA purification step can increase NoV detection by the ISO/TS15216-2 method in frozen berries containing high amounts of RT-PCR inhibitors. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

Implementation of fuzzy-sliding mode based control of a grid connected photovoltaic system.

PubMed

Menadi, Abdelkrim; Abdeddaim, Sabrina; Ghamri, Ahmed; Betka, Achour

2015-09-01

The present work describes an optimal operation of a small scale photovoltaic system connected to a micro-grid, based on both sliding mode and fuzzy logic control. Real time implementation is done through a dSPACE 1104 single board, controlling a boost chopper on the PV array side and a voltage source inverter (VSI) on the grid side. The sliding mode controller tracks permanently the maximum power of the PV array regardless of atmospheric condition variations, while The fuzzy logic controller (FLC) regulates the DC-link voltage, and ensures via current control of the VSI a quasi-total transit of the extracted PV power to the grid under a unity power factor operation. Simulation results, carried out via Matlab-Simulink package were approved through experiment, showing the effectiveness of the proposed control techniques. Copyright © 2015. Published by Elsevier Ltd.

Textual and shape-based feature extraction and neuro-fuzzy classifier for nuclear track recognition

NASA Astrophysics Data System (ADS)

Khayat, Omid; Afarideh, Hossein

2013-04-01

Track counting algorithms as one of the fundamental principles of nuclear science have been emphasized in the recent years. Accurate measurement of nuclear tracks on solid-state nuclear track detectors is the aim of track counting systems. Commonly track counting systems comprise a hardware system for the task of imaging and software for analysing the track images. In this paper, a track recognition algorithm based on 12 defined textual and shape-based features and a neuro-fuzzy classifier is proposed. Features are defined so as to discern the tracks from the background and small objects. Then, according to the defined features, tracks are detected using a trained neuro-fuzzy system. Features and the classifier are finally validated via 100 Alpha track images and 40 training samples. It is shown that principle textual and shape-based features concomitantly yield a high rate of track detection compared with the single-feature based methods.

Anti-Acne Activity of Italian Medicinal Plants Used for Skin Infection

PubMed Central

Nelson, Kate; Lyles, James T.; Li, Tracy; Saitta, Alessandro; Addie-Noye, Eugenia; Tyler, Paula; Quave, Cassandra L.

2016-01-01

Propionibacterium acnes is implicated in the pathogenesis of acne vulgaris, which impacts >85% of teenagers. Novel therapies are in high demand and an ethnopharmacological approach to discovering new plant sources of anti-acne therapeutics could contribute to filling this void in effective therapies. The aims of our study were two-fold: (1) To determine if species identified in ethnopharmacological field studies as having traditional uses for skin and soft tissue infection (SSTI) exhibit significantly more activity against P. acnes than species with no such reported use; and (2) Chemically characterize active extracts and assess their suitability for future investigation. Extracts of Italian medicinal (for acne and other skin infection) and randomly collected plants and fungi were screened for growth-inhibitory and anti-biofilm activity in P. acnes using broth microdilution methods. Bioactive extracts were chemically characterized by HPLC and examined for cytotoxicity against human keratinocytes (HaCaTs). Following evaluation of 157 extracts from 10 fungi and 58 plants, we identified crude extracts from seven species exhibiting growth inhibitory activity (MICs 64–256 μg mL−1). All active extracts were examined for cytotoxicity against HaCaTs; extracts from one fungal and one plant species were toxic (IC50 256 μg mL−1). HPLC analysis with chemical standards revealed many of these extracts contained chlorogenic acid, p-coumaric acid, ellagic acid, gallic acid, and tannic acid. In conclusion, species used in traditional medicine for the skin exhibited significantly greater (p < 0.05) growth inhibitory and biofilm eradication activity than random species, supporting the validity of an ethnobotanical approach to identifying new therapeutics. The anti-acne activity of three extracts is reported for the first time: Vitis vinifera leaves, Asphodelus microcarpus leaves, and Vicia sativa aerial parts. PMID:27891094

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981-2012

NASA Astrophysics Data System (ADS)

Cao, Xiaoming; Feng, Yiming; Wang, Juanle

2017-06-01

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981-2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981-2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

Application of a Fuzzy Neural Network Model in Predicting Polycyclic Aromatic Hydrocarbon- Mediated Perturbations of the Cyp1b1 Transcriptional Regulatory Network in Mouse Skin

PubMed Central

Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

2013-01-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave one out cross-validation. Predictions were within 1 log2 fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. PMID:23274566

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

PubMed

Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

2018-06-01

Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

Intellectual technologies in the problems of thermal power engineering control: formalization of fuzzy information processing results using the artificial intelligence methodology

NASA Astrophysics Data System (ADS)

Krokhin, G.; Pestunov, A.

2017-11-01

Exploitation conditions of power stations in variable modes and related changes of their technical state actualized problems of creating models for decision-making and state recognition basing on diagnostics using the fuzzy logic for identification their state and managing recovering processes. There is no unified methodological approach for obtaining the relevant information is a case of fuzziness and inhomogeneity of the raw information about the equipment state. The existing methods for extracting knowledge are usually unable to provide the correspondence between of the aggregates model parameters and the actual object state. The switchover of the power engineering from the preventive repair to the one, which is implemented according to the actual technical state, increased the responsibility of those who estimate the volume and the duration of the work. It may lead to inadequacy of the diagnostics and the decision-making models if corresponding methodological preparations do not take fuzziness into account, because the nature of the state information is of this kind. In this paper, we introduce a new model which formalizes the equipment state using not only exact information, but fuzzy as well. This model is more adequate to the actual state, than traditional analogs, and may be used in order to increase the efficiency and the service period of the power installations.

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

Modified Betatron Accelerator Studies.

DTIC Science & Technology

1983-12-01

extraction ports. 29 14 Combined negative mass and beam breakup instability growth rates (solid curves) for t - 13 and Be - 2 kG. Growth of the negative...a stable beam equilibrium 0 for the duration of the acceleration period, typically thousands of revolu- tions; and (c) extraction of the beam, which...bom extraction , it was felt that it would be premature to start work eI I’l 1r a S - - Fip’S .1IU~V~~’ f mdifedbetO coflCPt’ The ajo radius of U tS W

Effect of Psidium cattleianum leaf extract on enamel demineralisation and dental biofilm composition in situ.

PubMed

Brighenti, Fernanda Lourenção; Gaetti-Jardim, Elerson; Danelon, Marcelle; Evangelista, Gustavo Vaz; Delbem, Alberto Carlos Botazzo

2012-08-01

Previous evaluations of Psidium cattleianum leaf extract were not done in conditions similar to the oral environment. The aim of this study was to evaluate the effect of P. cattleianum leaf extract on enamel demineralisation, extracellular polysaccharide formation, and the microbial composition of dental biofilms formed in situ. Ten volunteers took part in this crossover study. They wore palatal appliances containing 4 enamel blocks for 14 days. Each volunteer dripped 20% sucrose 8 times per day on the enamel blocks. Twice a day, deionised water (negative control), extract, or a commercial mouthwash (active control) was dripped after sucrose application. On the 12th and 13th days of the experiment, plaque acidogenicity was measured with a microelectrode, and the pH drop was calculated. On the 14th day, biofilms were harvested and total anaerobic microorganisms (TM), total streptococci (TS), mutans streptococci (MS), and extracellular polysaccharides (EPS) were evaluated. Enamel demineralisation was evaluated by the percentage change of surface microhardness (%ΔSMH) and integrated loss of subsurface hardness (ΔKHN). The researcher was blinded to the treatments during data collection. The extract group showed lower TM, TS, MS, EPS, %ΔSMH, and ΔKHN values than the negative control group. There were no differences between the active and negative control groups regarding MS and EPS levels. There were no differences in pH drop between the extract and active control groups, although they were significantly different from the negative control group. For all other parameters, the extract differed from the active control group. Psidium cattleianum leaf extract exhibits a potential anticariogenic effect. Copyright © 2012 Elsevier Ltd. All rights reserved.

Wood texture classification by fuzzy neural networks

NASA Astrophysics Data System (ADS)

Gonzaga, Adilson; de Franca, Celso A.; Frere, Annie F.

1999-03-01

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are base don texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cracks, by analyzing their texture. By this methodology we divide the plate image in several rectangular windows or local areas and reduce the number of gray levels. From each local area, we compute the histogram of difference sand extract texture features, given them as input to a Local Neuro-Fuzzy Network. Those features are from the histogram of differences instead of the image pixels due to their better performance and illumination independence. Among several features like media, contrast, second moment, entropy, and IDN, the last three ones have showed better results for network training. Each LNN output is taken as input to a Partial Neuro-Fuzzy Network (PNFN) classifying a pencil region on the plate. At last, the outputs from the PNFN are taken as input to a Global Fuzzy Logic doing the plate classification. Each pencil classification within the plate is done taking into account each quality index.

The role of dorsomedial hypotalamus ionotropic glutamate receptors in the hypertensive and tachycardic responses evoked by Tityustoxin intracerebroventricular injection.

PubMed

Silva, F C; Guidine, Patrícia Alves Maia; Machado, Natalia Lima; Xavier, Carlos Henrique; de Menezes, R C; Moraes-Santos, Tasso; Moraes, Márcio Flávio; Chianca, Deoclécio Alves

2015-03-01

The scorpion envenoming syndrome is an important worldwide public health problem due to its high incidence and potential severity of symptoms. Some studies address the high sensitivity of the central nervous system to this toxin action. It is known that cardiorespiratory manifestations involve the activation of the autonomic nervous system. However, the origin of this modulation remains unclear. Considering the important participation of the dorsomedial hypotalamus (DMH) in the cardiovascular responses during emergencial situations, the aim of this work is to investigate the involvement of the DMH on cardiovascular responses induced by intracerebroventricular (icv) injection of Tityustoxin (TsTX, a α-type toxin extracted from the Tityus serrulatus scorpion venom). Urethane-anaesthetized male Wistar rats (n=30) were treated with PBS, muscimol or ionotropic glutamate receptor antagonists, bilaterally in DMH and later, with an icv injection of TsTX, or treated only with PBS in both regions. TsTX evoked a marked increase in mean arterial pressure and heart rate in all control rats. Interestingly, injection of muscimol, a GABAA receptor agonist, did not change the pressor and tachycardic responses evoked by TsTX. Remarkably, the injection ionotropic glutamate receptors antagonists in DMH abolished the pressor and the tachycardic response evoked by TsTX. Our data suggest that the central circuit recruited by TsTX, whose activation results in an array of physiological and behavioral alterations, depend on the activation of DMH ionotropic glutamate receptors. Moreover, our data provide new insights on the central mechanisms involved in the development of symptoms in the severe scorpion envenomation syndrome. Copyright © 2014 Elsevier Inc. All rights reserved.

Model predictive control of non-linear systems over networks with data quantization and packet loss.

PubMed

Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping

2015-11-01

This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

NASA Astrophysics Data System (ADS)

Albrecht, F.; Hölbling, D.; Friedl, B.

2017-09-01

Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

Designing and Implementation of Fuzzy Case-based Reasoning System on Android Platform Using Electronic Discharge Summary of Patients with Chronic Kidney Diseases

PubMed Central

Tahmasebian, Shahram; Langarizadeh, Mostafa; Ghazisaeidi, Marjan; Mahdavi-Mazdeh, Mitra

2016-01-01

Introduction: Case-based reasoning (CBR) systems are one of the effective methods to find the nearest solution to the current problems. These systems are used in various spheres as well as industry, business, and economy. The medical field is not an exception in this regard, and these systems are nowadays used in the various aspects of diagnosis and treatment. Methodology: In this study, the effective parameters were first extracted from the structured discharge summary prepared for patients with chronic kidney diseases based on data mining method. Then, through holding a meeting with experts in nephrology and using data mining methods, the weights of the parameters were extracted. Finally, fuzzy system has been employed in order to compare the similarities of current case and previous cases, and the system was implemented on the Android platform. Discussion: The data on electronic discharge records of patients with chronic kidney diseases were entered into the system. The measure of similarity was assessed using the algorithm provided in the system, and then compared with other known methods in CBR systems. Conclusion: Developing Clinical fuzzy CBR system used in Knowledge management framework for registering specific therapeutic methods, Knowledge sharing environment for experts in a specific domain and Powerful tools at the point of care. PMID:27708490

Improved reservoir characterisation using fuzzy logic platform: an integrated petrophysical, seismic structural and poststack inversion study

NASA Astrophysics Data System (ADS)

Jafri, Muhammad Kamran; Lashin, Aref; Ibrahim, El-Khedr Hassan; Hassanein, Kamal A.; Al Arifi, Nassir; Naeem, Muhammad

2017-06-01

There is a tendency for applying different integrated geophysical approaches for better hydrocarbon reservoir characterisation and interpretation. In this study, petrophysical properties, seismic structural and poststack seismic inversion results are integrated using the fuzzy logic AND operator to characterise the Tensleep Sandstone Formation (TSF) at Powder River Basin (PRB), Wyoming, USA. TSF is deposited in a coastal plain setting during the Pennsylvanian era, and contains cross-bedded sandstone of Aeolian origin as a major lithology with alternative sabkha dolomite/carbonates. Wireline logging datasets from 17 wells are used for the detailed petrophysical evaluation. Three units of the TSF (A-sandstone, B-dolomite and B-sandstone) are targeted and their major rock properties estimated (i.e. shale/clay volume, Vsh; porosity, φEff permeability, K; fluid saturations, Sw and SH; and bulk volume water, BVW). The B-sandstone zone, with its petrophysical properties of 5-20% effective porosity, 0.10-250 mD permeability and hydrocarbon potential up to 72%, is considered the best reservoir zone among the three studied units. Distributions of the most important petrophysical parameters of the B-sandstone reservoir (Vsh, φEff, K, Sw) are generated as GIS thematic layers. The two-dimensional (2D) and three-dimensional (3D) seismic structural interpretations revealed that the hydrocarbons are entrapped in an anticlinal structure bounded with fault closures at the west of the study area. Poststack acoustic impedance (PSAI) inversion is performed on 3D seismic data to extract the inverted acoustic impedance (AI) cube. Two attribute slices (inverted AI and seismic amplitude) were extracted at the top of the B-sandstone unit as GIS thematic layers. The reservoir properties and inverted seismic attributes were then integrated using fuzzy AND operator. Finally, a fuzzy reservoir quality map was produced, and a prospective reservoir area with best reservoir characteristics is proposed for future exploration. The current study showed that integration of petrophysical, seismic structural and poststack inversion under a fuzzy logic platform can be used as an effective tool for interpreting multiple reservoir zones.

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

NASA Astrophysics Data System (ADS)

Mehmanpazir, Farhad; Asadi, Shahrokh

2017-03-01

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

NASA Astrophysics Data System (ADS)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Automatic sleep staging using multi-dimensional feature extraction and multi-kernel fuzzy support vector machine.

PubMed

Zhang, Yanjun; Zhang, Xiangmin; Liu, Wenhui; Luo, Yuxi; Yu, Enjia; Zou, Keju; Liu, Xiaoliang

2014-01-01

This paper employed the clinical Polysomnographic (PSG) data, mainly including all-night Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) signals of subjects, and adopted the American Academy of Sleep Medicine (AASM) clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM) were learned and the multi-kernel FSVM (MK-FSVM) was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

A genetic fuzzy system for unstable angina risk assessment.

PubMed

Dong, Wei; Huang, Zhengxing; Ji, Lei; Duan, Huilong

2014-02-18

Unstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized. In order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information's vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment. The proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%). By comparing the results that are obtained through the proposed system with those resulting from the physician's decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.

Phytosterols from Dunaliella tertiolecta Reduce Cell Proliferation in Sheep Fed Flaxseed during Post Partum

PubMed Central

Ciliberti, Maria Giovanna; Francavilla, Matteo; Intini, Simona; Albenzio, Marzia; Marino, Rosaria; Santillo, Antonella; Caroprese, Mariangela

2017-01-01

The post partum period is characterized by immunosuppression and increased disease susceptibility. Both phytosterols from microalga Dunaniella tertiolecta and dietary supplementation with n-3 polyunsaturated fatty acids (PUFA) influence cell proliferation and cytokine release during inflammation. The objective of this paper was the evaluation of the effects of physterols, extracted and purified from D. tertiolecta, on the in vitro immune responses of ewes supplemented with flaxseed during post partum. Twenty Comisana parturient ewes were divided in two balanced groups, and supplemented with flaxseed (FS, 250 g/day) or fed with a conventional diet (CON). Blood samples (15 mL) were collected for five weeks, starting from lambing, in order to isolate peripheral blood mononuclear cells (PBMC). Stimulated PBMC were treated with a total sterols fraction from D. tertiolecta (TS), a mix of ergosterol and 7-dehydroporiferasterol (purified extract, PE), and a mix of acetylated ergosterol and 7-dehydroporiferasterol (acetylated purified extract, AcPE), extracted and purified from D. tertiolecta at two concentrations (0.4 and 0.8 mg/mL). Results of the experiment demonstrated that n-3 PUFA from flaxseed induced an anti-inflammatory cytokine profile, with an increase of both IL-10, IL-6 and a decrease of IL-1β. TS, PE, and AcPE purified from D. tertiolecta showed an anti-proliferative effect on sheep PBMC regardless their chemical composition and concentration. PMID:28684702

Automated cloud classification with a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Tovinkere, Vasanth; Baum, Bryan A.

1993-01-01

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

How multiple factors control evapotranspiration in North America evergreen needleleaf forests.

PubMed

Chen, Yueming; Xue, Yueju; Hu, Yueming

2018-05-01

Identifying the factors dominating ecosystem water flux is a critical step for predicting evapotranspiration (ET). Here, the fuzzy rough set with binary shuffled frog leaping (BSFL-FRSA) was used to identify both individual factors and multi-factor combinations that dominate the half-hourly ET variation at evergreen needleleaf forests (ENFs) sites across three different climatic zones in the North America. Among 21factors, air temperature (TA), atmospheric CO 2 concentration (CCO 2 ), soil temperature (TS), soil water content (SWC) and net radiation (NETRAD) were evaluated as dominant single factors, contributed to the ET variation averaged for all ENF sites by 48%, 36%, 32%, 18% and 13%, respectively. While the importance order would vary with climatic zones, and TA was assessed as the most influential factor at a single climatic zone level, counting a contribution rate of 54.7%, 49.9%, and 38.6% in the subarctic, warm summer continental, and Mediterranean climatic zones, respectively. In view of impacts of each multi-factors combination on ET, both TA and CCO 2 made a contribution of 71% across three climate zones; the combination of TA, CCO 2 and NETRAD was evaluated the most dominant at Mediterranean and subarctic ENF sites, and the combination of TA, CCO 2 and TS at warm summer continental sites. Our results suggest that temperature was most critical for ET variation at the warm summer continental ENF. Copyright © 2017 Elsevier B.V. All rights reserved.

Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

NASA Astrophysics Data System (ADS)

Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

2017-02-01

In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

Extraction of Coastlines with Fuzzy Approach Using SENTINEL-1 SAR Image

NASA Astrophysics Data System (ADS)

Demir, N.; Kaynarca, M.; Oy, S.

2016-06-01

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

Decontamination of electronic waste-polluted soil by ultrasound-assisted soil washing.

PubMed

Chen, Fu; Yang, Baodan; Ma, Jing; Qu, Junfeng; Liu, Gangjun

2016-10-01

Laboratorial scale experiments were performed to evaluate the efficacy of a washing process using the combination of methyl-β-cyclodextrin (MCD) and tea saponin (TS) for simultaneous desorption of hydrophobic organic contaminants (HOCs) and heavy metals from an electronic waste (e-waste) site. Ultrasonically aided mixing of the field contaminated soil with a combination of MCD and TS solutions simultaneously mobilizes most of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and the analyte metal (Pb, Cu, and Ni) burdens. It is found that 15 g/L MCD and 10 g/L TS is an efficient reagent combination reconciling extraction performance and reagent costs. Under these conditions, the removal efficiencies of HOCs and heavy metals are 93.5 and 91.2 %, respectively, after 2 cycles of 60-min ultrasound-assisted washing cycles. By contrast, 86.3 % of HOCs and 88.4 % of metals are removed from the soil in the absence of ultrasound after 3 cycles of 120-min washing. The ultrasound-assisted soil washing could generate high removal efficiency and decrease the operating time significantly. Finally, the feasibility of regenerating and reusing the spent washing solution in extracting pollutants from the soil is also demonstrated. By application of this integrated technology, it is possible to recycle the washing solution for a purpose to reduce the consumption of surfactant solutions. Collectively, it has provided an effective and economic treatment of e-waste-polluted soil.

Comparison of key aroma compounds in five different types of Japanese soy sauces by aroma extract dilution analysis (AEDA).

PubMed

Kaneko, Shu; Kumazawa, Kenji; Nishimura, Osamu

2012-04-18

An investigation by the aroma extract dilution analysis (AEDA) technique of the aroma concentrate from five different types of Japanese soy sauces, categorized according to Japan Agricultural Standards as Koikuchi Shoyu (KS), Usukuchi Shoyu (US), Tamari Shoyu (TS), Sai-Shikomi Shoyu (SSS), and Shiro Shoyu (SS), revealed 25 key aroma compounds. Among them, 3-ethyl-1,2-cyclopentanedione and 2'-aminoacetophenone were identified in the soy sauces for the first time. Whereas 3-(methylthio)propanal (methional) and 3-hydroxy-4,5-dimethyl-2(5H)-furanone (sotolon) were detected in all of the soy sauce aroma concentrates as having high flavor dilution (FD) factors, 4-ethyl-2-methoxyphenol was detected as having a high FD factor in only four of the soy sauces (KS, US, TS, and SSS). Furthermore, 5(or 2)-ethyl-4-hydroxy-2(or 5)-methyl-3(2H)-furanone (4-HEMF) and 4-hydroxy-2,5-dimethyl-3(2H)-furanone (4-HDMF), which were thought to be the key odorants in KS, were detected in KS, US, TS, and SSS, but the FD factors widely varied among them. The sensory evaluations demonstrated that the aroma descriptions of a cooked potato-like note and a caramel-like/seasoning-like note were evaluated as high scores with no significant differences among the five soy sauces. On the other hand, a burnt/spicy note was evaluated as having high scores in KS, TS, and SSS, but it was evaluated as having a low score in SS. The comparative AEDA experiments and the auxiliary sensory experiments demonstrated that the five different types of Japanese soy sauces varied in their key aroma compounds and aroma characteristics, and the key aroma compounds in KS might not always be highly contributing in the other types of Japanese soy sauces.

A software package for interactive motor unit potential classification using fuzzy k-NN classifier.

PubMed

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2008-01-01

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

SAR image segmentation using skeleton-based fuzzy clustering

NASA Astrophysics Data System (ADS)

Cao, Yun Yi; Chen, Yan Qiu

2003-06-01

SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.

Prediction on sunspot activity based on fuzzy information granulation and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Lingling; Yan, Haisheng; Yang, Zhigang

2018-04-01

In order to analyze the range of sunspots, a combined prediction method of forecasting the fluctuation range of sunspots based on fuzzy information granulation (FIG) and support vector machine (SVM) was put forward. Firstly, employing the FIG to granulate sample data and extract va)alid information of each window, namely the minimum value, the general average value and the maximum value of each window. Secondly, forecasting model is built respectively with SVM and then cross method is used to optimize these parameters. Finally, the fluctuation range of sunspots is forecasted with the optimized SVM model. Case study demonstrates that the model have high accuracy and can effectively predict the fluctuation of sunspots.

A health insurance company-initiated practice support intervention for optimizing acid-suppressing drug prescriptions in primary care.

PubMed

Smeets, Hugo M; Hoes, Arno W; Zuithoff, Nicolaas P A; van Dijk, Paul C M; van der Lee, Arnold P M; de Wit, Niek J

2011-08-01

A health insurance-initiated programme to improve cost-effectiveness of acid-suppressing drugs (ASDs). To evaluate the effect of two different interventions of general practitioner support in reducing drug prescription. A sequential cluster randomized controlled trial with 90 participating general practitioners in a telephone support (TS) group or practice visit (PV) group. TS group received support in phase-1 (first 6 months), but served as control group in phase-2 (6-12 months period). PV group received no intervention in phase-1, serving as the control group for the TS group, but received support in phase-2. Prescription data were extracted from Agis Health Insurance Database. Outcomes were the proportion of responders to drug reduction and the number of defined daily dose (DDD). Differences in users and DDD were analysed using multilevel regression analysis. At baseline, 3424 patients used ASD chronically (211 DDDs, on average). The difference between TS and control groups among responders was 3.2% [95% confidence interval (CI): 0.8; 5.6] and relative risk was 1.26 (95% CI: 1.06; 1.51). The difference between PV and control groups was not relevant (0.4%, 95% CI: -1.99; 2.79 and relative risk: 1.01, 95% CI: 0.82; 1.20). The difference in DDD per patient was -3.0 (95% CI: -8.9; 2.9) and -5.82 (95% CI: -12.4; 0.73), respectively. This health insurance company-initiated intervention had a moderate effect on ASD prescription. In contrast to TS, PVs did not seem to reduce ASD prescription rates.

Fuzzy clustering evaluation of the discrimination power of UV-Vis and (±) ESI-MS detection system in individual or coupled RPLC for characterization of Ginkgo Biloba standardized extracts.

PubMed

Medvedovici, Andrei; Albu, Florin; Naşcu-Briciu, Rodica Domnica; Sârbu, Costel

2014-02-01

Discrimination power evaluation of UV-Vis and (±) electrospray ionization/mass spectrometric techniques, (ESI-MS) individually considered or coupled as detectors to reversed phase liquid chromatography (RPLC) in the characterization of Ginkgo Biloba standardized extracts, is used in herbal medicines and/or dietary supplements with the help of Fuzzy hierarchical clustering (FHC). Seventeen batches of Ginkgo Biloba commercially available standardized extracts from seven manufacturers were measured during experiments. All extracts were within the criteria of the official monograph dedicated to dried refined and quantified Ginkgo extracts, in the European Pharmacopoeia. UV-Vis and (±) ESI-MS spectra of the bulk standardized extracts in methanol were acquired. Additionally, an RPLC separation based on a simple gradient elution profile was applied to the standardized extracts. Detection was made through monitoring UV absorption at 220 nm wavelength or the total ion current (TIC) produced through (±) ESI-MS analysis. FHC was applied to raw, centered and scaled data sets, for evaluating the discrimination power of the method with respect to the origins of the extracts and to the batch to batch variability. The discrimination power increases with the increase of the intrinsic selectivity of the spectral technique being used: UV-Vis

Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.

PubMed

Trnka, Hjalte; Wu, Jian X; Van De Weert, Marco; Grohganz, Holger; Rantanen, Jukka

2013-12-01

Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

The Space-Time Scales of Variability in Oceanic Thermal Structure Off the Central California Coast.

DTIC Science & Technology

1983-12-01

SST and sea- surface salinity (SSS) boundaries extracted from the shipboard (2m) thermalsalinograph (T/S) records (Figs. 23, 24, and 25). For these... extracted for comparison. At 175m the density gradient is sufficient to support vigorous internal wave activity in this region. As a result, the predominant... VB2 (VB squared) profiles were calculated from density profiles taken from each phase at a common location (Fig. 149). The location is approximately

Overcoming phytoremediation limitations. A case study of Hg contaminated soil

NASA Astrophysics Data System (ADS)

Barbafieri, Meri

2013-04-01

Phytoremediation is a broad term that comprises several technologies to clean up water and soil. Despite the numerous articles appearing in scientific journals, very few field applications of phytoextraction have been successfully realized. The research here reported on Phytoextraction, the use the plant to "extract" metals from contaminated soil, is focused on implementations to overcome two main drawbacks: the survival of plants in unfavorable environmental conditions (contaminant toxicity, low fertility, etc.) and the often lengthy time it takes to reduce contaminants to the requested level. Moreover, to overcome the imbalance between the technology's potential and its drawbacks, there is growing interest in the use of plants to reduce only the fraction that is the most hazardous to the environment and human health, that is to target the bioavailable fractions of metals in soil. Bioavailable Contaminant Stripping (BCS) would be a remediation approach focused to remove the bioavailable metal fractions. BCS have been used in a mercury contaminated soil from Italian industrial site. Bioavailable fractions were determined by sequential extraction with H2O and NH4Cl.Combined treatments of plant hormone and thioligand to strength Hg uptake by crop plants (Brassica juncea and Helianthus annuus) were tested. Plant biomass, evapotranspiration, Hg uptake and distribution following treatments were compared. Results indicate the plant hormone, cytokinine (CK) foliar treatment, increased evapotranspiration rate in both tested plants. The Hg uptake and translocation in both tested plants increased with simultaneous addition of CK and TS treatments. B. juncea was the most effective in Hg uptake. Application of CK to plants grown in TS-treated soil lead to an increase in Hg concentration of 232% in shoots and 39% in roots with respect to control. While H. annuus gave a better response in plant biomass production, the application of CK to plants grown in TS-treated soil lead to an increase in Hg concentration of 248% in shoots and 185% in roots with respect to control plants. The BCS efficiency were evaluated analyzing the labile-Hg residue in the soil after the plant growing. Plants grown with CK and TS in one growing cycle significantly affected labile-Hg pools in soil characterized by sequential extraction, but did not significantly reduce the total metals in the soil. Moreover, if properly optimized, the use of a coupled phytohormone/thioligand system may be a viable strategy to strength Hg uptake by crop plants.

Weighting Criteria and Prioritizing of Heat stress indices in surface mining using a Delphi Technique and Fuzzy AHP-TOPSIS Method.

PubMed

Asghari, Mehdi; Nassiri, Parvin; Monazzam, Mohammad Reza; Golbabaei, Farideh; Arabalibeik, Hossein; Shamsipour, Aliakbar; Allahverdy, Armin

2017-01-01

Heat stress as a physical harmful agent can increase the risk of health and safety problems in different workplaces such as mining. Although there are different indices to assess the heat stress imposed on workers, choosing the best index for a specific workplace is so important. Since various criteria affect an index applicability, extracting the most effective ones and determining their weights help to prioritize the existing indices and select the optimal index. In order to achieve this aim, present study compared some heat stress indices using effective methods. The viewpoints of occupational health experts and the qualitative Delphi methods were used to extract the most important criteria. Then, the weights of 11 selected criteria were determined by Fuzzy Analytic Hierarchy Process. Finally, fuzzy TOPSIS technique was applied for choosing the most suitable heat stress index. According to result, simplicity, reliability, being low cost, and comprehensiveness were the most determinative criteria for a heat stress index. Based on these criteria and their weights, the existing indices were prioritized. Eventually, wet bulb glob temperature appropriated the first priority and it was proposed as an applicable index for evaluating the heat stress at outdoor hot environments such as surface mines. The use of these strong methods allows introducing the most simple, precise, and applicable tool for evaluation the heat stress in hot environments. It seems that WBGT acts as an appropriate index for assessing the heat stress in mining activities at outdoors.

Rapid determination of five antibiotic residues in swine wastewater by online solid-phase extraction-high performance liquid chromatography-tandem mass spectrometry.

PubMed

Tagiri-Endo, Misako; Suzuki, Shigeru; Nakamura, Tomoyuki; Hatakeyama, Takashi; Kawamukai, Kazuo

2009-02-01

A simple and quick online solid-phase extraction (SPE) coupled to liquid chromatography (LC)/tandem mass spectrometry (MS/MS) for the determination of the five antibiotics (florfenicol, FF; lincomycin, LCM; oxytetracyclin, OTC; tylosin, TS; valnemulin, VLM) in swine wastewater has been developed. After filtration, aliquots (100 microl) of wastewater samples were directly injected to a column-switching LC system. Some matrix interference was removed by washing up SPE column with 0.2% formic acid solution and acetonitrile. Antibiotics eluted from SPE column were separated on analytical column by converting switching valve and were detected by MS/MS. Calibration curves using the method of standard addition had very good correlation coefficients (r > 0.99) in the range of 0.1 to 2 ng/ml. The intra-day precision of the method was less than 12% and the inter-day precision was between 6 to 17%. The detection limits were 0.01-0.1 ng/ml. When this method was applied to wastewater samples in swine facilities, four compounds (LCM, OTC, TS, and VLM) were detected.

Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

PubMed

Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

2017-10-01

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

NASA Astrophysics Data System (ADS)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

Using a plant hormone and a thioligand to improve phytoremediation of Hg-contaminated soil from a petrochemical plant.

PubMed

Cassina, L; Tassi, E; Pedron, F; Petruzzelli, G; Ambrosini, P; Barbafieri, M

2012-09-15

Mercury-contaminated soils from a petrochemical plant in southern Italy were investigated to assess the phytoextraction efficiency of crop plants treated with the phytohormone, cytokinine (CK foliar treatment), and with the thioligand, ammonium thiosulfate (TS, soil application). Plant biomass, evapotranspiration, Hg uptake and distribution in plant tissues following treatment were compared. Results indicate the effectiveness of CK in increasing plant biomass and the evapotranspiration rate while TS treatment promoted soil Hg solubility and availability. The simultaneous addition of CK and TS treatments increased Hg uptake and translocation in both tested plants with up to 248 and 232% in Brassica juncea (Indian mustard) and Helianthus annuus (sunflower) respectively. B. juncea was more effective in Hg uptake, whereas H. annuus gave better response regarding plant biomass production. The effectiveness of the treatments was confirmed by the calculation of Hg phytoextraction and evaluation of labile-Hg residue in the soil after plant growth. In one growing cycle the plants subject to simultaneous CK and TS treatment significantly reduced labile-Hg pools that were characterized by the soil sequential extraction, but did not significantly affect the pseudototal metal content in the soil. Results support the use of plant growth regulators in the assisted phytoextraction process for Hg-contaminated soils. Copyright © 2012 Elsevier B.V. All rights reserved.

Effect of Tea Saponin-Treated Host Plants on Activities of Antioxidant Enzymes in Larvae of the Diamondback Moth Plutella xylostella (Lepidoptera: Plutellidae).

PubMed

Lin, Shuo; Chen, Yixin; Bai, Yan; Cai, Hongjiao; Wei, Hui; Tian, Houjun; Zhao, Jianwei; Chen, Yong; Yang, Guang; Gu, Xiaojun; Murugan, Kadarkarai

2018-06-06

Tea saponin (TS) is extracted from the seeds of the tea plant and is generally regarded as a safe compound that has insecticidal properties and can act synergistically with other compounds. In this study, the activities of antioxidant enzymes superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and the levels of malondialdehyde (MDA) were compared in midgut tissues of third instar larvae of the diamondback moth (DBM), Plutella xylostella L. (Lepidoptera: Plutellidae). The larvae were fed on three different host plants, cabbage (Brassica oleracea L. var. capitata [Capparales: Brassicaceae]), radish (Raphanus sativus L. var. radiculus Persi [Capparales: Brassicaceae]), or rape (Brassica campestris L. [Capparales: Brassicaceae]), that had been treated with TS. Higher SOD, POD, and CAT activities were found in DBM larvae fed on cabbage after LC20 (concentration that induced 20% larval mortality) or LC50 (concentration that induced 50% larval mortality) treatment than on the control. On rape, TS treatments led to lower SOD and CAT activities than in the control and to higher POD activities after 24 h. MDA content increased in larvae fed on rape but decreased in larvae fed on radish after 12 h. Our results indicated that DBM larvae are more susceptible to TS on rape than on cabbage and radish, suggesting that this treatment may be an economic and effective means of controlling DBM on rape.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

Determination of the ultrasound power effects on flavonoid compounds from Psidium guajava L. using ANFIS

NASA Astrophysics Data System (ADS)

Ratu Ayu, Humairoh; Suryono, Suryono; Endro Suseno, Jatmiko; Kurniawati, Ratna

2018-05-01

The Adaptive Neural Fuzzy Inference System (ANFIS) model was used to predict and optimize the content of flavonoid compounds in guava leaves (Psidium Guajava L.). The extraction process was carried out by using ultrasound assisted extraction (UAE) with the variable parameters: temperature ranging from 25°C to 35°C, ultrasonic frequency (30 - 40 kHz) and extraction time (20 - 40 minutes). ANFIS learning procedure began by providing the input variable data set (temperature, frequency and time) and the output of the flavonoid compounds from the experiments that had been done. Subtractive clustering methods was used in the manufacture of FIS (fuzzy inference system) structures by varying the range of influence parameters to generate the ANFIS system. The ANFIS trainingsconducted wereaimed at minimum error value. The results showed that the best ANFIS models used a subtractive clustering method, in which the ranges of influence 0.1 were 0.70 x 10-4 for training RMSE, 8.11 for testing RMSE, 2.7 % MAPE, and 7.72 MAE. The optimum condition was obtained at a temperature of 35°C and frequency of 40 kHz, for 30 minutes. This result proves that the ANFIS model can be used to predict the content of flavonoid compounds in guava leaves.

An object recognition method based on fuzzy theory and BP networks

NASA Astrophysics Data System (ADS)

Wu, Chuan; Zhu, Ming; Yang, Dong

2006-01-01

It is difficult to choose eigenvectors when neural network recognizes object. It is possible that the different object eigenvectors is similar or the same object eigenvectors is different under scaling, shifting, rotation if eigenvectors can not be chosen appropriately. In order to solve this problem, the image is edged, the membership function is reconstructed and a new threshold segmentation method based on fuzzy theory is proposed to get the binary image. Moment invariant of binary image is extracted and normalized. Some time moment invariant is too small to calculate effectively so logarithm of moment invariant is taken as input eigenvectors of BP network. The experimental results demonstrate that the proposed approach could recognize the object effectively, correctly and quickly.

Space debris tracking based on fuzzy running Gaussian average adaptive particle filter track-before-detect algorithm

NASA Astrophysics Data System (ADS)

Torteeka, Peerapong; Gao, Peng-Qi; Shen, Ming; Guo, Xiao-Zhang; Yang, Da-Tao; Yu, Huan-Huan; Zhou, Wei-Ping; Zhao, You

2017-02-01

Although tracking with a passive optical telescope is a powerful technique for space debris observation, it is limited by its sensitivity to dynamic background noise. Traditionally, in the field of astronomy, static background subtraction based on a median image technique has been used to extract moving space objects prior to the tracking operation, as this is computationally efficient. The main disadvantage of this technique is that it is not robust to variable illumination conditions. In this article, we propose an approach for tracking small and dim space debris in the context of a dynamic background via one of the optical telescopes that is part of the space surveillance network project, named the Asia-Pacific ground-based Optical Space Observation System or APOSOS. The approach combines a fuzzy running Gaussian average for robust moving-object extraction with dim-target tracking using a particle-filter-based track-before-detect method. The performance of the proposed algorithm is experimentally evaluated, and the results show that the scheme achieves a satisfactory level of accuracy for space debris tracking.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Placental telomere shortening in stillbirth: a sign of premature senescence?

PubMed

Ferrari, Francesca; Facchinetti, Fabio; Saade, George; Menon, Ramkumar

2016-01-01

The objective of this study is to investigate placental telomere shortening in unexplained stillbirths (SBs) as an indication of premature senescence. Placentas were collected from 42 unexplained SB (>22 weeks), 43 term and 15 preterm live births, at the Policlinico Hospital of Modena (Italy). DNA extracted from placentae was studied for telomere length by real time PCR. Standard curves were generated for telomere lengths from single copy gene amplifications using a reference DNA. The telomere length for each sample was derived based on the ratio of telomere length between the sample and single copy gene standard (T/S ratio). The mean ratio of placental telomere in term live births was 5.181 ± 3.841. A twofold decrease in telomere length was seen in SBs (over all 2.455 ± 1.239; p < 0.001). For early SBs (above 34 weeks), the T/S was 2.8884 ± 1.224 and for late SBs, the T/S was 2.207 ± 1.201, both lower than term live births (both p < 0.01). T/S remained lower both in small for gestational age-SB (2.639 ± 1.619) and appropriate for gestational age-SB (2.653 ± 1.335) with no difference between these subgroups (p = ns). T/S was lower in SB compared with spontaneous preterm births (PTBs) (6.382 ± 5.525; p < 0.01), whereas SBs telomere length were similar to those of preterm premature rupture of membranes (pPROM) (3.296 ± 3.599; p = ns). Substantial reduction in telomere length in SBs is indicative of placental senescence. These data provide mechanistic insights that premature aging may lead to placental dysfunction as an initiator of fetal demise in unexplained SBs.

Differential impact of ionic and coordinate covalent chromium (Cr)-DNA binding on DNA replication.

PubMed

Fornsaglio, Jamie L; O'Brien, Travis J; Patierno, Steven R

2005-11-01

The reactive species produced by the reduction of Cr(VI), particularly Cr(III), can form both ionic and coordinate covalent complexes with DNA. These Cr(III)-DNA interactions consist of Cr-DNA monoadducts, Cr-DNA ternary adducts, and Cr-DNA interstrand cross-links (Cr-ICLs), the latter of which are DNA polymerase arresting lesions (PALs). We sought to determine the impact of Cr-DNA interactions on the formation of replication blocking lesions in S. cerevisiae using a PCR-based method. We found that target sequence (TS) amplification using DNA isolated from Cr(VI)-treated yeast actually increased as a function of Cr(VI) concentration. Moreover, the enhanced TS amplification was reproduced in vitro using Cr(III)-treated DNA. In contrast, PCR amplification of TS from DNA isolated from yeast exposed to equitoxic doses of the inorganic DNA cross-linking agent cisplatin (CDDP), was decreased in a concentration-dependent manner. This paradox suggested that a specific Cr-DNA interaction, such as an ionic Cr-DNA complex, was responsible for the enhanced TS amplification, thereby masking the replication-blocking effect of certain ternary Cr-DNA adducts (i.e. interstrand cross-links). To test this possibility, we removed ionically associated Cr from the DNA using salt extraction prior to PCR analysis. This procedure obviated the increased amplification and revealed a dose-dependent decrease in TS amplification and an increase in Cr-PALs. These data from DNA analyzed ex vivo after treatment of intact cells indicate that ionic interactions of Cr with DNA result in increased DNA amplification whereas coordinate-covalent Cr-DNA complexes lead to formation of Cr-PALs. Thus, these results suggest that treatment of living cells with Cr(VI) leads to two modes of Cr-binding, which may have conflicting effects on DNA replication.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

PubMed Central

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

Investigation on the effect of geometrical and geotechnical parameters on elongated offshore piles using fuzzy inference systems

NASA Astrophysics Data System (ADS)

Aminfar, Ali; Mojtahedi, Alireza; Ahmadi, Hamid; Aminfar, Mohammad Hossain

2017-06-01

Among numerous offshore structures used in oil extraction, jacket platforms are still the most favorable ones in shallow waters. In such structures, log piles are used to pin the substructure of the platform to the seabed. The pile's geometrical and geotechnical properties are considered as the main parameters in designing these structures. In this study, ANSYS was used as the FE modeling software to study the geometrical and geotechnical properties of the offshore piles and their effects on supporting jacket platforms. For this purpose, the FE analysis has been done to provide the preliminary data for the fuzzy-logic post-process. The resulting data were implemented to create Fuzzy Inference System (FIS) classifications. The resultant data of the sensitivity analysis suggested that the orientation degree is the main factor in the pile's geometrical behavior because piles which had the optimal operational degree of about 5° are more sustained. Finally, the results showed that the related fuzzified data supported the FE model and provided an insight for extended offshore pile designs.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Image Quality Assessment of High-Resolution Satellite Images with Mtf-Based Fuzzy Comprehensive Evaluation Method

NASA Astrophysics Data System (ADS)

Wu, Z.; Luo, Z.; Zhang, Y.; Guo, F.; He, L.

2018-04-01

A Modulation Transfer Function (MTF)-based fuzzy comprehensive evaluation method was proposed in this paper for the purpose of evaluating high-resolution satellite image quality. To establish the factor set, two MTF features and seven radiant features were extracted from the knife-edge region of image patch, which included Nyquist, MTF0.5, entropy, peak signal to noise ratio (PSNR), average difference, edge intensity, average gradient, contrast and ground spatial distance (GSD). After analyzing the statistical distribution of above features, a fuzzy evaluation threshold table and fuzzy evaluation membership functions was established. The experiments for comprehensive quality assessment of different natural and artificial objects was done with GF2 image patches. The results showed that the calibration field image has the highest quality scores. The water image has closest image quality to the calibration field, quality of building image is a little poor than water image, but much higher than farmland image. In order to test the influence of different features on quality evaluation, the experiment with different weights were tested on GF2 and SPOT7 images. The results showed that different weights correspond different evaluating effectiveness. In the case of setting up the weights of edge features and GSD, the image quality of GF2 is better than SPOT7. However, when setting MTF and PSNR as main factor, the image quality of SPOT7 is better than GF2.

A Risk Assessment System with Automatic Extraction of Event Types

NASA Astrophysics Data System (ADS)

Capet, Philippe; Delavallade, Thomas; Nakamura, Takuya; Sandor, Agnes; Tarsitano, Cedric; Voyatzi, Stavroula

In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon-Grammar). Its role is to automatically feed the leaves with input data.

[Mechanism study on difference of biotransformation between Mycobacterium fortuitum MF2 and MF96].

PubMed

Ling, Liang-Fei; Ge, Mei; Fu, Lei; Huang, Wei-Yi; Chen, Dai-Jie

2005-08-01

Biotransformation difference between parent strain (MF2) and mutant strain (MF96) of Mycobacterium fortuitum was observed. Biotransformation with resting cells showed that the major products of biotransformation by both parent and mutant strains are delta4-androstenedione(4AD) and testosterone(TS). Experiments with cell-free extract system showed that the proportion of 4AD/TS obtained from parent and mutant strains was almost same when enough NAD+ and NADH were supplied in this system. It was suggested that the difference of the ratio of products transformed by both strains in resting cell system may result from their different ratio of NAD+/NADH. This speculation was verified to be true by determination of the amount of NAD+ and NADH presented in both strains.

An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms.

PubMed

Andreotti, Fernando; Behar, Joachim; Zaunseder, Sebastian; Oster, Julien; Clifford, Gari D

2016-05-01

Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed. This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e.g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio). The overall median FQRS detection accuracies (i.e. considering all non-stationary events) for the best performing methods in each group were 99.9% for BSS, 97.9% for AM and 96.0% for TS. Both FQRS detections and morphological parameters were shown to heavily depend on the extraction techniques and signal-to-noise ratio. Particularly, it is shown that their evaluation in the source domain, obtained after using a BSS technique, should be avoided. Data, extraction algorithms and evaluation routines were released as part of the fecgsyn toolbox on Physionet under an GNU GPL open-source license. This contribution provides a standard framework for benchmarking and regulatory testing of NI-FECG extraction algorithms.

A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

PubMed

Marucci-Wellman, H; Lehto, M; Corns, H

2011-12-01

Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

Automated labeling of bibliographic data extracted from biomedical online journals

NASA Astrophysics Data System (ADS)

Kim, Jongwoo; Le, Daniel X.; Thoma, George R.

2003-01-01

A prototype system has been designed to automate the extraction of bibliographic data (e.g., article title, authors, abstract, affiliation and others) from online biomedical journals to populate the National Library of Medicine"s MEDLINE database. This paper describes a key module in this system: the labeling module that employs statistics and fuzzy rule-based algorithms to identify segmented zones in an article"s HTML pages as specific bibliographic data. Results from experiments conducted with 1,149 medical articles from forty-seven journal issues are presented.

The effect of seasonal variation on the performances of grid connected photovoltaic system in southern of Algeria

NASA Astrophysics Data System (ADS)

Zaghba, L.; Khennane, M.; Terki, N.; Borni, A.; Bouchakour, A.; Fezzani, A.; Mahamed, I. Hadj; Oudjana, S. H.

2017-02-01

This paper presents modeling, simulation, and analysis evaluation of the grid-connected PV generation system performance under MATLAB/Simulink. The objective is to study the effect of seasonal variation on the performances of grid connected photovoltaic system in southern of Algeria. This system works with a power converter. This converter allows the connection to the network and extracts maximum power from photovoltaic panels with the MPPT algorithm based on robust neuro-fuzzy sliding approach. The photovoltaic energy produced by the PV generator will be completely injected on the network. Simulation results show that the system controlled by the neuro-fuzzy sliding adapts to changing external disturbances and show their effectiveness not only for continued maximum power point but also for response time and stability.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurt Derr; Milos Manic

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. In addition, pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A person/object in proximity to certain areas for specific durations of time may pose a risk hazard either to themselves, others, or the environment. This paper presents a novel fuzzy based spatio-temporal risk calculationmore » DSTiPE method that an object with wireless communications presents to the environment. The presented Matlab based application for fuzzy spatio-temporal risk cluster extraction is verified on a diagonal vehicle movement example.« less

SOM guided fuzzy logic prospectivity model for gold in the Häme Belt, southwestern Finland

NASA Astrophysics Data System (ADS)

Leväniemi, Hanna; Hulkki, Helena; Tiainen, Markku

2017-04-01

This study investigated gold prospectivity in the Paleoproterozoic Häme Belt, located in southwestern Finland. The Häme Belt comprises calc-alkaline and tholeitic volcanic rocks, migmatites, granitoids, and mafic to ultramafic intrusions. Mineral exploration in the region has resulted in the discovery of several gold occurrences during recent decades; however, no prospectivity modeling for gold has yet been conducted. This study integrated till geochemical and geophysical data to examine and extract data characteristics critical for gold occurrences. Modeling was guided by self-organizing map (SOM) analysis to define essential data associations and to aid in model input data selection and generation. The final fuzzy logic prospectivity model map yielded high predictability values for most known Au or Cu-Au occurrences, but also highlighted new targets for exploration.

Production of Aluminum Stabilized Superconducting Cable for the Mu2e Transport Solenoid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lombardo, Vito; Ambrosio, Giorgio; Evbota, Daniel

Here, the Fermilab Mu2e experiment, currently under construction at Fermilab, has the goal of measuring the rare process of direct muon to electron conversion in the field of a nucleus. The experiment features three large superconducting solenoids: the production solenoid (PS), the transport solenoid (TS), and the detector solenoid (DS). The TS is an “S-shaped” solenoid that sits in between the PS and the DS producing a magnetic field ranging between 2.5 and 2.0 T. This paper describes the various steps that led to the successful procurement of over 740 km of superconducting wire and 44 km of Al-stabilized Rutherfordmore » cable needed to build the 52 coils that constitute the Mu2e TS cold mass. The main cable properties and results of electrical and mechanical test campaigns are summarized and discussed. Critical current measurements of the Al-stabilized cables are presented and compared to expected critical current values as measured on extracted strands from the final cables after chemical etching of the aluminum stabilizer. A robust and reliable approach to cable welding is presented, and the effect of cable bending on the transport current is also investigated and presented.« less

Production of Aluminum Stabilized Superconducting Cable for the Mu2e Transport Solenoid

DOE PAGES

Lombardo, Vito; Ambrosio, Giorgio; Evbota, Daniel; ...

2018-01-15

Here, the Fermilab Mu2e experiment, currently under construction at Fermilab, has the goal of measuring the rare process of direct muon to electron conversion in the field of a nucleus. The experiment features three large superconducting solenoids: the production solenoid (PS), the transport solenoid (TS), and the detector solenoid (DS). The TS is an “S-shaped” solenoid that sits in between the PS and the DS producing a magnetic field ranging between 2.5 and 2.0 T. This paper describes the various steps that led to the successful procurement of over 740 km of superconducting wire and 44 km of Al-stabilized Rutherfordmore » cable needed to build the 52 coils that constitute the Mu2e TS cold mass. The main cable properties and results of electrical and mechanical test campaigns are summarized and discussed. Critical current measurements of the Al-stabilized cables are presented and compared to expected critical current values as measured on extracted strands from the final cables after chemical etching of the aluminum stabilizer. A robust and reliable approach to cable welding is presented, and the effect of cable bending on the transport current is also investigated and presented.« less

Antioxidant Activity in Extracts of 27 Indigenous Taiwanese Vegetables

PubMed Central

Chao, Pi-Yu; Lin, Su-Yi; Lin, Kuan-Hung; Liu, Yu-Fen; Hsu, Ju-Ing; Yang, Chi-Ming; Lai, Jun-You

2014-01-01

The objectives of this study were to identify the antioxidants and antioxidant axtivity in 27 of Taiwan’s indigenous vegetables. Lycium chinense (Lc), Lactuca indica (Li), and Perilla ocymoides (Po) contained abundant quercetin (Que), while Artemisia lactiflora (Al) and Gynura bicolor (Gb) were rich in morin and kaempferol, respectively. Additionally, Nymphoides cristata (Nc) and Sechium edule (Se)-yellow had significantly higher levels of myricetin (Myr) than other tested samples. Cyanidin (Cyan) and malvidin (Mal) were abundant in Gb, Abelmoschus esculentus Moench (Abe), Po, Anisogonium esculentum (Retz.) Presl (Ane), Ipomoea batatas (Ib)-purple, and Hemerocallis fulva (Hf)-bright orange. Relatively high levels of Trolox equivalent antioxidant capacity (TEAC), oxygen radical absorption capacity (ORAC), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical scavenger were generated from extracts of Toona sinensis (Ts) and Po. Significant and positive correlations between antioxidant activity and polyphenols, anthocyanidins, Que, Myr, and morin were observed, indicating that these phytochemicals were some of the main components responsible for the antioxidant activity of tested plants. The much higher antioxidant activity of Po, Ts, and Ib (purple leaf) may be related to their higher Cyan, Que, and polyphenol content. PMID:24858497

Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods.

PubMed

Rajabi, Mohamadreza; Mansourian, Ali; Bazmani, Ahad

2012-11-01

Visceral leishmaniasis (VL) is a vector-borne disease, highly influenced by environmental factors, which is an increasing public health problem in Iran, especially in the north-western part of the country. A geographical information system was used to extract data and map environmental variables for all villages in the districts of Kalaybar and Ahar in the province of East Azerbaijan. An attempt to predict VL prevalence based on an analytical hierarchy process (AHP) module combined with ordered weighted averaging (OWA) with fuzzy quantifiers indicated that the south-eastern part of Ahar is particularly prone to high VL prevalence. With the main objective to locate the villages most at risk, the opinions of experts and specialists were generalised into a group decision-making process by means of fuzzy weighting methods and induced OWA. The prediction model was applied throughout the entire study area (even where the disease is prevalent and where data already exist). The predicted data were compared with registered VL incidence records in each area. The results suggest that linguistic fuzzy quantifiers, guided by an AHP-OWA model, are capable of predicting susceptive locations for VL prevalence with an accuracy exceeding 80%. The group decision-making process demonstrated that people in 15 villages live under particularly high risk for VL contagion, i.e. villages where the disease is highly prevalent. The findings of this study are relevant for the planning of effective control strategies for VL in northwest Iran.

Fuzzy similarity measures for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Emara, Salem M.; Badawi, Ahmed M.; Youssef, Abou-Bakr M.

1995-03-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver from a normal one, by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases is rather confusing and highly dependent upon the sonographer's experience. The need for computerized tissue characterization is thus justified to quantitatively assist the sonographer for accurate differentiation and to minimize the degree of risk from erroneous interpretation. In this paper we used the fuzzy similarity measure as an approximate reasoning technique to find the maximum degree of matching between an unknown case defined by a feature vector and a family of prototypes (knowledge base). The feature vector used for the matching process contains 8 quantitative parameters (textural, acoustical, and speckle parameters) extracted from the ultrasound image. The steps done to match an unknown case with the family of prototypes (cirr, fatty, normal) are: Choosing the membership functions for each parameter, then obtaining the fuzzification matrix for the unknown case and the family of prototypes, then by the linguistic evaluation of two fuzzy quantities we obtain the similarity matrix, then by a simple aggregation method and the fuzzy integrals we obtain the degree of similarity. Finally, we find that the similarity measure results are comparable to the neural network classification techniques and it can be used in medical diagnosis to determine the pathology of the liver and to monitor the extent of the disease.

An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications.

PubMed

d'Acierno, Antonio; Esposito, Massimo; De Pietro, Giuseppe

2013-01-01

The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data. We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages. The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept. The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.

An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications

PubMed Central

2013-01-01

Background The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data. Methods We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages. Results The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept. Conclusions The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed. PMID:23368970

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larkin, Andrew; Department of Statistics, Oregon State University; Superfund Research Center, Oregon State University

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdanimore » logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions for combining PAH mixtures in agreement with microarrays ► Predictions highly dependent on aryl hydrocarbon receptor repressor expression.« less

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

NASA Astrophysics Data System (ADS)

Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.

2004-02-01

We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.

PubMed

Safdari, Reza; Arpanahi, Hadi Kazemi; Langarizadeh, Mostafa; Ghazisaiedi, Marjan; Dargahi, Hossein; Zendehdel, Kazem

2018-01-01

Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer. to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals. This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert's opinion. 50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert's diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%. The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

Fuzzy-trace theory: dual processes in memory, reasoning, and cognitive neuroscience.

PubMed

Brainerd, C J; Reyna, V F

2001-01-01

Fuzzy-trace theory has evolved in response to counterintuitive data on how memory development influences the development of reasoning. The two traditional perspectives on memory-reasoning relations--the necessity and constructivist hypotheses--stipulate that the accuracy of children's memory for problem information and the accuracy of their reasoning are closely intertwined, albeit for different reasons. However, contrary to necessity, correlational and experimental dissociations have been found between children's memory for problem information that is determinative in solving certain problems and their solutions of those problems. In these same tasks, age changes in memory for problem information appear to be dissociated from age changes in reasoning. Contrary to constructivism, correlational and experimental dissociations also have been found between children's performance on memory tests for actual experience and memory tests for the meaning of experience. As in memory-reasoning studies, age changes in one type of memory performance do not seem to be closely connected to age changes in the other type of performance. Subsequent experiments have led to dual-process accounts in both the memory and reasoning spheres. The account of memory development features four other principles: parallel verbatim-gist storage, dissociated verbatim-gist retrieval, memorial bases of conscious recollection, and identity/similarity processes. The account of the development of reasoning features three principles: gist extraction, fuzzy-to-verbatim continua, and fuzzy-processing preferences. The fuzzy-processing preference is a particularly important notion because it implies that gist-based intuitive reasoning often suffices to deliver "logical" solutions and that such reasoning confers multiple cognitive advantages that enhance accuracy. The explanation of memory-reasoning dissociations in cognitive development then falls out of fuzzy-trace theory's dual-process models of memory and reasoning. More explicitly, in childhood reasoning tasks, it is assumed that both verbatim and gist traces of problem information are stored. Responding accurately to memory tests for presented problem information depends primarily on verbatim memory abilities (preserving traces of that information and accessing them when the appropriate memory probes are administered). However, accurate solutions to reasoning problems depend primarily on gist-memory abilities (extracting the correct gist from problem information, focusing on that gist during reasoning, and accessing reasoning operations that process that gist). Because verbatim and gist memories exhibit considerable dissociation, both during storage and when they are subsequently accessed on memory tests, dissociations of verbatim-based memory performance from gist-based reasoning are predictable. Conversely, associations are predicted in situations in which memory and reasoning are based on the same verbatim traces (Brainerd & Reyna, 1988) and in situations in which memory and reasoning are based on the same gist traces (Reyna & Kiernan, 1994). Fuzzy-trace theory's memory and reasoning principles have been applied in other research domains. Four such domains are developmental cognitive neuroscience studies of false memory, studies of false memory in brain-damaged patients, studies of reasoning errors in judgment and decision making, and studies of retrieval mechanisms in recall. In the first domain, the principles of parallel verbatim-gist storage, dissociated verbatim-gist retrieval, and identity/similarity processes have been used to explain both spontaneous and implanted false reports in children and in the elderly. These explanations have produced some surprising predictions that have been verified: false reports do not merely decline with age during childhood but increase under theoretically specified conditions; reports of events that were not experienced can nevertheless be highly persistent over time; and false reports can be suppressed by retrieving verbatim traces of corresponding true events. In the second domain, the same principles have been invoked to explain why some forms of brain damage lead to elevated levels of false memory and other forms lead to reduced levels of false memory. In the third domain, the principles of gist extraction, fuzzy-to-verbatim continua, and fuzzy-processing preferences have been exploited to formulate a general theory of loci of processing failures in judgment and decision making, cluminating in a developmental account of degrees of rationality that distinguishes more and less advanced reasoning. This theory has in turn been used to formulate local models, such as the inclusion illusions model, that explain the characteristic reasoning errors that are observed on specific judgment and decision-making tasks. Finally, in the fourth domain, a dual-process conception of recall has been derived from the principles of parallel verbatim-gist storage and dissociated verbatim-gist retrieval. In this conception, which has been used to explain cognitive triage effects in recall and robust false recall, targets are recalled either by directly accessing their verbatim traces and reading the retrieved information out of consciousness or by reconstructively processing their gist traces.

Indian girls have higher bone mineral content per unit of lean body than boys through puberty.

PubMed

Khadilkar, Anuradha V; Sanwalka, Neha; Mughal, M Zulf; Chiplonkar, Shashi; Khadilkar, Vaman

2018-05-01

Our aim is to describe changes in the muscle-bone unit assessed as a ratio of bone mineral content (BMC) to lean body mass (LBM) through puberty at total body and various skeletal sites in Indian boys and girls. A cross-sectional study was conducted (888 children, 480 boys, aged 5-17 years) in Pune, India. Pubertal staging was assessed. BMC, LBM and fat percentage at the arms, legs, android, gynoid and total body (less the head) were assessed by dual energy X-ray absorptiometry. The amount of BMC per unit LBM (BMC/LBM) was computed. Changes in mean BMC/LBM at 5 Tanner (pubertal) stages after adjustment for age and fat percentage were calculated. In boys, adjusted BMC/LBM was significantly higher with successive Tanner stages [legs (TS-II vs TS-I), android (TS-III vs TS-II, TS-IV vs TS-III) and gynoid region (TS-III vs TS-II and TS-II vs TS-I) (p < 0.05)]. In girls, adjusted BMC/LBM was significantly higher with successive Tanner stages at total body, legs and gynoid (TS-III vs TS-II; TS-II vs TS-I; TS-V vs TS-IV), arms (TS-I to TS-V) and android regions (TS-V vs TS-IV) (p < 0.05). Boys had significantly higher adjusted BMC/LBM than girls at earlier Tanner stages (TS-I to TS-III), whereas girls had significantly higher adjusted BMC/LBM than boys at later Tanner stages (TS-IV, TS-V) (p < 0.05). Indian boys and girls showed higher total and regional body, and age- and fat percentage-adjusted BMC/LBM with successive pubertal stages. Girls had higher BMC/LBM than boys which may possibly act as a reservoir for later demands of pregnancy and lactation.

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

A Fuzzy Query Mechanism for Human Resource Websites

NASA Astrophysics Data System (ADS)

Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih

Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.

Certain and possible rules for decision making using rough set theory extended to fuzzy sets

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.

1993-01-01

Uncertainty may be caused by the ambiguity in the terms used to describe a specific situation. It may also be caused by skepticism of rules used to describe a course of action or by missing and/or erroneous data. To deal with uncertainty, techniques other than classical logic need to be developed. Although, statistics may be the best tool available for handling likelihood, it is not always adequate for dealing with knowledge acquisition under uncertainty. Inadequacies caused by estimating probabilities in statistical processes can be alleviated through use of the Dempster-Shafer theory of evidence. Fuzzy set theory is another tool used to deal with uncertainty where ambiguous terms are present. Other methods include rough sets, the theory of endorsements and nonmonotonic logic. J. Grzymala-Busse has defined the concept of lower and upper approximation of a (crisp) set and has used that concept to extract rules from a set of examples. We will define the fuzzy analogs of lower and upper approximations and use these to obtain certain and possible rules from a set of examples where the data is fuzzy. Central to these concepts will be the idea of the degree to which a fuzzy set A is contained in another fuzzy set B, and the degree of intersection of a set A with set B. These concepts will also give meaning to the statement; A implies B. The two meanings will be: (1) if x is certainly in A then it is certainly in B, and (2) if x is possibly in A then it is possibly in B. Next, classification will be looked at and it will be shown that if a classification will be looked at and it will be shown that if a classification is well externally definable then it is well internally definable, and if it is poorly externally definable then it is poorly internally definable, thus generalizing a result of Grzymala-Busse. Finally, some ideas of how to define consensus and group options to form clusters of rules will be given.

Nanostructured copper phthalocyanine-sensitized multiwall carbon nanotube films.

PubMed

Hatton, Ross A; Blanchard, Nicholas P; Stolojan, Vlad; Miller, Anthony J; Silva, S Ravi P

2007-05-22

We report a detailed study of the interaction between surface-oxidized multiwall carbon nanotubes (o-MWCNTs) and the molecular semiconductor tetrasulfonate copper phthalocyanine (TS-CuPc). Concentrated dispersions of o-MWCNT in aqueous solutions of TS-CuPc are stable toward nanotube flocculation and exhibit spontaneous nanostructuring upon rapid drying. In addition to hydrogen-bonding interactions, the compatibility between the two components is shown to result from a ground-state charge-transfer interaction with partial charge transfer from o-MWCNT to TS-CuPc molecules orientated such that the plane of the macrocycle is parallel to the nanotube surface. The electronegativity of TS-CuPc as compared to unsubsubtituted copper phthalocyanine is shown to result from the electron-withdrawing character of the sulfonate substituents, which increase the molecular ionization potential and promote cofacial molecular aggregation upon drying. Upon spin casting to form uniform thin films, the experimental evidence is consistent with an o-MWCNT scaffold decorated with phthalocyanine molecules self-assembled into extended aggregates reminiscent of 1-D linearly stacked phthalocyanine polymers. Remarkably, this self-organization occurs in a fraction of a second during the spin-coating process. To demonstrate the potential utility of this hybrid material, it is successfully incorporated into a model organic photovoltaic cell at the interface between a poly(3-hexylthiophene):[6,6]-phenyl-C61 butyric acid methyl ester bulk heterojunction layer and an indium-tin oxide-coated glass electrode to increase the light-harvesting capability of the device and facilitate hole extraction. The resulting enhancement in power conversion efficiency is rationalized in terms of the electronic, optical, and morphological properties of the nanostructured thin film.

An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry.

PubMed

Wang, Yang; Liu, Fang; Li, Peng; He, Chengwei; Wang, Ruibing; Su, Huanxing; Wan, Jian-Bo

2016-07-13

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies. Copyright © 2016 Elsevier B.V. All rights reserved.

Cell cycle phase dependent emergence of thymidylate synthase studied by monoclonal antibody (M-TS-4).

PubMed

Shibui, S; Hoshino, T; Iwasaki, K; Nomura, K; Jastreboff, M M

1989-05-01

A method of identifying thymidylate synthase (TS) at the cellular level was developed using anti-TS monoclonal antibody (M-TS-4), a monoclonal antibody created against purified TS from a HeLa cell line. In HeLa cells and four human glioma cell lines (U-251, U-87, 343-MGA, and SF-188), TS was identified primarily in the cytoplasm. Autoradiographic and flow cytometric studies showed that TS appeared mainly in the G1 phase and subsided early in the S phase; thus, the G1 phase can be divided into TS-positive and -negative fractions. Nuclear TS was not demonstrated unequivocally with M-TS-4, and the relationship between nuclear TS and DNA synthesis could not be determined. Although the percentage of TS-positive cells was larger than the S-phase fraction measured by autoradiography after a pulse of tritiated thymidine or by the immunoperoxidase method using BUdR, the ratios were within a similar range (1.2-1.4) in all cell lines studied. Therefore, the S-phase fraction can be estimated indirectly from the percentage of TS-positive cells measured by M-TS-4. Because the emergence of TS detected by our method is cell cycle dependent, M-TS-4 may be useful for biochemical studies of TS and for cytokinetic analysis.

Phase 2 study of treatment selection based on tumor thymidylate synthase expression in previously untreated patients with metastatic colorectal cancer: A trial of the ECOG-ACRIN Cancer Research Group (E4203).

PubMed

Meropol, Neal J; Feng, Yang; Grem, Jean L; Mulcahy, Mary F; Catalano, Paul J; Kauh, John S; Hall, Michael J; Saltzman, Joel N; George, Thomas J; Zangmeister, Jeffrey; Chiorean, Elena G; Cheema, Puneet S; O'Dwyer, Peter J; Benson, Al B

2018-02-15

The authors hypothesized that patients with metastatic colorectal cancer (mCRC) who had tumors with low thymidylate synthase (TS-L) expression would have a higher response rate to combined 5-fluorouracil, leucovorin, and oxaliplatin (FOLFOX) plus bevacizumab (FOLFOX/Bev) than those with high TS (TS-H) expression and that combined irinotecan and oxaliplatin (IROX) plus bevacizumab (IROX/Bev) would be more effective than FOLFOX/Bev in those with TS-H tumors. TS protein expression was determined in mCRC tissue. Patients who had TS-L tumors received FOLFOX/Bev, and those who had TS-H tumors were randomly assigned to receive either FOLFOX/Bev or IROX/Bev. The primary endpoint was the response rate (complete plus partial responses). In total, 211 of 247 patients (70% TS-H) were registered to the treatment phase. Efficacy analyses included eligible patients who had started treatment (N = 186). The response rates for patients who received IROX/Bev (TS-H), FOLFOX/Bev (TS-H), and FOLFOX/Bev (TS-L) were 33%, 38%, and 49%, respectively (P = nonsignificant). The median progression-free survival (PFS) was 10 months (95% confidence interval [CI], 9-12 months; 10 months in the IROX/Bev TS-H group, 9 months in the FOLFOX/Bev TS-H group, and 13 months in the FOLFOX/Bev TS-L group). The TS-L group had improved PFS compared with the TS-H group that received FOLFOX/Bev (hazard ratio, 1.6; 95% CI, 1.0%-2.4%; P = .04; Cox regression). The median overall survival (OS) was 22 months (95% CI, 20 29 months; 18 months in the IROX/Bev TS-H group, 21 months in the FOLFOX/Bev TS-H group, and 32 months in the TS-L group). OS comparisons for the 2 TS-H arms and for the FOLFOX/Bev TS-H versus TS-L arms were not significantly different. TS expression was prognostic: Patients with TS-L tumors who received FOLFOX/Bev had a longer PFS than those with TS-H tumors, along with a trend toward longer OS. Patients with TS-H tumors did not benefit more from IROX/Bev than from FOLFOX/Bev. Cancer 2018;124:688-97. © 2017 American Cancer Society. © 2017 American Cancer Society.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

PubMed

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

Carbohydrate Recognition Specificity of Trans-sialidase Lectin Domain from Trypanosoma congolense

PubMed Central

Waespy, Mario; Gbem, Thaddeus T.; Elenschneider, Leroy; Jeck, André-Philippe; Day, Christopher J.; Hartley-Tassell, Lauren; Bovin, Nicolai; Tiralongo, Joe; Haselhorst, Thomas; Kelm, Sørge

2015-01-01

Fourteen different active Trypanosoma congolense trans-sialidases (TconTS), 11 variants of TconTS1 besides TconTS2, TconTS3 and TconTS4, have been described. Notably, the specific transfer and sialidase activities of these TconTS differ by orders of magnitude. Surprisingly, phylogenetic analysis of the catalytic domains (CD) grouped each of the highly active TconTS together with the less active enzymes. In contrast, when aligning lectin-like domains (LD), the highly active TconTS grouped together, leading to the hypothesis that the LD of TconTS modulates its enzymatic activity. So far, little is known about the function and ligand specificity of these LDs. To explore their carbohydrate-binding potential, glycan array analysis was performed on the LD of TconTS1, TconTS2, TconTS3 and TconTS4. In addition, Saturation Transfer Difference (STD) NMR experiments were done on TconTS2-LD for a more detailed analysis of its lectin activity. Several mannose-containing oligosaccharides, such as mannobiose, mannotriose and higher mannosylated glycans, as well as Gal, GalNAc and LacNAc containing oligosaccharides were confirmed as binding partners of TconTS1-LD and TconTS2-LD. Interestingly, terminal mannose residues are not acceptor substrates for TconTS activity. This indicates a different, yet unknown biological function for TconTS-LD, including specific interactions with oligomannose-containing glycans on glycoproteins and GPI anchors found on the surface of the parasite, including the TconTS itself. Experimental evidence for such a scenario is presented. PMID:26474304

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

Antigen induced inhibition of autoimmune response to rat male accessory glands: role of thymocytes on the efferent phase of the suppression.

PubMed

Ferro, M E; Romero-Piffiguer, M; Rivero, V; Yranzo-Volonte, N; Correa, S; Riera, C M

1991-01-01

In the present study, we report that Cy-sensitive, MRAG-adherent spleen mononuclear (SpM) inductor-phase T suppressor (Ts) cells obtained from rats pretreated with low doses of a purified fraction (FI) of rat male accessory gland antigens (RAG) are mainly OX19+ and W3/25+. Furthermore, thymocytes from rats pretreated with FI of RAG restore the suppression of the autoimmune response to RAG autoantigens in irradiated recipients of SpM inductor-phase Ts cells. In contrast, thymocytes from rats pretreated with rat heart saline extract (unrelated antigen) did not recuperate the suppression of the autoimmune response detected by macrophage migration inhibitory factor (MIF) and delayed-type hypersensitivity. The suppressor thymocytes did not directly exert their inhibitory effect because they were not effective to suppress the autoimmune response to RAG autoantigens when irradiated recipients did not receive SpM inductor-phase Ts cells. The effect of these thymocytes was found in PNA--but not in PNA+ thymic cell population. The perithymic injection of Toxoplasma gondii did block their suppressor activity. The present report clearly shows an active participation of thymus in the efferent phase of the suppressor circuit that controls the autoimmune response to MRAG. The implications of these findings are discussed.

Optimal solution of full fuzzy transportation problems using total integral ranking

NASA Astrophysics Data System (ADS)

Sam’an, M.; Farikhin; Hariyanto, S.; Surarso, B.

2018-03-01

Full fuzzy transportation problem (FFTP) is a transportation problem where transport costs, demand, supply and decision variables are expressed in form of fuzzy numbers. To solve fuzzy transportation problem, fuzzy number parameter must be converted to a crisp number called defuzzyfication method. In this new total integral ranking method with fuzzy numbers from conversion of trapezoidal fuzzy numbers to hexagonal fuzzy numbers obtained result of consistency defuzzyfication on symmetrical fuzzy hexagonal and non symmetrical type 2 numbers with fuzzy triangular numbers. To calculate of optimum solution FTP used fuzzy transportation algorithm with least cost method. From this optimum solution, it is found that use of fuzzy number form total integral ranking with index of optimism gives different optimum value. In addition, total integral ranking value using hexagonal fuzzy numbers has an optimal value better than the total integral ranking value using trapezoidal fuzzy numbers.

Commercial applications

NASA Technical Reports Server (NTRS)

Togai, Masaki

1990-01-01

Viewgraphs on commercial applications of fuzzy logic in Japan are presented. Topics covered include: suitable application area of fuzzy theory; characteristics of fuzzy control; fuzzy closed-loop controller; Mitsubishi heavy air conditioner; predictive fuzzy control; the Sendai subway system; automatic transmission; fuzzy logic-based command system for antilock braking system; fuzzy feed-forward controller; and fuzzy auto-tuning system.

A fuzzy stochastic framework for managing hydro-environmental and socio-economic interactions under uncertainty

NASA Astrophysics Data System (ADS)

Subagadis, Yohannes Hagos; Schütze, Niels; Grundmann, Jens

2014-05-01

An amplified interconnectedness between a hydro-environmental and socio-economic system brings about profound challenges of water management decision making. In this contribution, we present a fuzzy stochastic approach to solve a set of decision making problems, which involve hydrologically, environmentally, and socio-economically motivated criteria subjected to uncertainty and ambiguity. The proposed methodological framework combines objective and subjective criteria in a decision making procedure for obtaining an acceptable ranking in water resources management alternatives under different type of uncertainty (subjective/objective) and heterogeneous information (quantitative/qualitative) simultaneously. The first step of the proposed approach involves evaluating the performance of alternatives with respect to different types of criteria. The ratings of alternatives with respect to objective and subjective criteria are evaluated by simulation-based optimization and fuzzy linguistic quantifiers, respectively. Subjective and objective uncertainties related to the input information are handled through linking fuzziness and randomness together. Fuzzy decision making helps entail the linguistic uncertainty and a Monte Carlo simulation process is used to map stochastic uncertainty. With this framework, the overall performance of each alternative is calculated using an Order Weighted Averaging (OWA) aggregation operator accounting for decision makers' experience and opinions. Finally, ranking is achieved by conducting pair-wise comparison of management alternatives. This has been done on the basis of the risk defined by the probability of obtaining an acceptable ranking and mean difference in total performance for the pair of management alternatives. The proposed methodology is tested in a real-world hydrosystem, to find effective and robust intervention strategies for the management of a coastal aquifer system affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. The results show that the approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

AFRRI Reports, Third Quarter 1994

DTIC Science & Technology

1994-10-01

with biologic response modifiers (BRMs), such as LPS, 3D monophosphoryl lipid A (MPL), and synthetic trehalose dico- rynomycolate (S-TDCM...monophosphosphoryl lipid A; S-TDCM, synthetic trehalose dicorynomycolate; Sm-BRM, extract from Serratia marcescens; TS, 2% Tween 80 in 0.9% NaCI; RT-PCR...Immun. 58:2429. 42 23. Madonna, G. S.. G, D. Ledney, D. C. Funckes, and E. E. Ribi. 1988. Monophosphoryl lipid A and trehalose dimycolate therapy

Rapid conversion of sorbitol to isosorbide in hydrophobic ionic liquids under microwave irradiation.

PubMed

Kamimura, Akio; Murata, Kengo; Tanaka, Yoshiki; Okagawa, Tomoki; Matsumoto, Hiroshi; Kaiso, Kouji; Yoshimoto, Makoto

2014-12-01

Sorbitol was effectively converted to isosorbide by treatment with [TMPA][NTf2 ] in the presence of catalytic amounts of TsOH under microwave heating at 180 °C. The reaction completed within 10 min and isosorbide was isolated to about 60%. Ionic liquids were readily recovered by an extraction treatment and reused several times. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Thymidylate synthase (TS) gene expression in primary lung cancer patients: a large-scale study in Japanese population.

PubMed

Tanaka, F; Wada, H; Fukui, Y; Fukushima, M

2011-08-01

Previous small-sized studies showed lower thymidylate synthase (TS) expression in adenocarcinoma of the lung, which may explain higher antitumor activity of TS-inhibiting agents such as pemetrexed. To quantitatively measure TS gene expression in a large-scale Japanese population (n = 2621) with primary lung cancer, laser-captured microdissected sections were cut from primary tumors, surrounding normal lung tissues and involved nodes. TS gene expression level in primary tumor was significantly higher than that in normal lung tissue (mean TS/β-actin, 3.4 and 1.0, respectively; P < 0.01), and TS gene expression level was further higher in involved node (mean TS/β-actin, 7.7; P < 0.01). Analyses of TS gene expression levels in primary tumor according to histologic cell type revealed that small-cell carcinoma showed highest TS expression (mean TS/β-actin, 13.8) and that squamous cell carcinoma showed higher TS expression as compared with adenocarcinoma (mean TS/β-actin, 4.3 and 2.3, respectively; P < 0.01); TS gene expression was significantly increased along with a decrease in the grade of tumor cell differentiation. There was no significant difference in TS gene expression according to any other patient characteristics including tumor progression. Lower TS expression in adenocarcinoma of the lung was confirmed in a large-scale study.

An LMI approach to design H(infinity) controllers for discrete-time nonlinear systems based on unified models.

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

PubMed

Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

2014-10-01

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

Thymidylate synthase (TS) protein expression as a prognostic factor in advanced colorectal cancer: a comparison with TS mRNA expression.

PubMed

Nakagawa, Tateo; Shimada, Mitsuo; Kurita, Nobuhiro; Iwata, Takashi; Nishioka, Masanori; Yoshikawa, Kozo; Higashijima, Jun; Utsunomiya, Tohru

2012-06-01

The role of intratumoral thymidylate synthase (TS) mRNA or protein expression is still controversial and little has been reported regarding relation of them in colorectal cancer. Forty-six patients with advanced colorectal cancer who underwent surgical resection were included. TS mRNA expression was determined by the Danenberg tumor profile method based on laser-captured micro-dissection of the tumor cells. TS protein expression was evaluated using immunohistochemical staining. TS mRNA expression tended to relate TS protein expression. Statistical significance was not found in overall survival between the TS mRNA high group and low group regardless of performing adjuvant chemotherapy. The overall survival in the TS protein negative group was significantly higher than that in positive group in all and the patients without adjuvant chemotherapy. Multivariate analysis showed TS protein expression was as an independent prognostic factor. TS protein expression tends to be related TS mRNA expression and is an independent prognostic factor in advanced colorectal cancer.

VAS: A Vision Advisor System combining agents and object-oriented databases

NASA Technical Reports Server (NTRS)

Eilbert, James L.; Lim, William; Mendelsohn, Jay; Braun, Ron; Yearwood, Michael

1994-01-01

A model-based approach to identifying and finding the orientation of non-overlapping parts on a tray has been developed. The part models contain both exact and fuzzy descriptions of part features, and are stored in an object-oriented database. Full identification of the parts involves several interacting tasks each of which is handled by a distinct agent. Using fuzzy information stored in the model allowed part features that were essentially at the noise level to be extracted and used for identification. This was done by focusing attention on the portion of the part where the feature must be found if the current hypothesis of the part ID is correct. In going from one set of parts to another the only thing that needs to be changed is the database of part models. This work is part of an effort in developing a Vision Advisor System (VAS) that combines agents and objected-oriented databases.

Forest fire autonomous decision system based on fuzzy logic

NASA Astrophysics Data System (ADS)

Lei, Z.; Lu, Jianhua

2010-11-01

The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

Fuzzy Neural Network Applied to Gene Expression Profiling for Predicting the Prognosis of Diffuse Large B‐cell Lymphoma

PubMed Central

Ando, Tatsuya; Suguro, Miyuki; Hanai, Taizo; Kobayashi, Takeshi; Seto, Masao

2002-01-01

Diffuse large B‐cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long‐overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes (CD10, AA807551, AA805611 and IRF‐4) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy. PMID:12460461

Iris recognition using possibilistic fuzzy matching on local features.

PubMed

Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

2012-02-01

In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

PubMed

Zhang, Jian-Hua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Ru-Bin

2013-12-01

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Color image analysis technique for measuring of fat in meat: an application for the meat industry

NASA Astrophysics Data System (ADS)

Ballerini, Lucia; Hogberg, Anders; Lundstrom, Kerstin; Borgefors, Gunilla

2001-04-01

Intramuscular fat content in meat influences some important meat quality characteristics. The aim of the present study was to develop and apply image processing techniques to quantify intramuscular fat content in beefs together with the visual appearance of fat in meat (marbling). Color images of M. longissimus dorsi meat samples with a variability of intramuscular fat content and marbling were captured. Image analysis software was specially developed for the interpretation of these images. In particular, a segmentation algorithm (i.e. classification of different substances: fat, muscle and connective tissue) was optimized in order to obtain a proper classification and perform subsequent analysis. Segmentation of muscle from fat was achieved based on their characteristics in the 3D color space, and on the intrinsic fuzzy nature of these structures. The method is fully automatic and it combines a fuzzy clustering algorithm, the Fuzzy c-Means Algorithm, with a Genetic Algorithm. The percentages of various colors (i.e. substances) within the sample are then determined; the number, size distribution, and spatial distributions of the extracted fat flecks are measured. Measurements are correlated with chemical and sensory properties. Results so far show that advanced image analysis is useful for quantify the visual appearance of meat.

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

Idiopathic interstitial pneumonias and emphysema: detection and classification using a texture-discriminative approach

NASA Astrophysics Data System (ADS)

Fetita, C.; Chang-Chien, K. C.; Brillet, P. Y.; Pr"teux, F.; Chang, R. F.

2012-03-01

Our study aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on three-dimensional (3-D) mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). According to a preliminary evaluation on an extended database, the proposed method can overcome the drawbacks of a previously developed approach and achieve higher sensitivity and specificity.

Fuzzy logic system able to detect interesting areas of a video sequence

NASA Astrophysics Data System (ADS)

De Vleeschouwer, Christophe; Marichal, Xavier; Delmot, Thierry; Macq, Benoit M. M.

1997-06-01

This paper introduces an automatic tool able to analyze the picture according to the semantic interest an observer attributes to its content. Its aim is to give a 'level of interest' to the distinct areas of the picture extracted by any segmentation tool. For the purpose of dealing with semantic interpretation of images, a single criterion is clearly insufficient because the human brain, due to its a priori knowledge and its huge memory of real-world concrete scenes, combines different subjective criteria in order to assess its final decision. The developed method permits such combination through a model using assumptions to express some general subjective criteria. Fuzzy logic enables the user to encode knowledge in a form that is very close the way experts think about the decision process. This fuzzy modeling is also well suited to represent multiple collaborating or even conflicting experts opinions. Actually, the assumptions are verified through a non-hierarchical strategy that considers them in a random order, each partial result contributing to the final one. Presented results prove that the tool is effective for a wide range of natural pictures. It is versatile and flexible in that it can be used stand-alone or can take into account any a priori knowledge about the scene.

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

NASA Astrophysics Data System (ADS)

Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

2012-04-01

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

PubMed

Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

2017-01-14

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture

PubMed Central

Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

2017-01-01

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT. PMID:28098822

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Study on a Biometric Authentication Model based on ECG using a Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Kim, Ho J.; Lim, Joon S.

2018-03-01

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

Statistical and Clustering Based Rules Extraction Approaches for Fuzzy Model to Estimate Academic Performance in Distance Education

ERIC Educational Resources Information Center

Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

2015-01-01

The demand for distance education has been increasing at a rapid pace all around the world. This, in turn, places a special importance on the need for the development of more distance education systems. However, there is an alarming rise in the number of distance education students that drop out of the system without asking for any help. The…

The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph

DTIC Science & Technology

1993-12-01

actual feature extraction was done, It was decided to use the K-nearest neighbor ( KNN ) the data was preprocessed. The electrocardiogram classifier in...showing heart pulse, and a low frequency not known beforehand, and the KNN classifier does not component showing blood volume. The derivative of...the characteristics of the conventional KNN these six derived signals were detrended and filtered, classification method is that it assigns each

Improving land resource evaluation using fuzzy neural network ensembles

USGS Publications Warehouse

Xue, Yue-Ju; HU, Y.-M.; Liu, S.-G.; YANG, J.-F.; CHEN, Q.-C.; BAO, S.-T.

2007-01-01

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images.

PubMed

Shanthi, C; Pappa, N

2017-05-01

Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

PubMed Central

Almazroa, Ahmed; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-01-01

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu’s segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images. PMID:28515636

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction.

PubMed

Almazroa, Ahmed; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-01-01

We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu's segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

Clinical Concepts on Thyroid Emergencies

PubMed Central

Papi, Giampaolo; Corsello, Salvatore Maria; Pontecorvi, Alfredo

2014-01-01

Objective: Thyroid-related emergencies are caused by overt dysfunction of the gland which are so severe that require admission to intensive care units (ICU) frequently. Nonetheless, in the ICU setting, it is crucial to differentiate patients with non-thyroidal illness and alterations in thyroid function tests from those with intrinsic thyroid disease. This review presents and discusses the main etiopathogenetical and clinical aspects of hypothyroid coma (HC) and thyrotoxic storm (TS), including therapeutic strategy flow-charts. Furthermore, a special chapter is dedicated to the approach to massive goiter, which represents a surgical thyroid emergency. Data Source: We searched the electronic MEDLINE database on September 2013. Data Selection and Data Extraction: Reviews, original articles, and case reports on “myxedematous coma,” “HC,” “thyroid storm,” “TS,” “massive goiter,” “huge goiter,” “prevalence,” “etiology,” “diagnosis,” “therapy,” and “prognosis” were selected. Data Synthesis and Conclusion: Severe excess or defect of thyroid hormone is rare conditions, which jeopardize the life of patients in most cases. Both HC and TS are triggered by precipitating factors, which occur in patients with severe hypothyroidism or thyrotoxicosis, respectively. The pillars of HC therapy are high-dose l-thyroxine and/or tri-iodothyroinine; i.v. glucocorticoids; treatment of hydro-electrolyte imbalance (mainly, hyponatraemia); treatment of hypothermia; often, endotracheal intubation and assisted mechanic ventilation are needed. Therapy of TS is based on beta-blockers, thyrostatics, and i.v. glucocorticoids; eventually, high-dose of iodide compounds or lithium carbonate may be of benefit. Surgery represents the gold standard treatment in patients with euthyroid massive nodular goiter, although new techniques – e.g., percutaneous laser ablation – are helpful in subjects at high surgical risk or refusing operation. PMID:25071718

Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer.

PubMed

Gray, Richard G; Quirke, Philip; Handley, Kelly; Lopatin, Margarita; Magill, Laura; Baehner, Frederick L; Beaumont, Claire; Clark-Langone, Kim M; Yoshizawa, Carl N; Lee, Mark; Watson, Drew; Shak, Steven; Kerr, David J

2011-12-10

We developed quantitative gene expression assays to assess recurrence risk and benefits from chemotherapy in patients with stage II colon cancer. We sought validation by using RNA extracted from fixed paraffin-embedded primary colon tumor blocks from 1,436 patients with stage II colon cancer in the QUASAR (Quick and Simple and Reliable) study of adjuvant fluoropyrimidine chemotherapy versus surgery alone. A recurrence score (RS) and a treatment score (TS) were calculated from gene expression levels of 13 cancer-related genes (n = 7 recurrence genes and n = 6 treatment benefit genes) and from five reference genes with prespecified algorithms. Cox proportional hazards regression models and log-rank methods were used to analyze the relationship between the RS and risk of recurrence in patients treated with surgery alone and between TS and benefits of chemotherapy. Risk of recurrence was significantly associated with RS (hazard ratio [HR] per interquartile range, 1.38; 95% CI, 1.11 to 1.74; P = .004). Recurrence risks at 3 years were 12%, 18%, and 22% for predefined low, intermediate, and high recurrence risk groups, respectively. T stage (HR, 1.94; P < .001) and mismatch repair (MMR) status (HR, 0.31; P < .001) were the strongest histopathologic prognostic factors. The continuous RS was associated with risk of recurrence (P = .006) beyond these and other covariates. There was no trend for increased benefit from chemotherapy at higher TS (P = .95). The continuous 12-gene RS has been validated in a prospective study for assessment of recurrence risk in patients with stage II colon cancer after surgery and provides prognostic value that complements T stage and MMR. The TS was not predictive of chemotherapy benefit.

Do peat amendments to oil sands wet sediments affect Carex aquatilis biomass for reclamation success?

PubMed

Roy, Marie-Claude; Mollard, Federico P O; Foote, A Lee

2014-06-15

The oil sands industries of Alberta (Canada) have reclamation objectives to return the mined landscape to equivalent pre-disturbance land capability. Industrial operators are charged with reclaiming a vast landscape of newly exposed sediments on saline-sodic marine-shales sediments. Incorporated in these sediments are by-products resulting from bitumen extraction (consolidated tailings (CT), tailings-sand (TS), and oil sands processed water (OSPW)). A sedge community dominated by Carex aquatilis was identified as a desirable and representative late-succession community for wet-meadow zones of oil sands-created marshes. However, the physical and chemical conditions, including high salinity and low nutrient content of CT and TS sediments suppress plant growth and performance. We experimentally tested the response of C. aquatilis to amendments with peat-mineral-mix (PM) on oil sand sediments (CT and TS). In a two factorial design experiment, we also tested the effects of OSPW on C. aquatilis. We assessed survival, below- and aboveground biomass, and physiology (chlorophyll a fluorescence). We demonstrated that PM amendments to oil sands sediments significantly increased C. aquatilis survival as well as below and aboveground biomass. The use of OSPW significantly reduced C. aquatilis belowground biomass and affected its physiological performance. Due to its tolerance and performance, we verified that C. aquatilis was a good candidate for use in reclaiming the wet-meadow zones of oil sands-created marshes. Ultimately, amending CT and TS with PM expedited the reclamation of the wetland to a C. aquatilis-community which was similar in gross structure to undisturbed wetlands of the region. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prognostic significance of thymidylate synthase (TS) expression in cutaneous malignant melanoma.

PubMed

Shimizu, A; Kaira, K; Yasuda, M; Asao, T; Ishikawa, O

2016-01-01

Thymidylate synthase (TS) plays an essential role in the pathogenesis and development of cancer, and TS-targeting agents have been widely used against different types of cancers. However, it remains still unclear whether or not TS is expressed in malignant melanoma. We conducted the clinicopathological study to investigate the prognostic significance of TS expression in cutaneous malignant melanoma. Ninety-nine patients with surgically resected cutaneous malignant melanoma were assessed. Tumor sections were stained by immunohistochemistry for TS, Ki-67, and microvessel density (MVD) determined by CD34. TS was positively expressed in 26% (26 out of 99). The expression of TS was significantly associated with T factor, cell proliferation (Ki-67) and MVD (CD34). By Spearman's rank test, TS expression was significantly correlated with Ki67 and CD34. By univariate analysis, ulceration, disease stage, TS, Ki-67 and CD34 had a significant relationship with survival. Multivariate analysis confirmed that TS was an independent prognostic factor for poor prognosis of cutaneous malignant melanoma. The positive expression of TS could be a useful marker for predicting poor prognosis in patients with cutaneous malignant melanoma, and TS-targeting agents may be worth trying for the treatment of this dismal disease.

Characterizations of Some Fuzzy Prefilters (Filters) in EQ-Algebras

PubMed Central

Xin, Xiao Long; Yang, Yong Wei

2014-01-01

We introduce and study some types of fuzzy prefilters (filters) in EQ-algebras. First, we present several characterizations of fuzzy positive implicative prefilters (filters), fuzzy implicative prefilters (filters), and fuzzy fantastic prefilters (filters). Next, using their characterizations, we mainly consider the relationships among these special fuzzy filters. Particularly, we find some conditions under which a fuzzy implicative prefilter (filter) is equivalent to a fuzzy positive implicative prefilter (filter). As applications, we obtain some new results about classical filters in EQ-algebras and some related results about fuzzy filters in residuated lattices. PMID:24892096

Solving fully fuzzy transportation problem using pentagonal fuzzy numbers

NASA Astrophysics Data System (ADS)

Maheswari, P. Uma; Ganesan, K.

2018-04-01

In this paper, we propose a simple approach for the solution of fuzzy transportation problem under fuzzy environment in which the transportation costs, supplies at sources and demands at destinations are represented by pentagonal fuzzy numbers. The fuzzy transportation problem is solved without converting to its equivalent crisp form using a robust ranking technique and a new fuzzy arithmetic on pentagonal fuzzy numbers. To illustrate the proposed approach a numerical example is provided.

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

Kilowatt Isotope Power System: component test report for the Ground Demonstration System Alternator Stator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brainard, E.L.

1978-04-25

Results are presented of acceptance tests conducted on the Alternator Stator, S/N 002, for the Kilowatt Isotope Power System. These results show that the Alternator Stator, S/N 002 for the Kilowatt Isotope Power System has satisfactorily completed the testing set forth within Sundstrand Test Specification 2538. Test requirements of TS 2538 were extracted from the Kilowatt Isotope Power System, and Phase I Test Plan.

Intuitionistic fuzzy n-fold KU-ideal of KU-algebra

NASA Astrophysics Data System (ADS)

Mostafa, Samy M.; Kareem, Fatema F.

2018-05-01

In this paper, we apply the notion of intuitionistic fuzzy n-fold KU-ideal of KU-algebra. Some types of ideals such as intuitionistic fuzzy KU-ideal, intuitionistic fuzzy closed ideal and intuitionistic fuzzy n-fold KU-ideal are studied. Also, the relations between intuitionistic fuzzy n-fold KU-ideal and intuitionistic fuzzy KU-ideal are discussed. Furthermore, a few results of intuitionistic fuzzy n-fold KU-ideals of a KU-algebra under homomorphism are discussed.

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method

PubMed Central

Alguliyev, Rasim M.; Aliguliyev, Ramiz M.; Mahmudova, Rasmiyya S.

2015-01-01

Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method. PMID:26516634

Combining fuzzy mathematics with fuzzy logic to solve business management problems

NASA Astrophysics Data System (ADS)

Vrba, Joseph A.

1993-12-01

Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.

Fuzzy Logic Engine

NASA Technical Reports Server (NTRS)

Howard, Ayanna

2005-01-01

The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.

Design and implementation of fuzzy logic controllers. Thesis Final Report, 27 Jul. 1992 - 1 Jan. 1993

NASA Technical Reports Server (NTRS)

Abihana, Osama A.; Gonzalez, Oscar R.

1993-01-01

The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.

A two-phased fuzzy decision making procedure for IT supplier selection

NASA Astrophysics Data System (ADS)

Shohaimay, Fairuz; Ramli, Nazirah; Mohamed, Siti Rosiah; Mohd, Ainun Hafizah

2013-09-01

In many studies on fuzzy decision making, linguistic terms are usually represented by corresponding fixed triangular or trapezoidal fuzzy numbers. However, the fixed fuzzy numbers used in decision making process may not explain the actual respondents' opinions. Hence, a two-phased fuzzy decision making procedure is proposed. First, triangular fuzzy numbers were built based on respondents' opinions on the appropriate range (0-100) for each seven-scale linguistic terms. Then, the fuzzy numbers were integrated into fuzzy decision making model. The applicability of the proposed method is demonstrated in a case study of supplier selection in Information Technology (IT) department. The results produced via the developed fuzzy numbers were consistent with the results obtained using fixed fuzzy numbers. However, with different set of fuzzy numbers based on respondents, there is a difference in the ranking of suppliers based on criterion X1 (background of supplier). Hopefully the proposed model which incorporates fuzzy numbers based on respondents will provide a more significant meaning towards future decision making.

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

Influence of nuclei segmentation on breast cancer malignancy classification

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

PubMed

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

An integrated modeling approach to support management decisions of coupled groundwater-agricultural systems under multiple uncertainties

NASA Astrophysics Data System (ADS)

Hagos Subagadis, Yohannes; Schütze, Niels; Grundmann, Jens

2015-04-01

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational frameworks. Such integrative research to link different knowledge domains faces several practical challenges. Such complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. A fuzzy-stochastic multiple criteria decision analysis tool was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrated physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approach is applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structure. Results from the developed method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach has enabled to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. Sensitivity analysis applied within the developed tool has shown that the decision makers' risk aversion and risk taking attitude may yield in different ranking of decision alternatives. The developed approach can be applied to address the complexities and uncertainties inherent in water resources systems to support management decisions, while serving as a platform for stakeholder participation.

Construction of fuzzy spaces and their applications to matrix models

NASA Astrophysics Data System (ADS)

Abe, Yasuhiro

Quantization of spacetime by means of finite dimensional matrices is the basic idea of fuzzy spaces. There remains an issue of quantizing time, however, the idea is simple and it provides an interesting interplay of various ideas in mathematics and physics. Shedding some light on such an interplay is the main theme of this dissertation. The dissertation roughly separates into two parts. In the first part, we consider rather mathematical aspects of fuzzy spaces, namely, their construction. We begin with a review of construction of fuzzy complex projective spaces CP k (k = 1, 2, · · ·) in relation to geometric quantization. This construction facilitates defining symbols and star products on fuzzy CPk. Algebraic construction of fuzzy CPk is also discussed. We then present construction of fuzzy S 4, utilizing the fact that CP3 is an S2 bundle over S4. Fuzzy S4 is obtained by imposing an additional algebraic constraint on fuzzy CP3. Consequently it is proposed that coordinates on fuzzy S4 are described by certain block-diagonal matrices. It is also found that fuzzy S8 can analogously be constructed. In the second part of this dissertation, we consider applications of fuzzy spaces to physics. We first consider theories of gravity on fuzzy spaces, anticipating that they may offer a novel way of regularizing spacetime dynamics. We obtain actions for gravity on fuzzy S2 and on fuzzy CP3 in terms of finite dimensional matrices. Application to M(atrix) theory is also discussed. With an introduction of extra potentials to the theory, we show that it also has new brane solutions whose transverse directions are described by fuzzy S 4 and fuzzy CP3. The extra potentials can be considered as fuzzy versions of differential forms or fluxes, which enable us to discuss compactification models of M(atrix) theory. In particular, compactification down to fuzzy S4 is discussed and a realistic matrix model of M-theory in four-dimensions is proposed.

Neuropsychological functioning in children with Tourette syndrome (TS).

PubMed

Rasmussen, Carmen; Soleimani, Maryam; Carroll, Alan; Hodlevskyy, Oleksander

2009-11-01

We examined whether children with Tourette syndrome (TS) displayed a unique pattern of neuropsychological deficits on the CANTAB relative to control children. We also looked at whether children with TS and other comorbidities had more neuropsychological impairments than those with uncomplicated TS and how age was related to the profile of neuropsychological deficits in TS. Participants included 38 children with TS (aged 7 to 13 years) and 38 control children (aged 6 to 12 years). All children were administered 8 subtests from the CANTAB and parents and teachers completed the BRIEF rating scale on children in the TS group. Children with TS displayed deficits relative to control children on measures of visual memory, executive functioning, and attention from the CANTAB. Among the TS group, age was negatively correlated with performance on measures of executive functioning, speed of response and working memory. Identifying the pattern of neuropsychological deficits in children with TS on the CANTAB is important for highlighting areas of deficit that can be targeted for intervention and teaching strategies. With further research, the CANTAB may prove to be a useful resource in the assessment and treatment of children with TS.

Tuberous sclerosis: aberrant metabolism of ornithine, proline and glutamate in cultured fibroblasts.

PubMed

Tanaka, H; Nakazawa, K; Arima, M; Hayashi, A

1987-01-01

To investigate aberrant metabolism of proline (Pro) and its precursors in tuberous sclerosis (TS), 6, 7 and 5 strains of control, TS (normal skin) and TS (tumor) fibroblasts, respectively, were cultured in Eagle's MEM containing dialyzed fetal bovine serum with or without 0.1 mM ornithine (Orn). Ornithine aminotransferase (OAT) activity was decreased in TS, especially in TS (tumor) after mild sonication treatment. The yield of the OAT protein was inhibited in TS (tumor) when cultured without Orn. Free glutamate (Glu) in the medium was significantly increased in TS (tumor). Free proline (Pro) in cells was significantly decreased in TS (tumor) when cultured with Orn, but protein-bound Pro was not. The relative concentration of free Glu to glutamine (Gln) in the medium and that of free Glu to Pro in cells cultured with Orn were increased in TS (tumor). These results suggest that the requirement for Orn, increased turnover of Pro to Glu and increased elimination of Glu into the medium occur in TS (tumor). Aberrant regulation or turnover of Pro and Glu metabolism may occur in TS, especially in tumor cells.

Complex Fuzzy Set-Valued Complex Fuzzy Measures and Their Properties

PubMed Central

Ma, Shengquan; Li, Shenggang

2014-01-01

Let F*(K) be the set of all fuzzy complex numbers. In this paper some classical and measure-theoretical notions are extended to the case of complex fuzzy sets. They are fuzzy complex number-valued distance on F*(K), fuzzy complex number-valued measure on F*(K), and some related notions, such as null-additivity, pseudo-null-additivity, null-subtraction, pseudo-null-subtraction, autocontionuous from above, autocontionuous from below, and autocontinuity of the defined fuzzy complex number-valued measures. Properties of fuzzy complex number-valued measures are studied in detail. PMID:25093202

Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships.

PubMed

Chen, Shyi-Ming; Chen, Shen-Wen

2015-03-01

In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flanagan, Sheryl A., E-mail: sflan@umich.edu; Cooper, Kristin S.; Mannava, Sudha

Purpose: To determine the effect of short hairpin ribonucleic acid (shRNA)-mediated suppression of thymidylate synthase (TS) on cytotoxicity and radiosensitization and the mechanism by which these events occur. Methods and Materials: shRNA suppression of TS was compared with 5-fluoro-2 Prime -deoxyuridine (FdUrd) inactivation of TS with or without ionizing radiation in HCT116 and HT29 colon cancer cells. Cytotoxicity and radiosensitization were measured by clonogenic assay. Cell cycle effects were measured by flow cytometry. The effects of FdUrd or shRNA suppression of TS on dNTP deoxynucleotide triphosphate imbalances and consequent nucleotide misincorporations into deoxyribonucleic acid (DNA) were analyzed by high-pressure liquidmore » chromatography and as pSP189 plasmid mutations, respectively. Results: TS shRNA produced profound ({>=}90%) and prolonged ({>=}8 days) suppression of TS in HCT116 and HT29 cells, whereas FdUrd increased TS expression. TS shRNA also produced more specific and prolonged effects on dNTPs deoxynucleotide triphosphates compared with FdUrd. TS shRNA suppression allowed accumulation of cells in S-phase, although its effects were not as long-lasting as those of FdUrd. Both treatments resulted in phosphorylation of Chk1. TS shRNA alone was less cytotoxic than FdUrd but was equally effective as FdUrd in eliciting radiosensitization (radiation enhancement ratio: TS shRNA, 1.5-1.7; FdUrd, 1.4-1.6). TS shRNA and FdUrd produced a similar increase in the number and type of pSP189 mutations. Conclusions: TS shRNA produced less cytotoxicity than FdUrd but was equally effective at radiosensitizing tumor cells. Thus, the inhibitory effect of FdUrd on TS alone is sufficient to elicit radiosensitization with FdUrd, but it only partially explains FdUrd-mediated cytotoxicity and cell cycle inhibition. The increase in DNA mismatches after TS shRNA or FdUrd supports a causal and sufficient role for the depletion of dTTP thymidine triphosphate and consequent DNA mismatches underlying radiosensitization. Importantly, shRNA suppression of TS avoids FP-mediated TS elevation and its negative prognostic role. These studies support the further exploration of TS suppression as a novel radiosensitizing strategy.« less

A Comparison of Tropical Storm (TS) and Non-TS Gust Factors for Assessing Peak Wind Probabilities at the Eastern Range

NASA Technical Reports Server (NTRS)

Merceret, Francis J.; Crawford, Winifred C.

2010-01-01

Peak wind speed is an important forecast element to ensure the safety of personnel and flight hardware at Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) in East-Central Florida. The 45th Weather Squadron (45 WS), the organization that issues forecasts for the KSC/CCAFS area, finds that peak winds are more difficult to forecast than mean winds. This difficulty motivated the 45 WS to request two independent studies. The first (Merceret 2009) was the development of a reliable model for gust factors (GF) relating the peak to the mean wind speed in tropical storms (TS). The second (Lambert et al. 2008) was a climatological study of non-TS cool season (October-April) mean and peak wind speeds by the Applied Meteorology Unit (AMU; Bauman et al. 2004) without the use of GF. Both studies presented their statistics as functions of mean wind speed and height. Most of the few comparisons of TS and non-TS GF in the literature suggest that non-TS GF at a given height and mean wind speed are smaller than the corresponding TS GF. The investigation reported here converted the non-TS peak wind statistics calculated by the AMU to the equivalent GF statistics and compared them with the previous TS GF results. The advantage of this effort over all previously reported studies of its kind is that the TS and non-TS data were taken from the same towers in the same locations. This eliminates differing surface attributes, including roughness length and thermal properties, as a major source of variance in the comparison. The goal of this study is two-fold: to determine the relationship between the non-TS and TS GF and their standard deviations (GFSD) and to determine if models similar to those developed for TS data in Merceret (2009) could be developed for the non-TS environment. The results are consistent with the literature, but include much more detailed, quantitative information on the nature of the relationship between TS and non-TS GF and GFSD as a function of height and mean wind speed.

Noxious inhibition of temporal summation is impaired in chronic tension-type headache.

PubMed

Cathcart, Stuart; Winefield, Anthony H; Lushington, Kurt; Rolan, Paul

2010-03-01

To examine effects of stress on noxious inhibition and temporal summation (TS) in tension-type headache. Stress is the most commonly reported trigger of a chronic tension-type headache (CTH) episode; however, the mechanisms underlying this are unclear. Stress affects pain processing throughout the central nervous system, including, potentially, mechanisms of TS and diffuse noxious inhibitory controls (DNIC), both of which may be abnormal in CTH sufferers (CTH-S). No studies have examined TS of pressure pain or DNIC of TS in CTH-S to date. Similarly, effects of stress on TS or DNIC of TS have not been reported in healthy subjects or CTH-S to date. The present study measured TS and DNIC of TS in CTH-S and healthy controls (CNT) exposed to an hour-long stressful mental task, and in CTH-S exposed to an hour-long neutral condition. TS was elicited at finger and shoulder via 10 pulses from a pressure algometer, applied before and during stimulation from an occlusion cuff at painful intensity. Algometer pain ratings increased more in the CTH compared with the CNT group, and were inhibited during occlusion cuff more in the CNT compared with CTH groups. Task effects on TS or DNIC were not significant. The results indicate increased TS to pressure pain and impaired DNIC of TS in CTH-S. Stress does not appear to aggravate abnormal TS or DNIC mechanisms in CTH-S.

Acoustic/Seismic Ground Sensors for Detection, Localization and Classification on the Battlefield

DTIC Science & Technology

2006-10-01

controlled so that collisions are avoided. Figure 1 presents BACH system components. 3 BACH Sensor Posts (1 to 8) Command Post BACH MMI PC VHF...2.2.4 Processing scheme Processing inside SP is dedicated to stationary spectral lines extraction and derives from ASW algorithms. Special attention...is similar to that used for helicopters (see figure 4), with adaptations to cope with vehicles signatures (fuzzy unstable spectral lines, abrupt

Nonsaponifiable lipid components of the pollen of elder (Sambucus nigra L.).

PubMed

Stránsky, K; Valterová, I; Fiedler, P

2001-11-30

Pollen of the elder (Sambucus nigra L.) was extracted with chloroform-methanol. The extract was separated by column chromatography into the following groups of compounds: hydrocarbons (8.7%). polycyclic aromatic hydrocarbons (0.2%), complex esters (5.2%), triglycerides (18.7%), hydroxy esters (27.9%), free fatty acids and alcohols (16.8%), free sterols (6.8%), and triterpenic alcohols (4.0%). The nonsaponifiable components (hydrocarbons, fatty acids, alcohols, and sterols) were examined in detail using spectroscopic and chromatographic methods (IR spectroscopy, GC, and GC-MS). The identified compounds were characterized by their mass spectra and Kováts retention indices. The double bond positions and their configurations in unsaturated compounds are also reported.

System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.

Evaluating Tidal Energy Resource Assessment Guidelines

NASA Astrophysics Data System (ADS)

Haas, K. A.

2016-02-01

All tidal energy projects require resource assessments for determining the feasibility of a particular site, performing the project layout design and providing the projected annual energy production (AEP). The methods for the different resource assessments depend on both the assessment scope as well as the project scale. To assist with the development of the hydrokinetic industry as a whole, much work over the past decade has been completed to develop international technical standards that can be used by the full range of stakeholders in the hydrokinetic industry. In particular, a new International Electrotechnical Commission (IEC) Technical Specification (TS) has recently been published outlining a standardized methodology for performing tidal energy resource assessments. This presentation will cover the various methods for performing the different types of tidal resource assessments (national reconnaissance, regional feasibility and layout design). Illustrations through case studies will be presented for each type of resource assessment. In particular, the ability of a grid refinement technique which satisfies the TS grid resolution requirements for the assessment of tidal current energy while maintaining low computational expenses will be evaluated. Example applications will be described for mapping the tidal resources near two facilities (Portsmouth Naval Shipyard in Maine and Key West Naval Station in Florida) for possible future deployments of Marine Hydro-Kinetic (MHK) technologies. These assessments will include and demonstrate the importance of the effect of energy extraction as required by the TS.

Synthesis of mesoporous TS-1 using a hybrid SiO{sub 2}–TiO{sub 2} xerogel for catalytic oxidative desulfurization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Seung-Tae; Jeong, Kwang-Eun; Jeong, Soon-Yong

2012-12-15

Graphical abstract: Display Omitted Highlights: ► Meso-TS-1 catalyst was synthesized using a SiO{sub 2}–TiO{sub 2} xerogel with an organosilane precursor. ► Hierarchical pore structure was confirmed by characterization of the materials. ► Catalytic activity was tested using oxidative desulfurization of the model sulfur compounds. ► Meso-TS-1 demonstrated significantly improved catalytic activity than TS-1. -- Abstract: Mesoporous TS-1 (M-TS-1) was synthesized using a hybrid SiO{sub 2}–TiO{sub 2} xerogel combined with an organosilane precursor. Prepared samples were characterized by XRD, UV–vis spectroscopy, SEM, and N{sub 2} adsorption–desorption measurement. M-TS-1, prepared in 2 days, showed high crystallinity and the best textural properties amongmore » the samples. The N{sub 2} adsorption–desorption isotherms of M-TS-1 exhibited a hysteresis loop at pressure higher than P/P{sub 0} = 0.4, clearly indicating the existence of mesopores. M-TS-1 has significantly larger mesopore volume (0.48 cm{sup 3}/g) than that of conventional TS-1 (0.07 cm{sup 3}/g), and showed a narrow peak centered at ca. 6.3 nm. In the oxidative desulfurization reaction, M-TS-1 was more active than conventional TS-1 at the same Ti-loading; M-TS-1 produced a dibenzothiophene (DBT) conversion of 96%, whereas conventional TS-1 produced a final DBT conversion of 5.6% after a reaction time of 180 min. Oxidative desulfurization over TS-1 was influenced both by electron density and steric hindrance in the sulfur compounds tested.« less

Standardizing procedures to study sensitization of human spinal nociceptive processes: comparing parameters for temporal summation of the nociceptive flexion reflex (TS-NFR).

PubMed

Terry, Ellen L; France, Christopher R; Bartley, Emily J; Delventura, Jennifer L; Kerr, Kara L; Vincent, Ashley L; Rhudy, Jamie L

2011-09-01

Temporal summation of pain (TS-pain) is the progressive increase in pain ratings during a series of noxious stimulations. TS-pain has been used to make inferences about sensitization of spinal nociceptive processes; however, pain report can be biased thereby leading to problems with this inference. Temporal summation of the nociceptive flexion reflex (TS-NFR, a physiological measure of spinal nociception) can potentially overcome report bias, but there have been few attempts (generally with small Ns) to standardize TS-NFR procedures. In this study, 50 healthy participants received 25 series of noxious electric stimulations to evoke TS-NFR and TS-pain. Goals were to: 1) determine the stimulation frequency that best elicits TS-NFR and reduces electromyogram (EMG) contamination from muscle tension, 2) determine the minimum number of stimulations per series before NFR summation asymptotes, 3) compare NFR definition intervals (90-150ms vs. 70-150ms post-stimulation), and 4) compare TS-pain and TS-NFR when different stimulation frequencies are used. Results indicated TS-NFR should be elicited by a series of three stimuli delivered at 2.0Hz and TS-NFR should be defined from a 70-150ms post-stimulation scoring interval. Unfortunately, EMG contamination from muscle tension was greatest during 2.0Hz series. Discrepancies were noted between TS-NFR and TS-pain which raise concerns about using pain ratings to infer changes in spinal nociceptive processes. And finally, some individuals did not have reliable NFRs when the stimulation intensity was set at NFR threshold during TS-NFR testing; therefore, a higher intensity is needed. Implications of findings are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Effects of teeth clenching on the soleus H reflex during lower limb muscle fatigue.

PubMed

Mitsuyama, Akihiro; Takahashi, Toshiyuki; Ueno, Toshiaki

2017-04-01

We assessed whether the soleus H reflex was depressed or facilitated in association with voluntary teeth clenching during muscle fatigue. A total of 13 and 9 healthy adult subjects were instructed to perform right-side tiptoe standing for 5 (TS1) and 10min (TS2) to induce the soleus muscle fatigue. Electromyograms (EMGs) were recorded from the bilateral masseter as well as the right-side soleus muscles. H reflex was evoked using a surface electrode. The isometric muscle strength during plantar flexion was measured. We tested two dental occlusal conditions (1) with maximal voluntary teeth clenching (MVTC) and (2) at mandibular rest position (RP). H reflex was evoked before and after TS1 and TS2. The isometric muscle strength during plantar flexion was measured before and after TS1 and TS2. Mean amplitudes of H reflex with MVTC before and after TS1 were significantly larger than that with RP before and after TS1. The mean peak torque (PT) during isometric plantar flexion was observed significant differences in all subjects. The mean amplitude of H reflex with MVTC before TS2 was significantly larger than that with RP before TS2. No significant difference between RP after TS2 and MVTC after TS2. The mean PT with MVTC before TS2 was significantly larger than that with RP before TS2. There was no significant difference between RP and MVTC after TS2. The present study demonstrated that teeth clenching could facilitate H reflex regardless of the degree of muscle fatigue. Copyright © 2016 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

On Some Nonclassical Algebraic Properties of Interval-Valued Fuzzy Soft Sets

PubMed Central

2014-01-01

Interval-valued fuzzy soft sets realize a hybrid soft computing model in a general framework. Both Molodtsov's soft sets and interval-valued fuzzy sets can be seen as special cases of interval-valued fuzzy soft sets. In this study, we first compare four different types of interval-valued fuzzy soft subsets and reveal the relations among them. Then we concentrate on investigating some nonclassical algebraic properties of interval-valued fuzzy soft sets under the soft product operations. We show that some fundamental algebraic properties including the commutative and associative laws do not hold in the conventional sense, but hold in weaker forms characterized in terms of the relation =L. We obtain a number of algebraic inequalities of interval-valued fuzzy soft sets characterized by interval-valued fuzzy soft inclusions. We also establish the weak idempotent law and the weak absorptive law of interval-valued fuzzy soft sets using interval-valued fuzzy soft J-equal relations. It is revealed that the soft product operations ∧ and ∨ of interval-valued fuzzy soft sets do not always have similar algebraic properties. Moreover, we find that only distributive inequalities described by the interval-valued fuzzy soft L-inclusions hold for interval-valued fuzzy soft sets. PMID:25143964

On some nonclassical algebraic properties of interval-valued fuzzy soft sets.

PubMed

Liu, Xiaoyan; Feng, Feng; Zhang, Hui

2014-01-01

Interval-valued fuzzy soft sets realize a hybrid soft computing model in a general framework. Both Molodtsov's soft sets and interval-valued fuzzy sets can be seen as special cases of interval-valued fuzzy soft sets. In this study, we first compare four different types of interval-valued fuzzy soft subsets and reveal the relations among them. Then we concentrate on investigating some nonclassical algebraic properties of interval-valued fuzzy soft sets under the soft product operations. We show that some fundamental algebraic properties including the commutative and associative laws do not hold in the conventional sense, but hold in weaker forms characterized in terms of the relation = L . We obtain a number of algebraic inequalities of interval-valued fuzzy soft sets characterized by interval-valued fuzzy soft inclusions. We also establish the weak idempotent law and the weak absorptive law of interval-valued fuzzy soft sets using interval-valued fuzzy soft J-equal relations. It is revealed that the soft product operations ∧ and ∨ of interval-valued fuzzy soft sets do not always have similar algebraic properties. Moreover, we find that only distributive inequalities described by the interval-valued fuzzy soft L-inclusions hold for interval-valued fuzzy soft sets.

Fuzzy scalar and vector median filters based on fuzzy distances.

PubMed

Chatzis, V; Pitas, I

1999-01-01

In this paper, the fuzzy scalar median (FSM) is proposed, defined by using ordering of fuzzy numbers based on fuzzy minimum and maximum operations defined by using the extension principle. Alternatively, the FSM is defined from the minimization of a fuzzy distance measure, and the equivalence of the two definitions is proven. Then, the fuzzy vector median (FVM) is proposed as an extension of vector median, based on a novel distance definition of fuzzy vectors, which satisfy the property of angle decomposition. By defining properly the fuzziness of a value, the combination of the basic properties of the classical scalar and vector median (VM) filter with other desirable characteristics can be succeeded.

Research on Bounded Rationality of Fuzzy Choice Functions

PubMed Central

Wu, Xinlin; Zhao, Yong

2014-01-01

The rationality of a fuzzy choice function is a hot research topic in the study of fuzzy choice functions. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. The complete rationality and bounded rationality of fuzzy choice functions are defined based on the two fuzzy sets. An assumption is presented to study the fuzzy choice function, and especially the fuzzy choice function with bounded rationality is studied combined with some rationality conditions. Results show that the fuzzy choice function with bounded rationality also satisfies some important rationality conditions, but not vice versa. The research gives supplements to the investigation in the framework of the Banerjee choice function. PMID:24782677

Research on bounded rationality of fuzzy choice functions.

PubMed

Wu, Xinlin; Zhao, Yong

2014-01-01

The rationality of a fuzzy choice function is a hot research topic in the study of fuzzy choice functions. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. The complete rationality and bounded rationality of fuzzy choice functions are defined based on the two fuzzy sets. An assumption is presented to study the fuzzy choice function, and especially the fuzzy choice function with bounded rationality is studied combined with some rationality conditions. Results show that the fuzzy choice function with bounded rationality also satisfies some important rationality conditions, but not vice versa. The research gives supplements to the investigation in the framework of the Banerjee choice function.

A population-based analysis of mortality in patients with Turner syndrome and hypoplastic left heart syndrome using the Texas Birth Defects Registry.

PubMed

Lara, Diego A; Ethen, Mary K; Canfield, Mark A; Nembhard, Wendy N; Morris, Shaine A

2017-01-01

Hypoplastic left heart syndrome (HLHS) is strongly associated with Turner syndrome (TS); outcome data when these conditions coexist is sparse. We aimed to investigate long-term survival and causes of death in this population. The Texas Birth Defects Registry was queried for all live born infants with HLHS during 1999-2007. We used Kaplan-Meier and Cox regression analyses to compare survival among patients with HLHS with TS (HLHS/TS+) to patients who had HLHS without genetic disorders or extracardiac birth defects (HLHS/TS-). Of the 542 patients with HLHS, 11 had TS (2.0%), 71 had other extracardiac birth defects or genetic disorders, and 463 had neither. The median follow-up time was 4.2 y (interquartile range [IQR] 2.1-6.5). Comparing those with HLHS/TS+ to HLHS/TS-, 100% versus 35% were female (P < .001), and median birth weight was 2140 g (IQR 1809-2650) versus 3196 g (IQR 2807-3540, P < .001). Neonatal mortality was 36% in HLHS/TS+ versus 27% in HLHS/TS- (log rank = 0.431). Ten of the 11 TS+ patients died during the study period for cumulative mortality of 91% versus 50% (hazard ratio (HR) for TS+: 2.90, 95% CI 1.53-5.48). Six patients died prior to surgery, 5 underwent Stage 1 palliation (S1P), 3 died after S1P, 2 survived past S2P, and one of these died at age 19 mo. The underlying cause of death was listed as congenital heart disease on all the death certificates of HLHS/TS+ patients. In multivariable analysis controlling for low birth weight (<2500 g), TS remained associated with significantly increased cumulative mortality, although females without TS had higher mortality than males (HR for TS+ versus males: 2.42, 95% CI 1.24-4.73; HR for TS- females versus males: 1.41, 95% CI 1.08-1.83). TS with HLHS is associated with significant mortality. The increased mortality in females without documented TS calls to question if TS is undetected in a portion of females with HLHS. © 2016 Wiley Periodicals, Inc.

Total solids content drives high solid anaerobic digestion via mass transfer limitation.

PubMed

Abbassi-Guendouz, Amel; Brockmann, Doris; Trably, Eric; Dumas, Claire; Delgenès, Jean-Philippe; Steyer, Jean-Philippe; Escudié, Renaud

2012-05-01

The role of the total solids (TS) content on anaerobic digestion was investigated in batch reactors. A range of TS contents from 10% to 35% was evaluated, four replicates were performed. The total methane production slightly decreased with TS concentrations increasing from 10% to 25% TS. Two behaviors were observed at 30% TS: two replicates had similar performances to that at 25% TS; for the two other replicates, the methane production was inhibited as observed at 35% TS. This difference suggested that 30% TS content corresponded to a threshold of the solids content, above which methanogenesis was strongly inhibited. The Anaerobic Digestion Model No. 1 (ADM1) was used to describe the experimental data. The effects of hydrolysis step and liquid/gas mass transfer were particularly investigated. The simulations showed that mass transfer limitation could explain the low methane production at high TS, and that hydrolysis rate constants slightly decreased with increasing TS. Copyright © 2012 Elsevier Ltd. All rights reserved.

The intelligence of dual simplex method to solve linear fractional fuzzy transportation problem.

PubMed

Narayanamoorthy, S; Kalyani, S

2015-01-01

An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example.

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.

ERIC Educational Resources Information Center

Chen, Ruey-Shun; Hu, Yi-Chung

2003-01-01

Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)

A novel approach for analyzing fuzzy system reliability using different types of intuitionistic fuzzy failure rates of components.

PubMed

Kumar, Mohit; Yadav, Shiv Prasad

2012-03-01

This paper addresses the fuzzy system reliability analysis using different types of intuitionistic fuzzy numbers. Till now, in the literature, to analyze the fuzzy system reliability, it is assumed that the failure rates of all components of a system follow the same type of fuzzy set or intuitionistic fuzzy set. However, in practical problems, such type of situation rarely occurs. Therefore, in the present paper, a new algorithm has been introduced to construct the membership function and non-membership function of fuzzy reliability of a system having components following different types of intuitionistic fuzzy failure rates. Functions of intuitionistic fuzzy numbers are calculated to construct the membership function and non-membership function of fuzzy reliability via non-linear programming techniques. Using the proposed algorithm, membership functions and non-membership functions of fuzzy reliability of a series system and a parallel systems are constructed. Our study generalizes the various works of the literature. Numerical examples are given to illustrate the proposed algorithm. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Designing boosting ensemble of relational fuzzy systems.

PubMed

Scherer, Rafał

2010-10-01

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.

Solutions of interval type-2 fuzzy polynomials using a new ranking method

NASA Astrophysics Data System (ADS)

Rahman, Nurhakimah Ab.; Abdullah, Lazim; Ghani, Ahmad Termimi Ab.; Ahmad, Noor'Ani

2015-10-01

A few years ago, a ranking method have been introduced in the fuzzy polynomial equations. Concept of the ranking method is proposed to find actual roots of fuzzy polynomials (if exists). Fuzzy polynomials are transformed to system of crisp polynomials, performed by using ranking method based on three parameters namely, Value, Ambiguity and Fuzziness. However, it was found that solutions based on these three parameters are quite inefficient to produce answers. Therefore in this study a new ranking method have been developed with the aim to overcome the inherent weakness. The new ranking method which have four parameters are then applied in the interval type-2 fuzzy polynomials, covering the interval type-2 of fuzzy polynomial equation, dual fuzzy polynomial equations and system of fuzzy polynomials. The efficiency of the new ranking method then numerically considered in the triangular fuzzy numbers and the trapezoidal fuzzy numbers. Finally, the approximate solutions produced from the numerical examples indicate that the new ranking method successfully produced actual roots for the interval type-2 fuzzy polynomials.

Synergism and foaming properties in binary mixtures of a biosurfactant derived from Camellia oleifera Abel and synthetic surfactants.

PubMed

Jian, Hong-lei; Liao, Xiao-xia; Zhu, Li-wei; Zhang, Wei-ming; Jiang, Jian-xin

2011-07-15

A biosurfactant, named tea saponin (TS), was isolated and purified from the defatted seed of Camellia oleifera Abel. The characterization of TS including molecular weight, glycosyl composition, and thermal behavior as well as the surface and foaming properties was conducted. The synergistic interactions of binary systems of CTAB-TS, SDS-TS, and Brij35-TS were investigated. The results show that TS had a weight-average molecular weight of 809.12 g mol(-1) and contained four aglycones of L-rhamnose, D-galactose, D-glucose, and D-glucuronic acid. The critical micelle concentration (cmc) of 2.242 mmol L(-1) and the minimum surface tension (γ(cmc)) of 43.5 mN m(-1) were determined for TS. Synergisms in surface tension reduction efficiency, in mixed micelle formation, and in surface tension reduction effectiveness were observed in CTAB-TS and SDS-TS systems, whereas that was not shown in Brij35-TS mixtures. The mixtures of TS with CTAB and SDS showed synergism in foaming efficiency, but this synergism did not exist in Brij35-TS system with respect to the surface properties. Nevertheless, there appears to be no significant correlation between foam stability and the surface properties. Copyright © 2011 Elsevier Inc. All rights reserved.

Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

PubMed Central

Wang, Zhengfang; Chen, Pei; Yu, Liangli; Harrington, Peter de B.

2013-01-01

Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers. PMID:23398171

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

PubMed

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Implementation of Steiner point of fuzzy set.

PubMed

Liang, Jiuzhen; Wang, Dejiang

2014-01-01

This paper deals with the implementation of Steiner point of fuzzy set. Some definitions and properties of Steiner point are investigated and extended to fuzzy set. This paper focuses on establishing efficient methods to compute Steiner point of fuzzy set. Two strategies of computing Steiner point of fuzzy set are proposed. One is called linear combination of Steiner points computed by a series of crisp α-cut sets of the fuzzy set. The other is an approximate method, which is trying to find the optimal α-cut set approaching the fuzzy set. Stability analysis of Steiner point of fuzzy set is also studied. Some experiments on image processing are given, in which the two methods are applied for implementing Steiner point of fuzzy image, and both strategies show their own advantages in computing Steiner point of fuzzy set.

Are there distinct subtypes in Tourette syndrome? Pure-Tourette syndrome versus Tourette syndrome-plus, and simple versus complex tics

PubMed Central

Eapen, Valsamma; Robertson, Mary M

2015-01-01

This study addressed several questions relating to the core features of Tourette syndrome (TS) including in particular coprolalia (involuntary utterance of obscene words) and copropraxia (involuntary and inappropriate rude gesturing). A cohort of 400 TS patients was investigated. We observed that coprolalia occurred in 39% of the full cohort of 400 patients and copropraxia occurred in 20% of the cohort. Those with coprolalia had significantly higher Yale Global Tic Severity Scale (YGTSS) and Diagnostic Confidence Index (DCI) total scores and a significantly higher proportion also experienced copropraxia and echolalia. A subgroup of 222 TS patients with full comorbidity data available were also compared based on whether they had pure-TS (motor and vocal tics only) or associated comorbidities and co-existent psychopathologies (TS-plus). Pure-TS and TS-plus groups were compared across a number of characteristics including TS severity, associated clinical features, and family history. In this subgroup, 13.5% had pure-TS, while the remainder had comorbidities and psychopathologies consistent with TS-plus. Thirty-nine percent of the TS-plus group displayed coprolalia, compared to (0%) of the pure-TS group and the difference in proportions was statistically significant. The only other significant difference found between the two groups was that pure-TS was associated with no family history of obsessive compulsive disorder which is an interesting finding that may suggest that additional genes or environmental factors may be at play when TS is associated with comorbidities. Finally, differences between individuals with simple versus complex vocal/motor tics were evaluated. Results indicated that individuals with complex motor/vocal tics were significantly more likely to report premonitory urges/sensations than individuals with simple tics and TS. The implications of these findings for the assessment and understanding of TS are discussed. PMID:26089672

The consistency of positive fully fuzzy linear system

NASA Astrophysics Data System (ADS)

Malkawi, Ghassan O.; Alfifi, Hassan Y.

2017-11-01

In this paper, the consistency of fuzziness of positive solution of the n × n fully fuzzy linear system (P - FFLS) is studied based on its associated linear system (P - ALS). That can consist of the whole entries of triangular fuzzy numbers in a linear system without fuzzy operations. The nature of solution is differentiated in case of fuzzy solution, non-fuzzy solution and fuzzy non-positive solution. Moreover, the analysis reveals that the P - ALS is applicable to provide the set of infinite number of solutions. Numerical examples are presented to illustrate the proposed analysis.

Fuzzy topological digital space and digital fuzzy spline of electroencephalography during epileptic seizures

NASA Astrophysics Data System (ADS)

Shah, Mazlina Muzafar; Wahab, Abdul Fatah

2017-08-01

Epilepsy disease occurs because of there is a temporary electrical disturbance in a group of brain cells (nurons). The recording of electrical signals come from the human brain which can be collected from the scalp of the head is called Electroencephalography (EEG). EEG then considered in digital format and in fuzzy form makes it a fuzzy digital space data form. The purpose of research is to identify the area (curve and surface) in fuzzy digital space affected by inside epilepsy seizure in epileptic patient's brain. The main focus for this research is to generalize fuzzy topological digital space, definition and basic operation also the properties by using digital fuzzy set and the operations. By using fuzzy digital space, the theory of digital fuzzy spline can be introduced to replace grid data that has been use previously to get better result. As a result, the flat of EEG can be fuzzy topological digital space and this type of data can be use to interpolate the digital fuzzy spline.

Using fuzzy fractal features of digital images for the material surface analisys

NASA Astrophysics Data System (ADS)

Privezentsev, D. G.; Zhiznyakov, A. L.; Astafiev, A. V.; Pugin, E. V.

2018-01-01

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper, a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach, we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images.

[Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

PubMed

Luo, Yi; Zhang, Tao; Li, Xiao-song

2016-05-01

To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

Some induced intuitionistic fuzzy aggregation operators applied to multi-attribute group decision making

NASA Astrophysics Data System (ADS)

Su, Zhi-xin; Xia, Guo-ping; Chen, Ming-yuan

2011-11-01

In this paper, we define various induced intuitionistic fuzzy aggregation operators, including induced intuitionistic fuzzy ordered weighted averaging (OWA) operator, induced intuitionistic fuzzy hybrid averaging (I-IFHA) operator, induced interval-valued intuitionistic fuzzy OWA operator, and induced interval-valued intuitionistic fuzzy hybrid averaging (I-IIFHA) operator. We also establish various properties of these operators. And then, an approach based on I-IFHA operator and intuitionistic fuzzy weighted averaging (WA) operator is developed to solve multi-attribute group decision-making (MAGDM) problems. In such problems, attribute weights and the decision makers' (DMs') weights are real numbers and attribute values provided by the DMs are intuitionistic fuzzy numbers (IFNs), and an approach based on I-IIFHA operator and interval-valued intuitionistic fuzzy WA operator is developed to solve MAGDM problems where the attribute values provided by the DMs are interval-valued IFNs. Furthermore, induced intuitionistic fuzzy hybrid geometric operator and induced interval-valued intuitionistic fuzzy hybrid geometric operator are proposed. Finally, a numerical example is presented to illustrate the developed approaches.

The Intelligence of Dual Simplex Method to Solve Linear Fractional Fuzzy Transportation Problem

PubMed Central

Narayanamoorthy, S.; Kalyani, S.

2015-01-01

An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example. PMID:25810713

Fuzzy associative memories

NASA Technical Reports Server (NTRS)

Kosko, Bart

1991-01-01

Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

Bearing-only Cooperative Localization: Simulation and Experimental Results

DTIC Science & Technology

2013-01-01

matrix Fi and Bi are the system jacobian with respect to state Xi and control ui, which are given below Fi = I3 + Ts ∂fi ∂Xi |Xi=Xi(k) =  1 0 − ViTs ...sinψ(k)0 1 ViTs cosψ(k) 0 0 1  , (8) Bi = Ts ∂fi ∂ui |ui=ui(k) Ts cosψk 0Ts sinψk 0 0 Ts  , (9) and Qi(k) = ( σ2vi 0 0 σ2ωi ) , where σvi and σωi

Effect of Hammerhead Ribozyme against Human Thymidylate Synthase on the Cytotoxicity of Thymidylate Synthase Inhibitors

PubMed Central

Takemura, Yuzuru; Miyachi, Hayato; Skelton, Lorraine; Jackman, Ann L.

1995-01-01

One of the resistance mechanisms to folate‐based thymidylate synthase (TS) inhibitors is the increase in TS activity in tumor cells. Human B lymphoblastoid cell line (W1L2) was made resistant to a lipophilic non‐polyglutamatable TS inhibitor (ZM249148), and the subline (W1L2:R179) showed a 20‐fold increase in TS enzyme activity with concomitant overexpression of TS mRNA. To overcome the resistance, we designed a ribozyme that can cleave the CUC sequences in a triple tandemly repeated sequence of TS mRNA. Expression of this ribozyme in W1L2:R179 cells transfected with Epstein Barr virus‐based expression vector resulted in sensitization to TS inhibitors concomitantly with a decrease of TS expression. The ribozyme expressed in transfectants was shown to be functional in cleaving artificial TS RNA in vitro. PMID:8567390

A New Green Ionic Liquid-Based Corrosion Inhibitor for Steel in Acidic Environments.

PubMed

Atta, Ayman M; El-Mahdy, Gamal A; Al-Lohedan, Hamad A; Ezzat, Abdel Rahman O

2015-06-17

This work examines the use of new hydrophobic ionic liquid derivatives, namely octadecylammonium tosylate (ODA-TS) and oleylammonium tosylate (OA-TS) for corrosion protection of steel in 1 M hydrochloric acid solution. Their chemical structures were determined from NMR analyses. The surface activity characteristics of the prepared ODA-TS and OA-TS were evaluated from conductance, surface tension and contact angle measurements. The data indicate the presence of a double bond in the chemical structure of OA-TS modified its surface activity parameters. Potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) measurements, scanning electron microscope (SEM), Energy dispersive X-rays (EDX) analysis and contact angle measurements were utilized to investigate the corrosion protection performance of ODA-TS and OA-TS on steel in acidic solution. The OA-TS and ODA-TS compounds showed good protection performance in acidic chloride solution due to formation of an inhibitive film on the steel surface.

Encoding spatial images: A fuzzy set theory approach

NASA Technical Reports Server (NTRS)

Sztandera, Leszek M.

1992-01-01

As the use of fuzzy set theory continues to grow, there is an increased need for methodologies and formalisms to manipulate obtained fuzzy subsets. Concepts involving relative position of fuzzy patterns are acknowledged as being of high importance in many areas. In this paper, we present an approach based on the concept of dominance in fuzzy set theory for modelling relative positions among fuzzy subsets of a plane. In particular, we define the following spatial relations: to the left (right), in front of, behind, above, below, near, far from, and touching. This concept has been implemented to define spatial relationships among fuzzy subsets of the image plane. Spatial relationships based on fuzzy set theory, coupled with a fuzzy segmentation, should therefore yield realistic results in scene understanding.

Addressing the Complexity of Tourette's Syndrome through the Use of Animal Models

PubMed Central

Nespoli, Ester; Rizzo, Francesca; Boeckers, Tobias M.; Hengerer, Bastian; Ludolph, Andrea G.

2016-01-01

Tourette's syndrome (TS) is a neurodevelopmental disorder characterized by fluctuating motor and vocal tics, usually preceded by sensory premonitions, called premonitory urges. Besides tics, the vast majority—up to 90%—of TS patients suffer from psychiatric comorbidities, mainly attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). The etiology of TS remains elusive. Genetics is believed to play an important role, but it is clear that other factors contribute to TS, possibly altering brain functioning and architecture during a sensitive phase of neural development. Clinical brain imaging and genetic studies have contributed to elucidate TS pathophysiology and disease mechanisms; however, TS disease etiology still is poorly understood. Findings from genetic studies led to the development of genetic animal models, but they poorly reflect the pathophysiology of TS. Addressing the role of neurotransmission, brain regions, and brain circuits in TS disease pathomechanisms is another focus area for preclinical TS model development. We are now in an interesting moment in time when numerous innovative animal models are continuously brought to the attention of the public. Due to the diverse and largely unknown etiology of TS, there is no single preclinical model featuring all different aspects of TS symptomatology. TS has been dissected into its key symptomst hat have been investigated separately, in line with the Research Domain Criteria concept. The different rationales used to develop the respective animal models are critically reviewed, to discuss the potential of the contribution of animal models to elucidate TS disease mechanisms. PMID:27092043

Allium compounds, dipropyl and dimethyl thiosulfinates as antiproliferative and differentiating agents of human acute myeloid leukemia cell lines.

PubMed

Merhi, Faten; Auger, Jacques; Rendu, Francine; Bauvois, Brigitte

2008-12-01

Epidemiologic studies support the premise that Allium vegetables may lower the risk of cancers. The beneficial effects appear related to the organosulfur products generated upon processing of Allium. Leukemia cells from patients with acute myeloid leukemia (AML) display high proliferative capacity and have a reduced capacity of undergoing apoptosis and maturation. Whether the sulfur-containing molecules thiosulfinates (TS), diallyl TS (All(2)TS), dipropyl TS (Pr(2)TS) and dimethyl TS (Me(2)TS), are able to exert chemopreventative activity against AML is presently unknown. The present study was an evaluation of proliferation, cytotoxicity, differentiation and secretion of AML cell lines (U937, NB4, HL-60, MonoMac-6) in response to treatment with these TS and their related sulfides (diallylsulfide, diallyl disulfide, dipropyl disulfide, dimethyl disulfide). As assessed by flow cytometry, ELISA, gelatin zymogaphy and RT-PCR, we showed that Pr(2)TS and Me(2)TS, but not All(2)TS and sulfides, 1) inhibited cell proliferation in dose- and time-dependent manner and this process was neither due to cytotoxicity nor apoptosis, 2) induced macrophage maturation, and 3) inhibited the levels of secreted MMP-9 (protein and activity) and TNF-alpha protein, without altering mRNA levels. By establishing for the first time that Pr(2)TS and Me(2)TS affect proliferation, differentiation and secretion of leukemic cell lines, this study provides the opportunity to explore the potential efficiency of these molecules in AML.

Association of thymidylate synthase gene 3'-untranslated region polymorphism with sensitivity of non-small cell lung cancer to pemetrexed treatment: TS gene polymorphism and pemetrexed sensitivity in NSCLC.

PubMed

Wang, Xia; Wang, Yadi; Wang, Yue; Cheng, Jian; Wang, Yanyun; Ha, Minwen

2013-01-25

Thymidylate synthase (TS) is a key enzyme responsible for DNA synthesis and repair. Altered expression of TS protein or TS gene polymorphisms has been associated with cancer progression and treatment response. This study investigated the expressions of TS and its gene SNPs in non-small cell lung cancer (NSCLC), and then its association with sensitivity to pemetrexed treatment. Immunohistochemistry and qRT-PCR were performed on 160 resected NSCLC specimens and corresponding normal tissues to assess the expressions of TS protein and TS mRNA, and for associations with clinicopathological data. Blood samples of 106 lung adenocarcinoma patients were examined for polymorphisms of the TS gene 3'-UTR 1494del 6 bp, which was then investigated for associations with responses of the patients to pemetrexed treatment and survival. Expression of both TS protein and its mRNA was elevated in NSCLC tissues compared with matched normal tissues, and significantly higher in lung squamous cell carcinoma than in lung adenocarcinoma. TS expression was associated with poor tumor differentiation. Furthermore, the genotyping data showed that 56% of lung adenocarcinoma patients had the TS gene 3'-UTR 1494 bp (-6 bp/-6 bp) genotype and the rest had TS gene 3'-UTR 1494 bp (-6 bp/+6 bp). There was no TS 3'-UTR 1494 bp (+6 bp/+6 bp) genotype in any patients. Statistical analysis revealed that gender, tumor stage, and TS 3'-UTR 1494del 6 bp polymorphism were significant prognostic factors after short-term pemetrexed treatment. Log-rank analysis revealed that patients with the (-6 bp/-6 bp) genotype had significantly better progression-free and overall survival than patients with (-6 bp/+6 bp). This study showed that TS protein is highly expressed in NSCLC and that polymorphisms of TS 3'-UTR 1494del 6 bp are associated with sensitivity of lung adenocarcinoma patients to pemetrexed treatment. This suggests that TS gene polymorphisms should be further evaluated as prognostic markers for personalized therapy in lung adenocarcinoma.

Light-cone reduction vs. TsT transformations: a fluid dynamics perspective

NASA Astrophysics Data System (ADS)

Dutta, Suvankar; Krishna, Hare

2018-05-01

We compute constitutive relations for a charged (2+1) dimensional Schrödinger fluid up to first order in derivative expansion, using holographic techniques. Starting with a locally boosted, asymptotically AdS, 4 + 1 dimensional charged black brane geometry, we uplift that to ten dimensions and perform TsT transformations to obtain an effective five dimensional local black brane solution with asymptotically Schrödinger isometries. By suitably implementing the holographic techniques, we compute the constitutive relations for the effective fluid living on the boundary of this space-time and extract first order transport coefficients from these relations. Schrödinger fluid can also be obtained by reducing a charged relativistic conformal fluid over light-cone. It turns out that both the approaches result the same system at the end. Fluid obtained by light-cone reduction satisfies a restricted class of thermodynamics. Here, we see that the charged fluid obtained holographically also belongs to the same restricted class.

Natural gum-assisted phthalocyanine immobilization in electroactive nanocomposites: physicochemical characterization and sensing applications.

PubMed

Zampa, Maysa F; de Brito, Ana Cristina F; Kitagawa, Igor L; Constantino, Carlos J L; Oliveira, Osvaldo N; da Cunha, Helder N; Zucolotto, Valtencir; dos Santos, José Ribeiro; Eiras, Carla

2007-11-01

Natural gums have been traditionally applied in cosmetics and the food industry, mainly as emulsification agents. Due to their biodegradability and excellent mechanical properties, new technological applications have been proposed involving their use with conventional polymers forming blends and composites. In this study, we take advantage of the polyelectrolyte character exhibited by the natural gum ChichA (Sterculia striata), extracted in the Northeastern region of Brazil, to produce electroactive nanocomposites. The nanocomposites were fabricated in the form of ultrathin films by combining a metallic phthalocyanine (nickel tetrasulfonated phthalocyanine, NiTsPc) and the ChichA gum in a tetralayer architecture, in conjunction with conventional polyelectrolytes. The presence of the gum led to an efficient adsorption of the phthalocyanine and enhanced the electrochemical response of the films. Upon combining the electrochemical and UV-vis absorption data, energy diagrams of the ChichA/NiTsPc-based system were obtained. Furthermore, modified electrodes based on gum/phthalocyanine films were able to detect dopamine at concentrations as low as 10-5 M.

Fuzzy α-minimum spanning tree problem: definition and solutions

NASA Astrophysics Data System (ADS)

Zhou, Jian; Chen, Lu; Wang, Ke; Yang, Fan

2016-04-01

In this paper, the minimum spanning tree problem is investigated on the graph with fuzzy edge weights. The notion of fuzzy ? -minimum spanning tree is presented based on the credibility measure, and then the solutions of the fuzzy ? -minimum spanning tree problem are discussed under different assumptions. First, we respectively, assume that all the edge weights are triangular fuzzy numbers and trapezoidal fuzzy numbers and prove that the fuzzy ? -minimum spanning tree problem can be transformed to a classical problem on a crisp graph in these two cases, which can be solved by classical algorithms such as the Kruskal algorithm and the Prim algorithm in polynomial time. Subsequently, as for the case that the edge weights are general fuzzy numbers, a fuzzy simulation-based genetic algorithm using Prüfer number representation is designed for solving the fuzzy ? -minimum spanning tree problem. Some numerical examples are also provided for illustrating the effectiveness of the proposed solutions.

Solving the interval type-2 fuzzy polynomial equation using the ranking method

NASA Astrophysics Data System (ADS)

Rahman, Nurhakimah Ab.; Abdullah, Lazim

2014-07-01

Polynomial equations with trapezoidal and triangular fuzzy numbers have attracted some interest among researchers in mathematics, engineering and social sciences. There are some methods that have been developed in order to solve these equations. In this study we are interested in introducing the interval type-2 fuzzy polynomial equation and solving it using the ranking method of fuzzy numbers. The ranking method concept was firstly proposed to find real roots of fuzzy polynomial equation. Therefore, the ranking method is applied to find real roots of the interval type-2 fuzzy polynomial equation. We transform the interval type-2 fuzzy polynomial equation to a system of crisp interval type-2 fuzzy polynomial equation. This transformation is performed using the ranking method of fuzzy numbers based on three parameters, namely value, ambiguity and fuzziness. Finally, we illustrate our approach by numerical example.

Design of fuzzy system by NNs and realization of adaptability

NASA Technical Reports Server (NTRS)

Takagi, Hideyuki

1993-01-01

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

Short hairpin RNA suppression of thymidylate synthase produces DNA mismatches and results in excellent radiosensitization.

PubMed

Flanagan, Sheryl A; Cooper, Kristin S; Mannava, Sudha; Nikiforov, Mikhail A; Shewach, Donna S

2012-12-01

To determine the effect of short hairpin ribonucleic acid (shRNA)-mediated suppression of thymidylate synthase (TS) on cytotoxicity and radiosensitization and the mechanism by which these events occur. shRNA suppression of TS was compared with 5-fluoro-2'-deoxyuridine (FdUrd) inactivation of TS with or without ionizing radiation in HCT116 and HT29 colon cancer cells. Cytotoxicity and radiosensitization were measured by clonogenic assay. Cell cycle effects were measured by flow cytometry. The effects of FdUrd or shRNA suppression of TS on dNTP deoxynucleotide triphosphate imbalances and consequent nucleotide misincorporations into deoxyribonucleic acid (DNA) were analyzed by high-pressure liquid chromatography and as pSP189 plasmid mutations, respectively. TS shRNA produced profound (≥ 90%) and prolonged (≥ 8 days) suppression of TS in HCT116 and HT29 cells, whereas FdUrd increased TS expression. TS shRNA also produced more specific and prolonged effects on dNTPs deoxynucleotide triphosphates compared with FdUrd. TS shRNA suppression allowed accumulation of cells in S-phase, although its effects were not as long-lasting as those of FdUrd. Both treatments resulted in phosphorylation of Chk1. TS shRNA alone was less cytotoxic than FdUrd but was equally effective as FdUrd in eliciting radiosensitization (radiation enhancement ratio: TS shRNA, 1.5-1.7; FdUrd, 1.4-1.6). TS shRNA and FdUrd produced a similar increase in the number and type of pSP189 mutations. TS shRNA produced less cytotoxicity than FdUrd but was equally effective at radiosensitizing tumor cells. Thus, the inhibitory effect of FdUrd on TS alone is sufficient to elicit radiosensitization with FdUrd, but it only partially explains FdUrd-mediated cytotoxicity and cell cycle inhibition. The increase in DNA mismatches after TS shRNA or FdUrd supports a causal and sufficient role for the depletion of dTTP thymidine triphosphate and consequent DNA mismatches underlying radiosensitization. Importantly, shRNA suppression of TS avoids FP-mediated TS elevation and its negative prognostic role. These studies support the further exploration of TS suppression as a novel radiosensitizing strategy. Copyright © 2012 Elsevier Inc. All rights reserved.

Analysis of 320 gastroenteropancreatic neuroendocrine tumors identifies TS expression as independent biomarker for survival.

PubMed

Lee, Hye Seung; Chen, Min; Kim, Ji Hun; Kim, Woo Ho; Ahn, Soyeon; Maeng, Kyungah; Allegra, Carmen J; Kaye, Frederic J; Hochwald, Steven N; Zajac-Kaye, Maria

2014-07-01

Thymidylate synthase (TS), a critical enzyme for DNA synthesis and repair, is both a potential tumor prognostic biomarker as well as a tumorigenic oncogene in animal models. We have now studied the clinical implications of TS expression in gastroenteropancreatic (GEP) neuroendocrine tumors (NETs) and compared these results to other cell cycle biomarker genes. Protein tissue arrays were used to study TS, Ki-67, Rb, pRb, E2F1, p18, p21, p27 and menin expression in 320 human GEP-NETs samples. Immunohistochemical expression was correlated with univariate and multivariate predictors of survival utilizing Kaplan Meier and Cox proportional hazards models. Real time RT-PCR was used to validate these findings. We found that 78 of 320 GEP-NETs (24.4%) expressed TS. NETs arising in the colon, stomach and pancreas showed the highest expression of TS (47.4%, 42.6% and 37.3%, respectively), whereas NETs of the appendix, rectum and duodenum displayed low TS expression (3.3%, 12.9% and 15.4%, respectively). TS expression in GEP-NETs was associated with poorly differentiated endocrine carcinoma, angiolymphatic invasion, lymph node metastasis and distant metastasis (p < 0.05). Patients with TS-positive NETs had markedly worse outcomes than TS-negative NETs as shown by univariate (p < 0.001) and multivariate (p = 0.01) survival analyses. Expression of p18 predicted survival in TS-positive patients that received chemotherapy (p = 0.015). In conclusion, TS protein expression was an independent prognostic biomarker for GEP-NETs. The strong association of increased TS expression with aggressive disease and early death supports the role of TS as a cancer promoting agent in these tumors. © 2013 UICC.

The relationship of periaortic fat thickness and cardiovascular risk factors in children with Turner syndrome.

PubMed

Akyürek, Nesibe; Atabek, Mehmet Emre; Eklioglu, Beray Selver; Alp, Hayrullah

2015-06-01

Children with Turner syndrome (TS) have a broad range of later health problems, including an increased risk of cardiovascular morbidity and mortality. The aim of this study was to evaluate the relationship between periaortic fat thickness (PAFT) and metabolic and cardiovascular profiles in children with TS. Twenty-nine TS and 29 healthy children and adolescents were enrolled in the study. Anthropometric measurements, pubertal staging, and blood pressure measurements were performed. Fasting serum glucose, insulin, and lipid profile were measured. Periaortic fat thickness was measured using an echocardiography method, which has not previously been applied in children with TS. No difference was found between TS and control subject (CS) in age, weight, waist/hip ratio, HDL cholesterol and LDL cholesterol levels. However, in TS subjects, total cholesterol (p = 0.045) was greater than that in controls. It was determined that 13.7 % (N: 4) of TS subjects had dyslipidemia. Mean fasting glucose, fasting insulin, QUICK-I, HOMA, and FGIR index were similar in TS and in CS, whereas 17.2 % (N: 5) of TS subjects had insulin resistance (IR) and 13.7 % (N: 4) had impaired glucose tolerance. Six subjects (20.6 %) were diagnosed as hypertensive. Periaortic fat thickness was significantly higher in the TS group (p < 0.001) (0.1694 ± 0.025 mm in the TS group and 0.1416 ± 0.014 mm in the CS group) In children with TS, PAFT was positively correlated with fasting insulin, body mass index, and diastolic blood pressure. Our results provide additional evidence for the presence of subclinical cardiovascular disease in TS. In addition to existing methods, we recommend the measurement of periaortic fat thickness in children with TS to reveal the presence of early atherosclerosis.

A Comparison of Tropical Storm (TS) and Non-TS Gust Factors for Assessing Peak Wind Probabilities at the Eastern Range

NASA Technical Reports Server (NTRS)

Merceret, Francis J.; Crawford, Winifred C.

2010-01-01

Knowledge of peak wind speeds is important to the safety of personnel and flight hardware at Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS), but they are more difficult to forecast than mean wind speeds. Development of a reliable model for the gust factor (GF) relating the peak to the mean wind speed motivated a previous study of GF in tropical storms. The same motivation inspired a climatological study of non-TS peak wind speed statistics without the use of GF. Both studies presented their respective statistics as functions of mean wind speed and height. The few comparisons of IS and non-TS GF in the literature suggest that the non-TS GF at a given height and mean wind speed are smaller than the corresponding TS GF. The investigation reported here converted the non-TS peak wind statistics mentioned above to the equivalent GF statistics and compared the results with the previous TS GF results. The advantage of this effort over all previously reported studies of its kind is that the TS and non-TS data are taken from the same towers in the same locations. That eliminates differing surface attributes, including roughness length and thermal properties, as a major source of variance in the comparison. The results are consistent with the literature, but include much more detailed, quantitative information on the nature of the relationship between TS and non-TS GF as a function of height and mean wind speed. In addition, the data suggest the possibility of providing an operational model for non-TS GF as a function of height and wind speed in a manner similar to the one previously developed for TS GF.

A 6-year Follow-up survey of health status in middle-aged women with Turner syndrome.

PubMed

Fjermestad, Krister W; Naess, Eva E; Bahr, David; Gravholt, Claus H

2016-09-01

Studies suggest younger women with Turner syndrome (TS) have good quality of life. Less is known about everyday functioning in adults with TS. In a 6-year follow-up study, multiple areas of functioning were compared between TS women and controls. Women with TS and controls were mailed a self-report survey 6 years after a baseline study. Fifty-seven women with TS (M age 40·6 ± 11·1 years) and 101 controls (M age 38·8 ± 10·6 years, ns) responded. Measures of background information, experienced life strain and presence/impact of health conditions were developed for this study. The QPS Nordic measured perceived workload challenges. The LiSat-9 measured life satisfaction. The Rosenberg Self-Esteem Scale measured self-esteem. More TS women lived alone, fewer had biological children, and more had adoptive children. TS women reported fewer sex partners and less sexual confidence. Controls had higher education. There was no difference in employment status. More TS women received disability pensions. TS women reported their work as more physically challenging, less positively challenging and requiring less knowledge skills. TS women experienced more life strain in school, adolescence and late working life. Controls reported higher overall life satisfaction, with no difference between samples on specific domains. TS women reported lower self-esteem. For TS women only, physical health at baseline predicted length of education and mental health at baseline predicted self-esteem. Women with TS face more challenges than controls on several domains of functioning. Early physical and mental health may influence later educational achievement and self-esteem for women with TS. © 2016 John Wiley & Sons Ltd.

Mechanisms of dopaminergic and serotonergic neurotransmission in Tourette syndrome: clues from an in vivo neurochemistry study with PET.

PubMed

Wong, Dean F; Brasić, James R; Singer, Harvey S; Schretlen, David J; Kuwabara, Hiroto; Zhou, Yun; Nandi, Ayon; Maris, Marika A; Alexander, Mohab; Ye, Weiguo; Rousset, Olivier; Kumar, Anil; Szabo, Zsolt; Gjedde, Albert; Grace, Anthony A

2008-05-01

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by motor and phonic tics. Obsessive-compulsive disorder (OCD) is often concomitant with TS. Dysfunctional tonic and phasic dopamine (DA) and serotonin (5-HT) metabolism may play a role in the pathophysiology of TS. We simultaneously measured the density, affinity, and brain distribution of dopamine D2 receptors (D2-R's), dopamine transporter binding potential (BP), and amphetamine-induced dopamine release (DA(rel)) in 14 adults with TS and 10 normal adult controls. We also measured the brain distribution and BP of serotonin 5-HT2A receptors (5-HT2AR), and serotonin transporter (SERT) BP, in 11 subjects with TS and 10 normal control subjects. As compared with controls, DA rel was significantly increased in the ventral striatum among subjects with TS. Adults with TS+OCD exhibited a significant D(2)-R increase in left ventral striatum. SERT BP in midbrain and caudate/putamen was significantly increased in adults with TS (TS+OCD and TS-OCD). In three subjects with TS+OCD, in whom D2-R, 5-HT2AR, and SERT were measured within a 12-month period, there was a weakly significant elevation of DA rel and 5-HT2A BP, when compared with TS-OCD subjects and normal controls. The current study confirms, with a larger sample size and higher resolution PET scanning, our earlier report that elevated DA rel is a primary defect in TS. The finding of decreased SERT BP, and the possible elevation in 5-HT2AR in individuals with TS who had increased DA rel, suggest a condition of increased phasic DA rel modulated by low 5-HT in concomitant OCD.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

NASA Technical Reports Server (NTRS)

Wang, Aihui; Barlage, Michael; Zeng, Xubin; Draper, Clara Sophie

2014-01-01

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

Transportation optimization with fuzzy trapezoidal numbers based on possibility theory.

PubMed

He, Dayi; Li, Ran; Huang, Qi; Lei, Ping

2014-01-01

In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods.

Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters

PubMed Central

Dewal, M. L.; Rohit, Manoj Kumar

2014-01-01

Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images. PMID:27437499

Class dependency of fuzzy relational database using relational calculus and conditional probability

NASA Astrophysics Data System (ADS)

Deni Akbar, Mohammad; Mizoguchi, Yoshihiro; Adiwijaya

2018-03-01

In this paper, we propose a design of fuzzy relational database to deal with a conditional probability relation using fuzzy relational calculus. In the previous, there are several researches about equivalence class in fuzzy database using similarity or approximate relation. It is an interesting topic to investigate the fuzzy dependency using equivalence classes. Our goal is to introduce a formulation of a fuzzy relational database model using the relational calculus on the category of fuzzy relations. We also introduce general formulas of the relational calculus for the notion of database operations such as ’projection’, ’selection’, ’injection’ and ’natural join’. Using the fuzzy relational calculus and conditional probabilities, we introduce notions of equivalence class, redundant, and dependency in the theory fuzzy relational database.

The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller

NASA Technical Reports Server (NTRS)

Sultan, Labib; Janabi, Talib

1992-01-01

This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.

Real-time seam tracking control system based on line laser visions

NASA Astrophysics Data System (ADS)

Zou, Yanbiao; Wang, Yanbo; Zhou, Weilin; Chen, Xiangzhi

2018-07-01

A set of six-degree-of-freedom robotic welding automatic tracking platform was designed in this study to realize the real-time tracking of weld seams. Moreover, the feature point tracking method and the adaptive fuzzy control algorithm in the welding process were studied and analyzed. A laser vision sensor and its measuring principle were designed and studied, respectively. Before welding, the initial coordinate values of the feature points were obtained using morphological methods. After welding, the target tracking method based on Gaussian kernel was used to extract the real-time feature points of the weld. An adaptive fuzzy controller was designed to input the deviation value of the feature points and the change rate of the deviation into the controller. The quantization factors, scale factor, and weight function were adjusted in real time. The input and output domains, fuzzy rules, and membership functions were constantly updated to generate a series of smooth bias robot voltage. Three groups of experiments were conducted on different types of curve welds in a strong arc and splash noise environment using the welding current of 120 A short-circuit Metal Active Gas (MAG) Arc Welding. The tracking error was less than 0.32 mm and the sensor's metrical frequency can be up to 20 Hz. The end of the torch run smooth during welding. Weld trajectory can be tracked accurately, thereby satisfying the requirements of welding applications.

Using artificial intelligence to improve identification of nanofluid gas-liquid two-phase flow pattern in mini-channel

NASA Astrophysics Data System (ADS)

Xiao, Jian; Luo, Xiaoping; Feng, Zhenfei; Zhang, Jinxin

2018-01-01

This work combines fuzzy logic and a support vector machine (SVM) with a principal component analysis (PCA) to create an artificial-intelligence system that identifies nanofluid gas-liquid two-phase flow states in a vertical mini-channel. Flow-pattern recognition requires finding the operational details of the process and doing computer simulations and image processing can be used to automate the description of flow patterns in nanofluid gas-liquid two-phase flow. This work uses fuzzy logic and a SVM with PCA to improve the accuracy with which the flow pattern of a nanofluid gas-liquid two-phase flow is identified. To acquire images of nanofluid gas-liquid two-phase flow patterns of flow boiling, a high-speed digital camera was used to record four different types of flow-pattern images, namely annular flow, bubbly flow, churn flow, and slug flow. The textural features extracted by processing the images of nanofluid gas-liquid two-phase flow patterns are used as inputs to various identification schemes such as fuzzy logic, SVM, and SVM with PCA to identify the type of flow pattern. The results indicate that the SVM with reduced characteristics of PCA provides the best identification accuracy and requires less calculation time than the other two schemes. The data reported herein should be very useful for the design and operation of industrial applications.

Computer-aided bone age assessment for ethnically diverse older children using integrated fuzzy logic system

NASA Astrophysics Data System (ADS)

Ma, Kevin; Moin, Paymann; Zhang, Aifeng; Liu, Brent

2010-03-01

Bone Age Assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on the left hand x-ray radiograph. The current BAA standard in the US is using the Greulich & Pyle (G&P) Hand Atlas, which was developed fifty years ago and was only based on Caucasian population from the Midwest US. To bring the BAA procedure up-to-date with today's population, a Digital Hand Atlas (DHA) consisting of 1400 hand images of normal children of different ethnicities, age, and gender. Based on the DHA and to solve inter- and intra-observer reading discrepancies, an automatic computer-aided bone age assessment system has been developed and tested in clinical environments. The algorithm utilizes features extracted from three regions of interests: phalanges, carpal, and radius. The features are aggregated into a fuzzy logic system, which outputs the calculated bone age. The previous BAA system only uses features from phalanges and carpal, thus BAA result for children over age of 15 is less accurate. In this project, the new radius features are incorporated into the overall BAA system. The bone age results, calculated from the new fuzzy logic system, are compared against radiologists' readings based on G&P atlas, and exhibits an improvement in reading accuracy for older children.

Prescribed burning impact on forest soil properties--a Fuzzy Boolean Nets approach.

PubMed

Castro, Ana C Meira; Paulo Carvalho, Joao; Ribeiro, S

2011-02-01

The Portuguese northern forests are often and severely affected by wildfires during the Summer season. These occurrences significantly affect and negatively impact all ecosystems, namely soil, fauna and flora. In order to reduce the occurrences of natural wildfires, some measures to control the availability of fuel mass are regularly implemented. Those preventive actions concern mainly prescribed burnings and vegetation pruning. This work reports on the impact of a prescribed burning on several forest soil properties, namely pH, soil moisture, organic matter content and iron content, by monitoring the soil self-recovery capabilities during a one year span. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, which was kept intact from prescribed burnings during a period of four years. Soil samples were collected from five plots at three different layers (0-3, 3-6 and 6-18) 1 day before prescribed fire and at regular intervals after the prescribed fire. This paper presents an approach where Fuzzy Boolean Nets (FBN) and Fuzzy reasoning are used to extract qualitative knowledge regarding the effect of prescribed fire burning on soil properties. FBN were chosen due to the scarcity on available quantitative data. The results showed that soil properties were affected by prescribed burning practice and were unable to recover their initial values after one year. Copyright © 2010 Elsevier Inc. All rights reserved.

Combinational Reasoning of Quantitative Fuzzy Topological Relations for Simple Fuzzy Regions

PubMed Central

Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

2015-01-01

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design.

PubMed

Juang, C F; Lin, J Y; Lin, C T

2000-01-01

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

From Phenomena to Objects: Segmentation of Fuzzy Objects and its Application to Oceanic Eddies

NASA Astrophysics Data System (ADS)

Wu, Qingling

A challenging image analysis problem that has received limited attention to date is the isolation of fuzzy objects---i.e. those with inherently indeterminate boundaries---from continuous field data. This dissertation seeks to bridge the gap between, on the one hand, the recognized need for Object-Based Image Analysis of fuzzy remotely sensed features, and on the other, the optimization of existing image segmentation techniques for the extraction of more discretely bounded features. Using mesoscale oceanic eddies as a case study of a fuzzy object class evident in Sea Surface Height Anomaly (SSHA) imagery, the dissertation demonstrates firstly, that the widely used region-growing and watershed segmentation techniques can be optimized and made comparable in the absence of ground truth data using the principle of parsimony. However, they both have significant shortcomings, with the region growing procedure creating contour polygons that do not follow the shape of eddies while the watershed technique frequently subdivides eddies or groups together separate eddy objects. Secondly, it was determined that these problems can be remedied by using a novel Non-Euclidian Voronoi (NEV) tessellation technique. NEV is effective in isolating the extrema associated with eddies in SSHA data while using a non-Euclidian cost-distance based procedure (based on cumulative gradients in ocean height) to define the boundaries between fuzzy objects. Using this procedure as the first stage in isolating candidate eddy objects, a novel "region-shrinking" multicriteria eddy identification algorithm was developed that includes consideration of shape and vorticity. Eddies identified by this region-shrinking technique compare favorably with those identified by existing techniques, while simplifying and improving existing automated eddy detection algorithms. However, it also tends to find a larger number of eddies as a result of its ability to separate what other techniques identify as connected eddies. The research presented here is of significance not only to eddy research in oceanography, but also to other areas of Earth System Science for which the automated detection of features lacking rigid boundary definitions is of importance.

A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic

NASA Astrophysics Data System (ADS)

Al-Mousa, Amjed A.

Thin films are essential constituents of modern electronic devices and have a multitude of applications in such devices. The impact of the surface morphology of thin films on the device characteristics where these films are used has generated substantial attention to advanced film characterization techniques. In this work, we present a new approach to characterize surface nanostructures of thin films by focusing on isolating nanostructures and extracting quantitative information, such as the shape and size of the structures. This methodology is applicable to any Scanning Probe Microscopy (SPM) data, such as Atomic Force Microscopy (AFM) data which we are presenting here. The methodology starts by compensating the AFM data for some specific classes of measurement artifacts. After that, the methodology employs two distinct techniques. The first, which we call the overlay technique, proceeds by systematically processing the raster data that constitute the scanning probe image in both vertical and horizontal directions. It then proceeds by classifying points in each direction separately. Finally, the results from both the horizontal and the vertical subsets are overlaid, where a final decision on each surface point is made. The second technique, based on fuzzy logic, relies on a Fuzzy Inference Engine (FIE) to classify the surface points. Once classified, these points are clustered into surface structures. The latter technique also includes a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and then tune the fuzzy technique system uniquely for that surface. Both techniques have been applied to characterize organic semiconductor thin films of pentacene on different substrates. Also, we present a case study to demonstrate the effectiveness of our methodology to identify quantitatively particle sizes of two specimens of gold nanoparticles of different nominal dimensions dispersed on a mica surface. A comparison with other techniques like: thresholding, watershed and edge detection is presented next. Finally, we present a systematic study of the fuzzy logic technique by experimenting with synthetic data. These results are discussed and compared along with the challenges of the two techniques.

Molecular, Immunological, and Biological Characterization of Tityus serrulatus Venom Hyaluronidase: New Insights into Its Role in Envenomation

PubMed Central

Oliveira-Mendes, Bárbara Bruna Ribeiro; do Carmo, Anderson Oliveira; Duarte, Clara Guerra; Felicori, Liza Figueiredo; Machado-de-Ávila, Ricardo Andrez; Chávez-Olórtegui, Carlos; Kalapothakis, Evanguedes

2014-01-01

Background Scorpionism is a public health problem in Brazil, and Tityus serrulatus (Ts) is primarily responsible for severe accidents. The main toxic components of Ts venom are low-molecular-weight neurotoxins; however, the venom also contains poorly characterized high-molecular-weight enzymes. Hyaluronidase is one such enzyme that has been poorly characterized. Methods and principal findings We examined clones from a cDNA library of the Ts venom gland and described two novel isoforms of hyaluronidase, TsHyal-1 and TsHyal-2. The isoforms are 83% identical, and alignment of their predicted amino acid sequences with other hyaluronidases showed conserved residues between evolutionarily distant organisms. We performed gel filtration followed by reversed-phase chromatography to purify native hyaluronidase from Ts venom. Purified native Ts hyaluronidase was used to produce anti-hyaluronidase serum in rabbits. As little as 0.94 µl of anti-hyaluronidase serum neutralized 1 LD50 (13.2 µg) of Ts venom hyaluronidase activity in vitro. In vivo neutralization assays showed that 121.6 µl of anti-hyaluronidase serum inhibited mouse death 100%, whereas 60.8 µl and 15.2 µl of serum delayed mouse death. Inhibition of death was also achieved by using the hyaluronidase pharmacological inhibitor aristolochic acid. Addition of native Ts hyaluronidase (0.418 µg) to pre-neutralized Ts venom (13.2 µg venom+0.94 µl anti-hyaluronidase serum) reversed mouse survival. We used the SPOT method to map TsHyal-1 and TsHyal-2 epitopes. More peptides were recognized by anti-hyaluronidase serum in TsHyal-1 than in TsHyal-2. Epitopes common to both isoforms included active site residues. Conclusions Hyaluronidase inhibition and immunoneutralization reduced the toxic effects of Ts venom. Our results have implications in scorpionism therapy and challenge the notion that only neurotoxins are important to the envenoming process. PMID:24551256

A Combination of Extended Fuzzy AHP and Fuzzy GRA for Government E-Tendering in Hybrid Fuzzy Environment

PubMed Central

Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

2014-01-01

The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. PMID:25057506

A combination of extended fuzzy AHP and fuzzy GRA for government E-tendering in hybrid fuzzy environment.

PubMed

Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

2014-01-01

The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach.

Fuzzy tree automata and syntactic pattern recognition.

PubMed

Lee, E T

1982-04-01

An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

Crystal structures of nematode (parasitic T. spiralis and free living C. elegans), compared to mammalian, thymidylate synthases (TS). Molecular docking and molecular dynamics simulations in search for nematode-specific inhibitors of TS.

PubMed

Jarmuła, Adam; Wilk, Piotr; Maj, Piotr; Ludwiczak, Jan; Dowierciał, Anna; Banaszak, Katarzyna; Rypniewski, Wojciech; Cieśla, Joanna; Dąbrowska, Magdalena; Frączyk, Tomasz; Bronowska, Agnieszka K; Jakowiecki, Jakub; Filipek, Sławomir; Rode, Wojciech

2017-10-01

Three crystal structures are presented of nematode thymidylate synthases (TS), including Caenorhabditis elegans (Ce) enzyme without ligands and its ternary complex with dUMP and Raltitrexed, and binary complex of Trichinella spiralis (Ts) enzyme with dUMP. In search of differences potentially relevant for the development of species-specific inhibitors of the nematode enzyme, a comparison was made of the present Ce and Ts enzyme structures, as well as binary complex of Ce enzyme with dUMP, with the corresponding mammalian (human, mouse and rat) enzyme crystal structures. To complement the comparison, tCONCOORD computations were performed to evaluate dynamic behaviors of mammalian and nematode TS structures. Finally, comparative molecular docking combined with molecular dynamics and free energy of binding calculations were carried out to search for ligands showing selective affinity to T. spiralis TS. Despite an overall strong similarity in structure and dynamics of nematode vs mammalian TSs, a pool of ligands demonstrating predictively a strong and selective binding to TsTS has been delimited. These compounds, the E63 family, locate in the dimerization interface of TsTS where they exert species-specific interactions with certain non-conserved residues, including hydrogen bonds with Thr174 and hydrophobic contacts with Phe192, Cys191 and Tyr152. The E63 family of ligands opens the possibility of future development of selective inhibitors of TsTS and effective agents against trichinellosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

NASA Astrophysics Data System (ADS)

Sinuk, V. G.; Panchenko, M. V.

2018-03-01

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.

A fuzzy inventory model with acceptable shortage using graded mean integration value method

NASA Astrophysics Data System (ADS)

Saranya, R.; Varadarajan, R.

2018-04-01

In many inventory models uncertainty is due to fuzziness and fuzziness is the closed possible approach to reality. In this paper, we proposed a fuzzy inventory model with acceptable shortage which is completely backlogged. We fuzzily the carrying cost, backorder cost and ordering cost using Triangular and Trapezoidal fuzzy numbers to obtain the fuzzy total cost. The purpose of our study is to defuzzify the total profit function by Graded Mean Integration Value Method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models.

Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm.

PubMed

Kaga, Chiaki; Okochi, Mina; Tomita, Yasuyuki; Kato, Ryuji; Honda, Hiroyuki

2008-03-01

We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.

Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: a case study in central Taiwan.

PubMed

Chou, Wen-Chieh; Lin, Wen-Tzu; Lin, Chao-Yuan

2009-05-01

The catastrophic earthquake, 7.3 on the Richter scale, occurred on September 21, 1999 in Central Taiwan. Much of standing vegetation on slopes was eliminated and massive, scattered landslides were induced at the Jou-Jou Mountain area of the Wu-Chi basin in Nantou County. We evaluated three methods for assessing landslide hazard and vegetation recovery conditions. (1) Self-organizing map (SOM) neural network coupled with fuzzy technique was used to quickly extract the landslide. (2) The NDVI-based vegetation recovery index derived from multi-temporal SPOT satellite images was used to evaluate vegetation recovery rate in the denudation sites. (3) The spatial distribution index (SDI) based on land-cover topographic location was employed to analyze vegetation recovery patterns, including the invading, surviving and mixed patterns at the Jou-Jou Mountain area. On September 27, 1999, there were 849.20 ha of landslide area extracted using the self-organizing map and fuzzy technique combined model. After six years of natural vegetation succession, the landslide has gradually restored, and vegetation recovery rate reached up to 86%. On-site observation shows that many native pioneer plants have invaded onto the denudation sites even if disturbed by several typhoons. Two native surviving plants, Arundo formosana Hack and Pinus taiwanensis Hayata, play a vital role in natural vegetation succession in this area, especially for the sites on ridgeline and steep slopes.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

PubMed

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

PubMed

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-10-09

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

PubMed Central

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-01-01

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method. PMID:26450665

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

PubMed

Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

2017-09-01

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

Learning and Tuning of Fuzzy Rules

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Fuzzy logic in control systems: Fuzzy logic controller. I, II

NASA Technical Reports Server (NTRS)

Lee, Chuen Chien

1990-01-01

Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

Fuzzy model-based observers for fault detection in CSTR.

PubMed

Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

2015-11-01

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A neural fuzzy controller learning by fuzzy error propagation

NASA Technical Reports Server (NTRS)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

Differences in Medial and Lateral Posterior Tibial Slope: An Osteological Review of 1090 Tibiae Comparing Age, Sex, and Race.

PubMed

Weinberg, Douglas S; Williamson, Drew F K; Gebhart, Jeremy J; Knapik, Derrick M; Voos, James E

2017-01-01

Injuries to the anterior cruciate ligament (ACL) are common, and a number of knee morphological variables have been identified as risk factors for an ACL injury, including the posterior tibial slope (TS). However, limited data exist regarding innate population differences in the TS. To (1) establish normative values for the medial and lateral posterior TS; (2) determine what differences exist between ages, sexes, and races; and (3) determine how internal or external tibial rotation (as occurs during sagittal knee motion) influences the stereotactic perception of the TS. Cross-sectional study; Level of evidence, 3. A total of 545 cadaveric specimens (1090 tibiae) were obtained from the Hamann-Todd osteological collection. Specimens were leveled in the coronal, sagittal, and axial planes using a digital laser. Virtual representations of each bone were created with a 3-dimensional digitizer apparatus. The TS of the medial and lateral tibial plateaus were measured using techniques adapted from previous radiographic protocols. Medial and lateral TS were then again measured on 200 tibiae that were internally and externally rotated by 10° (axially). The mean (±SD) medial TS was 6.9° ± 3.7° posterior, which was greater than the mean lateral TS of 4.7° ± 3.6° posterior ( P < .001). Neither the medial nor lateral TS changed with age. Women had a greater mean TS compared with men on both the medial (7.5° ± 3.8° vs 6.8° ± 3.7°, respectively; P = .03) and lateral (5.2° ± 3.5° vs 4.6° ± 3.5°, respectively; P = .04) sides. Black specimens had a greater mean medial TS (8.7° ± 3.6° vs 5.8° ± 3.3°, respectively; P < .001) and lateral TS (5.9° ± 3.3° vs 3.8° ± 3.5°, respectively; P < .001) compared with white specimens. Axial rotation was shown to increase the perception of the medial and lateral TS ( P < .001). The medial TS was shown to be greater than the lateral TS. Important sex- and race-based differences exist in the TS. This study also highlights the role of axial rotation in measuring the TS.

A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System

PubMed Central

Tang, Yongchuan; Zhou, Deyun

2016-01-01

In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method. PMID:27482707

A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.

PubMed

Tang, Yongchuan; Zhou, Deyun; Jiang, Wen

2016-01-01

In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method.

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

PubMed

Kim, J; Kasabov, N

1999-11-01

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

Using fuzzy data mining to diagnose patients' degrees of melancholia

NASA Astrophysics Data System (ADS)

Huang, Yo-Ping; Kuo, Wen-Lin

2011-06-01

The common treatments of melancholia are psychotherapy and taking medicines. The psychotherapy treatment which this study focuses on is limited by time and location. It is easier for psychiatrists to grasp information from clinical manifestation but it is difficult for psychiatrists to collect information from patients' daily conversations or emotion. To design a system which psychiatrists enable to capture patients' daily symptoms will show great help in the treatment. This study proposes to use fuzzy data mining algorithm to find association rules among keywords segmented from patients' daily voice/text messages to assist psychiatrists extract useful information before outpatient service. Patients of melancholia can use devices such as mobile phones or computers to record their own emotion anytime and anywhere and then uploading the recorded files to the back-end server for further analysis. The analytical results can be used for psychiatrists to diagnose patients' degrees of melancholia. Experimental results will be given to verify the effectiveness of the proposed methodology.

Prospectivity Modeling of Karstic Groundwater Using a Sequential Exploration Approach in Tepal Area, Iran

NASA Astrophysics Data System (ADS)

Sharifi, Fereydoun; Arab-Amiri, Ali Reza; Kamkar-Rouhani, Abolghasem; Yousefi, Mahyar; Davoodabadi-Farahani, Meysam

2017-09-01

The purpose of this study is water prospectivity modeling (WPM) for recognizing karstic water-bearing zones by using analyses of geo-exploration data in Kal-Qorno valley, located in Tepal area, north of Iran. For this, a sequential exploration method applied on geo-evidential data to delineate target areas for further exploration. In this regard, two major exploration phases including regional and local scales were performed. In the first phase, indicator geological features, structures and lithological units, were used to model groundwater prospectivity as a regional scale. In this phase, for karstic WPM, fuzzy lithological and structural evidence layers were generated and combined using fuzzy operators. After generating target areas using WPM, in the second phase geophysical surveys including gravimetry and geoelectrical resistivity were carried out on the recognized high potential zones as a local scale exploration. Finally the results of geophysical analyses in the second phase were used to select suitable drilling locations to access and extract karstic groundwater in the study area.

Fuzzy logic-based approach to detecting a passive RFID tag in an outpatient clinic.

PubMed

Min, Daiki; Yih, Yuehwern

2011-06-01

This study is motivated by the observations on the data collected by radio frequency identification (RFID) readers in a pilot study, which was used to investigate the feasibility of implementing an RFID-based monitoring system in an outpatient eye clinic. The raw RFID data collected from RFID readers contain noise and missing reads, which prevent us from determining the tag location. In this paper, fuzzy logic-based algorithms are proposed to interpret the raw RFID data to extract accurate information. The proposed algorithms determine the location of an RFID tag by evaluating its possibility of presence and absence. To evaluate the performance of the proposed algorithms, numerical experiments are conducted using the data observed in the outpatient eye clinic. Experiments results showed that the proposed algorithms outperform existing static smoothing method in terms of minimizing both false positives and false negatives. Furthermore, the proposed algorithms are applied to a set of simulated data to show the robustness of the proposed algorithms at various levels of RFID reader reliability.

Selected clinical features of the head and neck in women with Turner syndrome and the 45,X/46,XY karyotype.

PubMed

Frelich, Agnieszka; Frelich, Jakub; Jeż, Wacław; Irzyniec, Tomasz

2017-01-01

A 45,X/46,XY karyotype in women with Turner syndrome (TS) is very rare. The presence of a Y chromosome in the karyotype causes phenotypic differences and increased risk for neoplastic disease, compared to TS-women with other karyotypes. Our study addresses an issue: non-genital phenotypic differences between TS-patients with a Y-chromosome of their karyotype and TS-women without it. Results from patient history/physical examinations of the head and neck of eight TS-women and the 45,X/46,XY karyotype were compared with those observed in 164 TS-women and 30 controls. The heights of TS-groups: 142.5 ± 7.2 and 144.9 ± 7.2 cm were lower than controls (165.2 ± 6.6 cm). Participants were examined from 1995 to 2014. Among 28 study parameters, 15 were more frequently observed in TS women with the 45,X/46,XY karyotype compared to controls. Only abnormalities in the oral cavity and a history of childhood lymphoedema, differed significantly in the TS groups. With respect to the head and neck, the patient history and physical examination results of TS-women and the 45,X/46,XY karyotype and TS and other karyotypes revealed similar differences compared to controls. Compared to others TS patients, 45,X/46,XY individuals might more frequently have oral cavity soft tissue abnormalities and more rarely a history of childhood lymphoedema. (Endokrynol Pol 2017; 68 (1): 47-52).

Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

ERIC Educational Resources Information Center

Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

2016-01-01

The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

Comparison of Fuzzy-Based Models in Landslide Hazard Mapping

NASA Astrophysics Data System (ADS)

Mijani, N.; Neysani Samani, N.

2017-09-01

Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

Development of Candidate Chemical Simulant List: The Evaluation of Candidate Chemical Simulants Which May Be Used in Chemically Hazardous Operations

DTIC Science & Technology

1982-12-01

generation FDA Food and Drug Administration (U.S.A.) FEMA Flavoring Extract Manufacturer’s Associatic. FID Flame ionization detector FPD Flame...medicinally in the form of local analgesic or anti-inflammatory ointmer,ts or liniments S (Collins et al., 1971). It was given GRAS status by the Flavor ...methyl salicylate is considered safe for use as a flavoring agent in various foods when added in low concentrations, it has been found to be acutely

Modified natural porcine surfactant modulates tobacco smoke-induced stress response in human monocytes.

PubMed

Pinot, F; Bachelet, M; François, D; Polla, B S; Walti, H

1999-01-01

Tobacco smoke (TS) is a potent source of oxidants and oxidative stress is an important mechanism by which TS exerts its toxicity in the lung. We have shown that TS induces heat shock (HS)/stress protein (HSP) synthesis in human monocytes. Pulmonary surfactant (PS) whose major physiological function is to confer mechanical stability to alveoli, also modulates oxidative metabolism and other pro-inflammatory functions of monocytes-macrophages. In order to determine whether PS alters the stress response induced by TS, we incubated human peripheral blood monocytes overnight with modified natural porcine surfactant (Curosurf) (1 mg/ml) before exposure to TS. Curosurf decreased TS-induced, but not HS-induced, expression of the major cytosolic, inducible 72 kD HSP (Hsp70). Furthermore, TS-generated superoxide anions production was significantly decreased by Curosurf in an acellular system, suggesting a direct scavenging effect of PS. We also examined the effects of TS and PS on monocytes ultrastructure. Monocytes incubated with Curosurf presented smoother cell membranes than control monocytes, while TS-induced monocyte vacuolization was, at least in part, prevented by Curosurf. Taken together, our data suggest that PS plays a protective role against oxygen radical-mediated, TS-induced cellular stress responses.

Train surfing and other high voltage trauma: differences in injury-related mechanisms and operative outcomes after fasciotomy, amputation and soft-tissue coverage.

PubMed

Lumenta, David Benjamin; Vierhapper, Martin Friedrich; Kamolz, Lars-Peter; Keck, Maike; Frey, Manfred

2011-12-01

In the context of scarce reports on train surfers among high voltage electric injuries, we conducted a retrospective review between January 1994 and December 2008. After matching for inclusion criteria we reviewed patient records of 37 true high voltage injuries (12 train surfers [TS] and 25 other high voltage injuries [HV]). TS were significantly younger (TS 15.8 years vs. HV 33.3 years, p<0.0001), and had a greater %TBSA (TS 49.7%TBSA vs. HV 21.5%TBSA, p=0.0003) without affecting the median length-of-stay (TS 52 days vs. HV 49 days) or number of operations (TS 4 vs. HV 3). TS had different injury patterns, with a higher percentage of affected extremities (TS 72.9% vs. HV 52.0%, p=0.0468) and associated injuries (TS 58% vs. HV 20%, n.s.) than HV. Both groups demonstrated comparable fasciotomy (TS 71.4% vs. HV 55.8%) and amputation rates (TS 17.1% vs. HV 15.4%). While TS required less flaps (TS 3/12 vs. HV 18/25; p=0.0153), soft-tissue reconstruction revealed an overall low incidence of complication rates (one partial pedicled flap loss and two total free flap losses). Train surfers have proven to be a distinct group of patients among high-voltage injuries notably as a result of a younger age, a shorter electric contact duration and higher velocity-induced trauma. With a possibly declining trend of train surfing-related accidents in an aging society, it will be interesting to see if emerging economies will face comparable phenomena, for which prevention strategies remain key. Copyright © 2011 Elsevier Ltd and ISBI. All rights reserved.

Limits to TYMS and TP53 genes as predictive determinants for fluoropyrimidine sensitivity and further evidence for an RNA-based toxicity as a major influence

PubMed Central

Brody, Jonathan R.; Hucl, Tomas; Costantino, Christina L.; Eshleman, James; Gallmeier, Eike; Zhu, Heng; Heijden, Michael S. van der; Winter, Jordan M; Wikiewicz, Agnieszka K.; Yeo, Charles J.; Kern, Scott E.

2010-01-01

The major determinants of 5-flurouracil response would appear, based on accumulated literature, to be thymidylate synthase (TYMS, TS) expression levels, TS gene modifications, and TP53 status. We tested 5-fluorouracil sensitivity in yeast and human cancer cell models in which TS or TP53 alleles and expression were varied. Polymorphic TS tandem repeat status, TS expression levels reported, TS intragenic mutations, and TP53 status in outbred and experimental cancer cell lines did not predict 5-FU sensitivity or resistance. Novel observations included a dose-resistant persistence of unbound TS protein in many cancers and, upon 5-FU treatment of the colon cancer cell line, HCT116, evidence of allelic switching favoring transcripts of the mutant TS allele. The reported alleles having an intragenic mutation could not be causally associated with major degrees of 5-FU sensitivity. In yeast, TS protein was altered upon treatment with fluoro-deoxyuridine monophosphate, but 5-FU toxicity appeared largely to be RNA-based, being rescued by uridine rather than by thymidine. Cancer cell lines were also rescued from 5-FU toxicity with uridine rather than thymidine. Additionally, a TS (CDC21) knockout yeast strain, obviating any potential role for TS protein as a target, was hypersensitive to 5-FU. When denatured proteins from cancer cells treated with radio-labeled 5-FU were, labeled species with alternative molecular weights other than TS were visualized, providing further evidence for alternative 5-FU protein targets. These data emphasize that TS and TP53 status do not consistently explain the variance in responses of fluoropyrimidine-treated cancer cells, in part due to RNA-based toxicity. PMID:19155291

Morphology and Molecular Mechanisms of Hepatic Injury in Rats under Simulated Weightlessness and the Protective Effects of Resistance Training.

PubMed

Du, Fang; Ding, Ye; Zou, Jun; Li, Zhili; Tian, Jijing; She, Ruiping; Wang, Desheng; Wang, Huijuan; Lv, Dongqiang; Chang, Lingling

2015-01-01

This study investigated the effects of long-term simulated weightlessness on liver morphology, enzymes, glycogen, and apoptosis related proteins by using two-month rat-tail suspension model (TS), and liver injury improvement by rat-tail suspension with resistance training model (TS&RT). Microscopically the livers of TS rats showed massive granular degeneration, chronic inflammation, and portal fibrosis. Mitochondrial and endoplasmic reticulum swelling and loss of membrane integrity were observed by transmission electron microscopy (TEM). The similar, but milder, morphological changes were observed in the livers of TS&RT rats. Serum biochemistry analysis revealed that the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were significantly higher (p<0.05) in TS rats than in controls. The levels of ALT and AST in TS&RT rats were slightly lower than in RT rats, but they were insignificantly higher than in controls. However, both TS and TS&RT rats had significantly lower levels (p<0.05) of serum glucose and hepatic glycogen than in controls. Immunohistochemistry demonstrated that the expressions of Bax, Bcl-2, and active caspase-3 were higher in TS rats than in TS&RT and control rats. Real-time polymerase chain reaction (real-time PCR) showed that TS rats had higher mRNA levels (P < 0.05) of glucose-regulated protein 78 (GRP78) and caspase-12 transcription than in control rats; whereas mRNA expressions of C/EBP homologous protein (CHOP) and c-Jun N-terminal kinase (JNK) were slightly higher in TS rats. TS&RT rats showed no significant differences of above 4 mRNAs compared with the control group. Our results demonstrated that long-term weightlessness caused hepatic injury, and may trigger hepatic apoptosis. Resistance training slightly improved hepatic damage.

Morphology and Molecular Mechanisms of Hepatic Injury in Rats under Simulated Weightlessness and the Protective Effects of Resistance Training

PubMed Central

Zou, Jun; Li, Zhili; Tian, Jijing; She, Ruiping; Wang, Desheng; Wang, Huijuan; Lv, Dongqiang; Chang, Lingling

2015-01-01

This study investigated the effects of long-term simulated weightlessness on liver morphology, enzymes, glycogen, and apoptosis related proteins by using two-month rat-tail suspension model (TS), and liver injury improvement by rat-tail suspension with resistance training model (TS&RT). Microscopically the livers of TS rats showed massive granular degeneration, chronic inflammation, and portal fibrosis. Mitochondrial and endoplasmic reticulum swelling and loss of membrane integrity were observed by transmission electron microscopy (TEM). The similar, but milder, morphological changes were observed in the livers of TS&RT rats. Serum biochemistry analysis revealed that the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were significantly higher (p<0.05) in TS rats than in controls. The levels of ALT and AST in TS&RT rats were slightly lower than in RT rats, but they were insignificantly higher than in controls. However, both TS and TS&RT rats had significantly lower levels (p<0.05) of serum glucose and hepatic glycogen than in controls. Immunohistochemistry demonstrated that the expressions of Bax, Bcl-2, and active caspase-3 were higher in TS rats than in TS&RT and control rats. Real-time polymerase chain reaction (real-time PCR) showed that TS rats had higher mRNA levels (P < 0.05) of glucose-regulated protein 78 (GRP78) and caspase-12 transcription than in control rats; whereas mRNA expressions of C/EBP homologous protein (CHOP) and c-Jun N-terminal kinase (JNK) were slightly higher in TS rats. TS&RT rats showed no significant differences of above 4 mRNAs compared with the control group. Our results demonstrated that long-term weightlessness caused hepatic injury, and may trigger hepatic apoptosis. Resistance training slightly improved hepatic damage. PMID:26000905

Terson syndrome in aneurysmal subarachnoid hemorrhage-its relation to intracranial pressure, admission factors, and clinical outcome.

PubMed

Joswig, Holger; Epprecht, Lorenz; Valmaggia, Christophe; Leschka, Sebastian; Hildebrandt, Gerhard; Fournier, Jean-Yves; Stienen, Martin Nikolaus

2016-06-01

A large number of reports have not been able to clarify the pathophysiology of Terson syndrome (TS) in aneurysmal subarachnoid hemorrhage (aSAH). Prospective single-center study on aSAH patients. Fundoscopic and radiological signs of TS were assessed. The opening intracranial pressure (ICP) in patients who required a ventriculostomy was recorded with a manometer. Six out of 36 included patients had TS (16.7 %), which was associated with unfavorable admission scores. Twenty-nine patients (80.5 %) required ventriculostomy; TS was associated with higher ICP (median, 40 vs. 15 cm cmH2O, p = .003); all patients with TS had pathological ICP values of >20 cmH2O. Patients with a ruptured aneurysm of the anterior cerebral artery complex were ten times as likely to suffer from TS (OR 10.0, 95 % CI 1.03-97.50). Detection of TS on CT had a sensitivity of 50 %, a specificity of 98.4 %, a positive predictive value of 83.3 %, and a negative predictive value of 92.4 %. Mortality was 45 times as high in patients with TS (OR 45.0, 95 % CI 3.86-524.7) and neurologic morbidity up until 3 months post-aSAH was significantly higher in patients with TS (mRS 4-6; 100 vs. 17 %; p = .001). Our findings demonstrate an association between raised ICP and the incidence of TS. TS should be ruled out in aSAH patients presenting comatose or with raised ICP to ensure upfront ophthalmological follow-up. In alert patients without visual complaints and a TS-negative CT scan, the likelihood for the presence of TS is very low.

Toe spatiotemporal differences between transition steps when ascending shorter flight stairways of different heights.

PubMed

Ajisafe, Toyin; Wu, Jianhua; Geil, Mark

2017-03-01

Studies have typically treated the first and second floor-to-stair transition steps (TS1 and TS2) as one stride. However, because the foot is devoid of plantar cutaneous input from the stair surface at TS1, these steps may have different toe spatiotemporal profiles, and resultantly, different susceptibilities to a trip and/or a fall. This study compared vertical toe clearance, forward velocity, and their respective variability magnitudes between TS1 and TS2 when ascending stairs of different heights. Twenty young adults (seven males and 13 females) (21.68 ± 2.49 years; 169.70 ± 9.56 cm; 63.91 ± 9.62 kg) negotiated an intervening three-step staircase placed midpoint on a 10 m walkway. There were three stair heights: low stairs (LS), medium stairs (MS), and high stairs (HS). Vertical toe clearance, forward velocity, and their variability magnitudes were calculated. Vertical toe clearance was only higher (P < 0.05) at TS1 than TS2 in the medium and high stairs. Vertical toe clearance was more variable (P < 0.05) in the low compared to medium stairs. Also, forward toe velocity was greater at TS1 than TS2, but was lower in the medium and high stairs. The locomotor system appeared cautious by exaggerating vertical toe clearance at both TS1 and TS2 only in low stairs, possibly due to higher forward toe velocity. If the exaggeration strategy consistently persists, this finding may suggest decreased trip or fall risk at both TS1 and TS2 only when transitioning onto low stairs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Elucidating X chromosome influences on Attention Deficit Hyperactivity Disorder and executive function.

PubMed

Green, Tamar; Bade Shrestha, Sharon; Chromik, Lindsay C; Rutledge, Keetan; Pennington, Bruce F; Hong, David S; Reiss, Allan L

2015-09-01

To identify distinct behavioral and cognitive profiles associated with ADHD in Turner syndrome (TS), relative to idiopathic ADHD and neurotypical controls, in order to elucidate X-linked influences contributing to ADHD. We used a multilevel-model approach to compare 49 girls with TS to 37 neurotypical females, aged 5-12, on established measures of behavior (BASC-2) and neurocognitive function (NEPSY). We further compared girls with TS to BASC-2 and NEPSY age-matched reference data obtained from children with idiopathic ADHD. Within the TS group, 51% scored at or above the "at-risk" range for ADHD-associated behaviors on the BASC-2 (TS/+ADHD). The BASC-2 behavioral profile in this TS/+ADHD-subgroup was comparable to a reference group of boys with ADHD with respect to attentional problems and hyperactivity. However, the TS/+ADHD-subgroup had significantly higher hyperactivity scores relative to a reference sample of girls with ADHD (p = 0.016). The behavioral profile in TS was associated with significantly lower attention and executive function scores on the NEPSY relative to neurotypical controls (p = 0.015); but was comparable to scores from a reference sample of children with idiopathic ADHD. Deficits in attention and executive function were not observed in girls with TS having low levels of ADHD-associated behavior (TS/-ADHD). ADHD-associated behavioral and cognitive problems in TS are prevalent and comparable in severity to those found in children with idiopathic ADHD. The ADHD phenotype in TS also appears relatively independent of cognitive features typically associated with TS, like visuospatial weaknesses. These findings suggest that X-linked haploinsufficiency and downstream biological effects contribute to increased risk for ADHD. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ts6 and Ts2 from Tityus serrulatus venom induce inflammation by mechanisms dependent on lipid mediators and cytokine production.

PubMed

Zoccal, Karina Furlani; Bitencourt, Claudia da Silva; Sorgi, Carlos Artério; Bordon, Karla de Castro Figueiredo; Sampaio, Suely Vilela; Arantes, Eliane Candiani; Faccioli, Lúcia Helena

2013-01-01

Inflammatory mediators are thought to be involved in the systemic and local immune response induced by the Tityus serrulatus scorpion envenomation. New functional aspects of lipid mediators have recently been described. Here, we examine the unreported role of lipid mediators in cell recruitment to the peritoneal cavity after an injection with Ts2 or Ts6 toxins isolated from the T. serrulatus scorpion venom. In this report, we demonstrate that following a single intraperitoneal (i.p.) injection of Ts2 or Ts6 (250 μg/kg) in mice, there was an induction of leukocytosis with a predominance of neutrophils observed at 4, 24, 48 and 96 h. Moreover, total protein, leukotriene (LT)B(4), prostaglandin (PG)E(2) and pro-inflammatory cytokine levels were increased. We also observed an increase of regulatory cytokines, including interleukin (IL)-10, after the Ts2 injection. Finally, we observed that Ts2 or Ts6 injection in 5-lipoxygenase (LO) deficient mice and in wild type (WT) 129sv mice pre-treated with LTs and PGs inhibitors (MK-886 and celecoxib, respectively) a reduction the influx of leukocytes occurs in comparison to WT. The recruitment of these cells demonstrated a phenotype characteristic of neutrophils, macrophages, CD4 and CD8 lymphocytes expressing GR1+, F4/80+, CD3+/CD4+ and CD3+/CD8+, respectively. In conclusion, our data demonstrate that Ts2 and Ts6 induce inflammation by mechanisms dependent on lipid mediators and cytokine production. Ts2 may play a regulatory role whereas Ts6 exhibits pro-inflammatory activity exclusively. Copyright © 2012 Elsevier Ltd. All rights reserved.

An epidemiologic study of Tourette's syndrome in a single school district.

PubMed

Comings, D E; Himes, J A; Comings, B G

1990-11-01

To evaluate the frequency of Gilles de la Tourette's syndrome (TS) in children, 3034 students in three schools in a single school district in greater Los Angeles were monitored frequently over a 2-year period by a school psychologist thoroughly familiar with the symptoms of the disorder. A portion of the cases were also evaluated in a TS clinic. A total of 14 males fulfilled the Tourette Syndrome Association research criteria for definite TS. When corrected for the number of students in special education classes in the monitored schools, the frequency of definite TS in males was 1 in 152. An additional 7 males who differed only in that they were not observed for a full year were termed definite TS less than 1 year. When the two groups were combined, the frequency of definite TS was 1 in 95 for males and 1 in 759 for females. These figures do not include an additional 10 males diagnosed as having definite transient tic disorder, 2 males diagnosed as having probable TS, and 10 males diagnosed as having possible TS. In addition to tics, most of these children had problems with attention span, obsessive compulsive behavior, and learning and/or conduct disorders. Seventy percent of the students with definite TS or definite TS less than 1 year were in special education classes. Twelve percent of the children in special education classes had definite TS or definite TS less than 1 year, and 28% were in one of the diagnostic categories of definite, probable, or possible. All of the 10 definite TS patients that were seen in the clinic had attention-deficit hyperactivity disorder.(ABSTRACT TRUNCATED AT 250 WORDS)

Fuzzy correlation analysis with realization

NASA Astrophysics Data System (ADS)

Tang, Yue Y.; Fan, Xinrui; Zheng, Ying N.

1998-10-01

The fundamental concept of fuzzy correlation is briefly discussed. Based on the correlation coefficient of classic correlation, polarity correlation and fuzzy correlation, the relationship between the correlations are analyzed. A fuzzy correlation analysis has the merits of both rapidity and accuracy as some amplitude information of random signals has been utilized. It has broad prospects for application. The form of fuzzy correlative analyzer with NLX 112 fuzzy data correlator and single-chip microcomputer is introduced.

A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

1993-01-01

A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

Measuring Distance of Fuzzy Numbers by Trapezoidal Fuzzy Numbers

NASA Astrophysics Data System (ADS)

Hajjari, Tayebeh

2010-11-01

Fuzzy numbers and more generally linguistic values are approximate assessments, given by experts and accepted by decision-makers when obtaining value that is more accurate is impossible or unnecessary. Distance between two fuzzy numbers plays an important role in linguistic decision-making. It is reasonable to define a fuzzy distance between fuzzy objects. To achieve this aim, the researcher presents a new distance measure for fuzzy numbers by means of improved centroid distance method. The metric properties are also studied. The advantage is the calculation of the proposed method is far simple than previous approaches.

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

NASA Astrophysics Data System (ADS)

Kladis, Georgios P.; Menon, Prathyush P.; Edwards, Christopher

2016-12-01

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi-Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Krishnan, M Muthu Rama; Molinari, Filippo; Zieleźnik, Witold; Bardales, Ricardo H; Witkowska, Agnieszka; Suri, Jasjit S

2014-02-01

Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Costimulatory Effects of an Immunodominant Parasite Antigen Paradoxically Prevent Induction of Optimal CD8 T Cell Protective Immunity.

PubMed

Eickhoff, Christopher S; Zhang, Xiuli; Vasconcelos, Jose R; Motz, R Geoffrey; Sullivan, Nicole L; O'Shea, Kelly; Pozzi, Nicola; Gohara, David W; Blase, Jennifer R; Di Cera, Enrico; Hoft, Daniel F

2016-09-01

Trypanosoma cruzi infection is controlled but not eliminated by host immunity. The T. cruzi trans-sialidase (TS) gene superfamily encodes immunodominant protective antigens, but expression of altered peptide ligands by different TS genes has been hypothesized to promote immunoevasion. We molecularly defined TS epitopes to determine their importance for protection versus parasite persistence. Peptide-pulsed dendritic cell vaccination experiments demonstrated that one pair of immunodominant CD4+ and CD8+ TS peptides alone can induce protective immunity (100% survival post-lethal parasite challenge). TS DNA vaccines have been shown by us (and others) to protect BALB/c mice against T. cruzi challenge. We generated a new TS vaccine in which the immunodominant TS CD8+ epitope MHC anchoring positions were mutated, rendering the mutant TS vaccine incapable of inducing immunity to the immunodominant CD8 epitope. Immunization of mice with wild type (WT) and mutant TS vaccines demonstrated that vaccines encoding enzymatically active protein and the immunodominant CD8+ T cell epitope enhance subdominant pathogen-specific CD8+ T cell responses. More specifically, CD8+ T cells from WT TS DNA vaccinated mice were responsive to 14 predicted CD8+ TS epitopes, while T cells from mutant TS DNA vaccinated mice were responsive to just one of these 14 predicted TS epitopes. Molecular and structural biology studies revealed that this novel costimulatory mechanism involves CD45 signaling triggered by enzymatically active TS. This enhancing effect on subdominant T cells negatively regulates protective immunity. Using peptide-pulsed DC vaccination experiments, we have shown that vaccines inducing both immunodominant and subdominant epitope responses were significantly less protective than vaccines inducing only immunodominant-specific responses. These results have important implications for T. cruzi vaccine development. Of broader significance, we demonstrate that increasing breadth of T cell epitope responses induced by vaccination is not always advantageous for host immunity.

Does the Institution of a Statewide Trauma System Reduce Preventable Mortality and Yield a Positive Return on Investment for Taxpayers?

PubMed

Maxson, Todd; Mabry, Charles D; Sutherland, Michael J; Robertson, Ronald D; Booker, James O; Collins, Terry; Spencer, Horace J; Rinker, Charles F; Sanddal, Teri L; Sanddal, Nels D

2017-04-01

In July 2009, Arkansas began to annually fund $20 million for a statewide trauma system (TS). We studied injury deaths both pre-TS (2009) and post-TS (2013 to 2014), with attention to causes of preventive mortality, societal cost of those preventable mortality deaths, and benefit to tax payers of the lives saved. A multi-specialty trauma-expert panel met and reviewed records of 672 decedents (290 pre-TS and 382 post-TS) who met standardized inclusion criteria, were judged potentially salvageable, and were selected by a proportional sampling of the roughly 2,500 annual trauma deaths. Deaths were adjudicated into sub-categories of nonpreventable and preventable causes. The value of lives lost was calculated for those lives potentially saved in the post-TS period. Total preventable mortality was reduced from 30% of cases pre-TS to 16% of cases studied post-TS, a reduction of 14%. Extrapolating a 14% reduction of preventable mortality to the post-TS study period, using the same inclusion criteria of the post-TS, we calculate that 79 lives were saved in 2013 to 2014 due to the institution of a TS. Using a minimal standard estimate of $100,000 value for a life-year, a lifetime value of $2,365,000 per person was saved. This equates to an economic impact of the lives saved of almost $186 million annually, representing a 9-fold return on investment from the $20 million of annual state funding invested in the TS. The implementation of a TS in Arkansas during a 5-year period resulted in a reduction of the preventable death rate to 16% post-TS, and a 9-fold return on investment by the tax payer. Additional life-saving gains can be expected with ongoing financial support and additional system performance-improvement efforts. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Insights into Tan Spot and Stem Rust Resistance and Susceptibility by Studying the Pre-Green Revolution Global Collection of Wheat

PubMed Central

Abdullah, Sidrat; Sehgal, Sunish Kumar; Jin, Yue; Turnipseed, Brent; Ali, Shaukat

2017-01-01

Tan spot (TS), caused by the fungus Pyrenophora tritici-repentis (Died) Drechs, is an important foliar disease of wheat and has become a threat to world wheat production since the 1970s. In this study a globally diverse pre-1940s collection of 247 wheat genotypes was evaluated against Ptr ToxA, P. tritici-repentis race 1, and stem rust to determine if; (i) acquisition of Ptr ToxA by the P. tritici-repentis from Stagonospora nodorum led to increased pathogen virulence or (ii) incorporation of TS susceptibility during development stem rust resistant cultivars led to an increase in TS epidemics globally. Most genotypes were susceptible to stem rust; however, a range of reactions to TS and Ptr ToxA were observed. Four combinations of disease-toxin reactions were observed among the genotypes; TS susceptible-Ptr ToxA sensitive, TS susceptible-Ptr ToxA insensitive, TS resistant-Ptr ToxA insensitive, and TS resistant-Ptr ToxA toxin sensitive. A weak correlation (r = 0.14 for bread wheat and −0.082 for durum) was observed between stem rust susceptibility and TS resistance. Even though there were no reported epidemics in the pre-1940s, TS sensitive genotypes were widely grown in that period, suggesting that Ptr ToxA may not be an important factor responsible for enhanced prevalence of TS. PMID:28381959

A comparison of standard definitions and sagittal abdominal ...

EPA Pesticide Factsheets

Introduction: Metabolic syndrome (MeTS) is the cluster of several clinical symptoms that together represent the strongest risk factor for cardiovascular disease. The prevalence of MeTS in adolescents is difficult to estimate given that there are several, but no agreed upon definition of MeTS for this age group. It is important to estimate MeTS and identify at-risk adolescents early in order to provide effective interventions prior to the development of diabetes and coronary heart disease. Objective: Study objectives are to: (1) estimate the prevalence of MeTS in U.S. adolescents using three widely adopted definitions and (2) compare changes in prevalence of MeTS when utilizing sagittal abdominal diameter (SAD) as a component of MeTS. Methods: Data from U.S. adolescents ages 12–19 years (N=970) in the NHANES (2011–2014) were analyzed. MeTS standard definitions developed by Cook et al. (2003), deFerranti et al. (2007), and the International Disease Federation (IDF, 2007) were applied to estimate the sex-stratified, weighted prevalence of MeTS and its individual components (i.e., high waist circumference (WC), hypertension, blood lipid abnormalities, and high fasting blood glucose (FBG)). The definitions were modified by substituting SAD for WC, and weighted MeTS prevalence was re-estimated. Results: Regardless of gender and definition, abnormal blood lipids and high WC were the most prevalent MeTS components. For both sexes, estimated prevalence of componen

Homology modelling, molecular docking, and molecular dynamics simulations reveal the inhibition of Leishmania donovani dihydrofolate reductase-thymidylate synthase enzyme by Withaferin-A.

PubMed

Vadloori, Bharadwaja; Sharath, A K; Prabhu, N Prakash; Maurya, Radheshyam

2018-04-16

Present in silico study was carried out to explore the mode of inhibition of Leishmania donovani dihydrofolate reductase-thymidylate synthase (Ld DHFR-TS) enzyme by Withaferin-A, a withanolide isolated from Withania somnifera. Withaferin-A (WA) is known for its profound multifaceted properties, but its antileishmanial activity is not well understood. The parasite's DHFR-TS enzyme is diverse from its mammalian host and could be a potential drug target in parasites. A 3D model of Ld DHFR-TS enzyme was built and verified using Ramachandran plot and SAVES tools. The protein was docked with WA-the ligand, methotrexate (MTX)-competitive inhibitor of DHFR, and dihydrofolic acid (DHFA)-substrate for DHFR-TS. Molecular docking studies reveal that WA competes for active sites of both Hu DHFR and TS enzymes whereas it binds to a site other than active site in Ld DHFR-TS. Moreover, Lys 173 residue of DHFR-TS forms a H-bond with WA and has higher binding affinity to Ld DHFR-TS than Hu DHFR and Hu TS. The MD simulations confirmed the H-bonding interactions were stable. The binding energies of WA with Ld DHFR-TS were calculated using MM-PBSA. Homology modelling, molecular docking and MD simulations of Ld DHFR-TS revealed that WA could be a potential anti-leishmanial drug.

Molecular cloning, characterization, and immunolocalization of two lactate dehydrogenase homologous genes from Taenia solium.

PubMed

Du, Wuying; Hu, Fengyu; Yang, Yabo; Hu, Dong; Hu, Xuchu; Yu, Xinbing; Xu, Jin; Dai, Jialin; Liao, Xinjiang; Huang, Jiang

2011-09-01

Two novel genes encoding lactate dehydrogenase A (LDHA) and B (LDHB) homologues, respectively, were identified from the cDNA libraries of adult Taenia solium (T. solium). The two deduced amino acid sequences both show more than 50% identity to the homologues for Danio rerio, Xenopus laevis, Schistosoma japonicum, Sus scrofa, Homo sapiens, et al. The identity of the amino acid sequence between TsLDHA and TsLDHB is 57.4%, and that of the nucleotide sequence is 61.5%. Recombinant TsLDHA homologue (rTsLDHA) and TsLDHB homologue (rTsLDHB) were expressed in Escherichia coli BL21/DE3 and purified. Though there were some differences in the sequence, the two LDH isozyme homologues show similarity in the conserved LDH domain, topological structure, primary immunological traits, localization on the tegument of T. solium adult, and partial physicochemical properties. The linear B-cell epitope analysis of TsLDHA and TsLDHB discovered a TsLDHA specific epitope. The purified rTsLDHA and rTsLDHB could be recognized by rat immuno-sera, serum from swine, or a patient infected with T. solium, respectively, but Western blot analysis showed cross-reactions, not only between these two LDH members but also with other common human tapeworms or helminths. The results suggested that the two LDH homologues are similar in the characteristics of LDH family, and they are not specific antigens for immunodiagnosis.

A Qualitative Exploration of the Experiences of Children and Adolescents with Tourette Syndrome

PubMed Central

Edwards, Kim R.; Mendlowitz, Sandra; Jackson, Elana; Champigny, Claire; Specht, Matt; Arnold, Paul; Gorman, Daniel; Dimitropoulos, Gina

2017-01-01

Objective The purpose of this qualitative study was to explore the experiences of youth with Tourette Syndrome (TS). Method Thirteen participants with TS were recruited from a large tertiary care hospital to complete semi-structured interviews and two questionnaires pertaining to demographic information and tic severity. Thematic analysis was utilized to systematically analyze the data. Results Three main themes were identified: 1) beliefs about TS; 2) TS related distress and impairment; and, 3) coping with TS. Conclusion The findings from this study suggest that most participants were aware of their tics but unaware of the cause of tics/TS. The interviews also highlighted that, for most participants, TS caused emotional, social, physical, and/or occupational impairment. Despite their distress, participants provided several suggestions for coping with TS and for supporting those who are diagnosed with this condition. PMID:28331502

Systematic methods for the design of a class of fuzzy logic controllers

NASA Astrophysics Data System (ADS)

Yasin, Saad Yaser

2002-09-01

Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study.

Evaluation of the egg transmission and pathogenicity of Mycoplasma gallisepticum isolates genotyped as ts-11.

PubMed

Armour, Natalie K; Ferguson-Noel, Naola

2015-01-01

Live Mycoplasma gallisepticum vaccines are used for the control of respiratory disease, egg production losses and egg transmission associated with M. gallisepticum infection in long-lived poultry. The first field case of apparent increased virulence and vertical transmission of ts-11, a live M. gallisepticum vaccine, has been reported. In that study a M. gallisepticum isolate from the broiler progeny of ts-11-vaccinated breeders was genotyped as ts-11 by sequence analysis of four different genetic targets and Random Amplified Polymorphic DNA and found to be significantly more virulent than ts-11 vaccine. The objective of the current study was to evaluate the rate of egg transmission and pathogenicity of ts-11 vaccine and isolates recovered from ts-11-vaccinated breeders (K6222B) and their broiler progeny (K6216D) which had been genotyped as ts-11. Groups of 28-week-old specific pathogen-free chickens at 87% average weekly egg production were inoculated with sterile broth media (negative controls), ts-11 vaccine, K6222B, K6216D or R strain (positive controls) by eye-drop and aerosol. K6216D transmitted via the egg at an average rate of 4.0% in the third and fourth weeks post-infection, while egg transmission of K6222B and ts-11 vaccine was not detected. M. gallisepticum was isolated from the air sacs, ovaries and oviducts of hens infected with K6216D and K6222B, but not from those infected with ts-11 vaccine. K6216D and K6222B both induced respiratory signs and significantly more tracheal colonization and more severe tracheal and air sac lesions than ts-11 vaccine (P ≤ 0.05). There were no substantial differences in the egg production of ts-11, K6216D and K6222B infected groups. These results provide the first conclusive evidence of transovarian transmission of an isolate genotyped as ts-11 and indicate that isolates genotyed as ts-11 vary in their virulence and ability to transmit via the egg.

A controlled study of Tourette syndrome. IV. Obsessions, compulsions, and schizoid behaviors.

PubMed Central

Comings, D E; Comings, B G

1987-01-01

To determine the frequency of obsessive, compulsive, and schizoid behaviors in Tourette syndrome (TS), we prospectively questioned 246 patients with TS, 17 with attention-deficit disorder (ADD), 15 with ADD due to a TS gene, and 47 random controls. The comparative frequency of obsessive, compulsive, and repetitive behaviors--such as obsessive unpleasant thoughts, obsessive silly thoughts, echolalia, palilalia, touching things excessively, touching things a specific number of times, touching others excessively, sexual touching, biting or hurting oneself, head banging, rocking, mimicking others, counting things, and occasional or frequent public exhibitionism--were significantly more common in TS patients than in controls. The frequency of each of these was much higher for grade 3 (severe) TS. Most of these behaviors also occurred significantly more often in individuals with ADD or in individuals with ADD secondary to TS (ADD 2(0) TS). When these features were combined into an obsessive-compulsive score, 45.4% of TS patients had a score of 4-15, whereas 8.5% of controls had a score of 4 or 5. These results indicate that obsessive-compulsive behaviors are an integral part of the expression of the TS gene and can be inherited as an autosomal dominant trait. Schizoid symptoms, such as thinking that people were watching them or plotting against them, were significantly more common in TS patients than in controls. Auditory hallucinations of hearing voices were present in 14.6% of TS patients, compared with 2.1% of controls (P = .02). These symptoms were absent in ADD patients but present in ADD 2(0) TS patients. These voices were often blamed for telling them to do bad things and were frequently identified with the devil. None of the controls had a total schizoid behavior score greater than 3, whereas 10.9% of the TS patients had scores of 4-10 (P = .02). This frequency increased to 20.6% in the grade 3 TS patients. These quantitative results confirm our clinical impression that some TS patients have paranoid ideations, often feel that people are out to get them, and hear voices. PMID:3479015

Association of glutathione S-transferase P1 (GSTP1) polymorphism with Tourette syndrome in Taiwanese patients.

PubMed

Shen, Che-Piao; Chou, I-Ching; Liu, Hsin-Ping; Lee, Cheng-Chun; Tsai, Yuhsin; Wu, Bor-Tsang; Hsu, Ban-Dar; Lin, Wei-Yong; Tsai, Fuu-Jen

2014-01-01

The etiology of Tourette syndrome (TS) is multifactorial. TS vulnerability may be associated with genetic and environmental factors. From the genetic point of view, TS is heterogeneous. Previous studies showed that some single-nucleotide polymorphisms (SNPs) of the glutathione-S-transferase P1 (GSTP1) gene can affect cellular proliferation and apoptotic activity and TS is a neurodevelopmental disorder. We guessed that there was a relationship between TS and genetic variants of the GSTP1 gene. Therefore, in this study, we aimed to test the hypothesis that GSTP1 SNPs were associated with TS. We performed a case-control study. One hundred twenty-one TS children and 105 normal children were included in the study. Polymerase chain reaction was used to identify the GSTP1 gene polymorphism at position rs6591256 (A/G, promoter polymorphism) in TS patients and normal children. The polymorphism at position rs6591256 in the GSTP1 gene revealed significant differences in the allele (p=0.0135) and genotype (p=0.0159) distributions between the TS patients and the control group. The A allele was present at a higher frequency than the G allele in the TS patients compared with the control group (odds ratio [OR]=1.91, 95% confidence interval [CI]: 1.14-3.21). The AA genotype was associated with susceptibility to TS with an OR of 2.38 for the AA versus AG genotype (95% CI: 1.29-4.41). These findings suggest that variants in the GSTP1 gene may play a role in susceptibility to TS.

Temperature-sensitive mutants of influenza A virus. XIV. Production and evaluation of influenza A/Georgia/74-ts-1[E] recombinant viruses in human adults.

PubMed

Richman, D D; Murphy, B R; Belshe, R B; Rusten, H M; Chanock, R M; Blacklow, N R; Parrino, T A; Rose, F B; Levine, M M; Caplan, E

1977-08-01

The two temperature-sensitive (ts) lesions present in influenza A/Hong Kong/68-ts-1[E] (H3N2 68) virus were transferred via genetic reassortment to influenza A/Georgia/74 (H3N2 74) wild-type virus. A recombinant clone possessing both ts lesions and the shutoff temperature of 38 C of the Hong Kong/68 ts donor and the two surface antigens of the Georgia/74 wild-type virus was administered to 32 seronegative adult volunteers. Thirty-one volunteers were infected, of whom only five experienced mild afebrile upper respiratory tract illness. The wild-type recipient virus was a cloned population that induced illness in five of six infected volunteers. Therfore, the attenuation exhibited by the Georgia/74-ts-1[E] virus could reasonably be assumed to be due to the acquisition of the two ts-1[E] lesions by the Georgia/74 wild-type virus. The serum and nasal wash antibody responses of the ts-1[E] vaccinees were equivalent to those of the volunteers who received wild-type virus. The two ts lesions present in the Hong Kong/68-ts-1[E] virus have now been transferred three times to a wild-type virus bearing a new hemagglutinin, and in each instance the new ts recombination exhibited a similar, satisfactory level of attenuation and antigenicity for adults. It seems likely that the transfer of the ts-1[E] lesions to any new influenza virus will regularly result in attenuation of a recombinat virus possessing the new surface antigens.

FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.

PubMed

Li, Pu; Chen, Bing

2011-04-01

Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk. Copyright © 2010 Elsevier Ltd. All rights reserved.

A new approach for automatic matching of ground control points in urban areas from heterogeneous images

NASA Astrophysics Data System (ADS)

Cong, Chao; Liu, Dingsheng; Zhao, Lingjun

2008-12-01

This paper discusses a new method for the automatic matching of ground control points (GCPs) between satellite remote sensing Image and digital raster graphic (DRG) in urban areas. The key of this method is to automatically extract tie point pairs according to geographic characters from such heterogeneous images. Since there are big differences between such heterogeneous images respect to texture and corner features, more detail analyzations are performed to find similarities and differences between high resolution remote sensing Image and (DRG). Furthermore a new algorithms based on the fuzzy-c means (FCM) method is proposed to extract linear feature in remote sensing Image. Based on linear feature, crossings and corners extracted from these features are chosen as GCPs. On the other hand, similar method was used to find same features from DRGs. Finally, Hausdorff Distance was adopted to pick matching GCPs from above two GCP groups. Experiences shown the method can extract GCPs from such images with a reasonable RMS error.

Yager’s ranking method for solving the trapezoidal fuzzy number linear programming

NASA Astrophysics Data System (ADS)

Karyati; Wutsqa, D. U.; Insani, N.

2018-03-01

In the previous research, the authors have studied the fuzzy simplex method for trapezoidal fuzzy number linear programming based on the Maleki’s ranking function. We have found some theories related to the term conditions for the optimum solution of fuzzy simplex method, the fuzzy Big-M method, the fuzzy two-phase method, and the sensitivity analysis. In this research, we study about the fuzzy simplex method based on the other ranking function. It is called Yager's ranking function. In this case, we investigate the optimum term conditions. Based on the result of research, it is found that Yager’s ranking function is not like Maleki’s ranking function. Using the Yager’s function, the simplex method cannot work as well as when using the Maleki’s function. By using the Yager’s function, the value of the subtraction of two equal fuzzy numbers is not equal to zero. This condition makes the optimum table of the fuzzy simplex table is undetected. As a result, the simplified fuzzy simplex table becomes stopped and does not reach the optimum solution.

Fuzzy logic-based flight control system design

NASA Astrophysics Data System (ADS)

Nho, Kyungmoon

The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.