Detecting text in natural scenes with multi-level MSER and SWT

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Liu, Renjun

2018-04-01

The detection of the characters in the natural scene is susceptible to factors such as complex background, variable viewing angle and diverse forms of language, which leads to poor detection results. Aiming at these problems, a new text detection method was proposed, which consisted of two main stages, candidate region extraction and text region detection. At first stage, the method used multiple scale transformations of original image and multiple thresholds of maximally stable extremal regions (MSER) to detect the text regions which could detect character regions comprehensively. At second stage, obtained SWT maps by using the stroke width transform (SWT) algorithm to compute the candidate regions, then using cascaded classifiers to propose non-text regions. The proposed method was evaluated on the standard benchmark datasets of ICDAR2011 and the datasets that we made our own data sets. The experiment results showed that the proposed method have greatly improved that compared to other text detection methods.

Path Planning for Robot based on Chaotic Artificial Potential Field Method

NASA Astrophysics Data System (ADS)

Zhang, Cheng

2018-03-01

Robot path planning in unknown environments is one of the hot research topics in the field of robot control. Aiming at the shortcomings of traditional artificial potential field methods, we propose a new path planning for Robot based on chaotic artificial potential field method. The path planning adopts the potential function as the objective function and introduces the robot direction of movement as the control variables, which combines the improved artificial potential field method with chaotic optimization algorithm. Simulations have been carried out and the results demonstrate that the superior practicality and high efficiency of the proposed method.

A ℓ2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Zhang, Jian; Li, Wei; Zhao, Dazhe; Huang, Min; Zaiane, Osmar

2017-03-01

The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ 2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ 2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ 2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ 2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ 2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Critical appraisal of fundamental items in approved clinical trial research proposals in Mashhad University of Medical Sciences

PubMed Central

Shakeri, Mohammad-Taghi; Taghipour, Ali; Sadeghi, Masoumeh; Nezami, Hossein; Amirabadizadeh, Ali-Reza; Bonakchi, Hossein

2017-01-01

Background: Writing, designing, and conducting a clinical trial research proposal has an important role in achieving valid and reliable findings. Thus, this study aimed at critically appraising fundamental information in approved clinical trial research proposals in Mashhad University of Medical Sciences (MUMS) from 2008 to 2014. Methods: This cross-sectional study was conducted on all 935 approved clinical trial research proposals in MUMS from 2008 to 2014. A valid and reliable as well as comprehensive, simple, and usable checklist in sessions with biostatisticians and methodologists, consisting of 11 main items as research tool, were used. Agreement rate between the reviewers of the proposals, who were responsible for data collection, was assessed during 3 sessions, and Kappa statistics was calculated at the last session as 97%. Results: More than 60% of the research proposals had a methodologist consultant, moreover, type of study or study design had been specified in almost all of them (98%). Appropriateness of study aims with hypotheses was not observed in a significant number of research proposals (585 proposals, 62.6%). The required sample size for 66.8% of the approved proposals was based on a sample size formula; however, in 25% of the proposals, sample size formula was not in accordance with the study design. Data collection tool was not selected appropriately in 55.2% of the approved research proposals. Type and method of randomization were unknown in 21% of the proposals and dealing with missing data had not been described in most of them (98%). Inclusion and exclusion criteria were (92%) fully and adequately explained. Moreover, 44% and 31% of the research proposals were moderate and weak in rank, respectively, with respect to the correctness of the statistical analysis methods. Conclusion: Findings of the present study revealed that a large portion of the approved proposals were highly biased or ambiguous with respect to randomization, blinding, dealing with missing data, data collection tool, sampling methods, and statistical analysis. Thus, it is essential to consult and collaborate with a methodologist in all parts of a proposal to control the possible and specific biases in clinical trials. PMID:29445703

Critical appraisal of fundamental items in approved clinical trial research proposals in Mashhad University of Medical Sciences.

PubMed

Shakeri, Mohammad-Taghi; Taghipour, Ali; Sadeghi, Masoumeh; Nezami, Hossein; Amirabadizadeh, Ali-Reza; Bonakchi, Hossein

2017-01-01

Background: Writing, designing, and conducting a clinical trial research proposal has an important role in achieving valid and reliable findings. Thus, this study aimed at critically appraising fundamental information in approved clinical trial research proposals in Mashhad University of Medical Sciences (MUMS) from 2008 to 2014. Methods: This cross-sectional study was conducted on all 935 approved clinical trial research proposals in MUMS from 2008 to 2014. A valid and reliable as well as comprehensive, simple, and usable checklist in sessions with biostatisticians and methodologists, consisting of 11 main items as research tool, were used. Agreement rate between the reviewers of the proposals, who were responsible for data collection, was assessed during 3 sessions, and Kappa statistics was calculated at the last session as 97%. Results: More than 60% of the research proposals had a methodologist consultant, moreover, type of study or study design had been specified in almost all of them (98%). Appropriateness of study aims with hypotheses was not observed in a significant number of research proposals (585 proposals, 62.6%). The required sample size for 66.8% of the approved proposals was based on a sample size formula; however, in 25% of the proposals, sample size formula was not in accordance with the study design. Data collection tool was not selected appropriately in 55.2% of the approved research proposals. Type and method of randomization were unknown in 21% of the proposals and dealing with missing data had not been described in most of them (98%). Inclusion and exclusion criteria were (92%) fully and adequately explained. Moreover, 44% and 31% of the research proposals were moderate and weak in rank, respectively, with respect to the correctness of the statistical analysis methods. Conclusion: Findings of the present study revealed that a large portion of the approved proposals were highly biased or ambiguous with respect to randomization, blinding, dealing with missing data, data collection tool, sampling methods, and statistical analysis. Thus, it is essential to consult and collaborate with a methodologist in all parts of a proposal to control the possible and specific biases in clinical trials.

What does it mean to "employ" the RE-AIM model?

PubMed

Kessler, Rodger S; Purcell, E Peyton; Glasgow, Russell E; Klesges, Lisa M; Benkeser, Rachel M; Peek, C J

2013-03-01

Many grant proposals identify the use of a given evaluation model or framework but offer little about how such models are implemented. The authors discuss what it means to employ a specific model, RE-AIM, and key dimensions from this model for program planning, implementation, evaluation, and reporting. The authors report both conceptual and content specifications for the use of the RE-AIM model and a content review of 42 recent dissemination and implementation grant applications to National Institutes of Health that proposed the use of this model. Outcomes include the extent to which proposals addressed the overall RE-AIM model and specific items within the five dimensions in their methods or evaluation plans. The majority of grants used only some elements of the model (less than 10% contained thorough measures across all RE-AIM dimensions). Few met criteria for "fully developed use" of RE-AIM and the percentage of key issues addressed varied from, on average, 45% to 78% across the RE-AIM dimensions. The results and discussion of key criteria should help investigators in their use of RE-AIM and illuminate the broader issue of comprehensive use of evaluation models.

Communicative Competence of the Fourth Year Students: Basis for Proposed English Language Program

ERIC Educational Resources Information Center

Tuan, Vu Van

2017-01-01

This study on level of communicative competence covering linguistic/grammatical and discourse has aimed at constructing a proposed English language program for 5 key universities in Vietnam. The descriptive method utilized was scientifically employed with comparative techniques and correlational analysis. The researcher treated the surveyed data…

Improved Quasi-Newton method via PSB update for solving systems of nonlinear equations

NASA Astrophysics Data System (ADS)

Mamat, Mustafa; Dauda, M. K.; Waziri, M. Y.; Ahmad, Fadhilah; Mohamad, Fatma Susilawati

2016-10-01

The Newton method has some shortcomings which includes computation of the Jacobian matrix which may be difficult or even impossible to compute and solving the Newton system in every iteration. Also, the common setback with some quasi-Newton methods is that they need to compute and store an n × n matrix at each iteration, this is computationally costly for large scale problems. To overcome such drawbacks, an improved Method for solving systems of nonlinear equations via PSB (Powell-Symmetric-Broyden) update is proposed. In the proposed method, the approximate Jacobian inverse Hk of PSB is updated and its efficiency has improved thereby require low memory storage, hence the main aim of this paper. The preliminary numerical results show that the proposed method is practically efficient when applied on some benchmark problems.

Game Theory Based Trust Model for Cloud Environment

PubMed Central

Gokulnath, K.; Uthariaraj, Rhymend

2015-01-01

The aim of this work is to propose a method to establish trust at bootload level in cloud computing environment. This work proposes a game theoretic based approach for achieving trust at bootload level of both resources and users perception. Nash equilibrium (NE) enhances the trust evaluation of the first-time users and providers. It also restricts the service providers and the users to violate service level agreement (SLA). Significantly, the problem of cold start and whitewashing issues are addressed by the proposed method. In addition appropriate mapping of cloud user's application to cloud service provider for segregating trust level is achieved as a part of mapping. Thus, time complexity and space complexity are handled efficiently. Experiments were carried out to compare and contrast the performance of the conventional methods and the proposed method. Several metrics like execution time, accuracy, error identification, and undecidability of the resources were considered. PMID:26380365

Correlation Filter Learning Toward Peak Strength for Visual Tracking.

PubMed

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

A new local-global approach for classification.

PubMed

Peres, R T; Pedreira, C E

2010-09-01

In this paper, we propose a new local-global pattern classification scheme that combines supervised and unsupervised approaches, taking advantage of both, local and global environments. We understand as global methods the ones concerned with the aim of constructing a model for the whole problem space using the totality of the available observations. Local methods focus into sub regions of the space, possibly using an appropriately selected subset of the sample. In the proposed method, the sample is first divided in local cells by using a Vector Quantization unsupervised algorithm, the LBG (Linde-Buzo-Gray). In a second stage, the generated assemblage of much easier problems is locally solved with a scheme inspired by Bayes' rule. Four classification methods were implemented for comparison purposes with the proposed scheme: Learning Vector Quantization (LVQ); Feedforward Neural Networks; Support Vector Machine (SVM) and k-Nearest Neighbors. These four methods and the proposed scheme were implemented in eleven datasets, two controlled experiments, plus nine public available datasets from the UCI repository. The proposed method has shown a quite competitive performance when compared to these classical and largely used classifiers. Our method is simple concerning understanding and implementation and is based on very intuitive concepts. Copyright 2010 Elsevier Ltd. All rights reserved.

Smoke regions extraction based on two steps segmentation and motion detection in early fire

NASA Astrophysics Data System (ADS)

Jian, Wenlin; Wu, Kaizhi; Yu, Zirong; Chen, Lijuan

2018-03-01

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

An adaptive field detection method for bridge scour monitoring using motion-sensing radio transponders (RFIDs).

DOT National Transportation Integrated Search

2014-01-01

A comprehensive field detection method is proposed that is aimed at developing advanced capability for : reliable monitoring, inspection and life estimation of bridge infrastructure. The goal is to utilize Motion-Sensing Radio Transponders (RFIDS) on...

Novel true-motion estimation algorithm and its application to motion-compensated temporal frame interpolation.

PubMed

Dikbas, Salih; Altunbasak, Yucel

2013-08-01

In this paper, a new low-complexity true-motion estimation (TME) algorithm is proposed for video processing applications, such as motion-compensated temporal frame interpolation (MCTFI) or motion-compensated frame rate up-conversion (MCFRUC). Regular motion estimation, which is often used in video coding, aims to find the motion vectors (MVs) to reduce the temporal redundancy, whereas TME aims to track the projected object motion as closely as possible. TME is obtained by imposing implicit and/or explicit smoothness constraints on the block-matching algorithm. To produce better quality-interpolated frames, the dense motion field at interpolation time is obtained for both forward and backward MVs; then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly. Finally, the performance of the proposed algorithm for MCTFI is demonstrated against recently proposed methods and smoothness constraint optical flow employed by a professional video production suite. Experimental results show that the quality of the interpolated frames using the proposed method is better when compared with the MCFRUC techniques.

A modified precise integration method for transient dynamic analysis in structural systems with multiple damping models

NASA Astrophysics Data System (ADS)

Ding, Zhe; Li, Li; Hu, Yujin

2018-01-01

Sophisticated engineering systems are usually assembled by subcomponents with significantly different levels of energy dissipation. Therefore, these damping systems often contain multiple damping models and lead to great difficulties in analyzing. This paper aims at developing a time integration method for structural systems with multiple damping models. The dynamical system is first represented by a generally damped model. Based on this, a new extended state-space method for the damped system is derived. A modified precise integration method with Gauss-Legendre quadrature is then proposed. The numerical stability and accuracy of the proposed integration method are discussed in detail. It is verified that the method is conditionally stable and has inherent algorithmic damping, period error and amplitude decay. Numerical examples are provided to assess the performance of the proposed method compared with other methods. It is demonstrated that the method is more accurate than other methods with rather good efficiency and the stable condition is easy to be satisfied in practice.

A novel method for overlapping community detection using Multi-objective optimization

NASA Astrophysics Data System (ADS)

Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa

2018-09-01

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.

Spatio-temporal alignment of multiple sensors

NASA Astrophysics Data System (ADS)

Zhang, Tinghua; Ni, Guoqiang; Fan, Guihua; Sun, Huayan; Yang, Biao

2018-01-01

Aiming to achieve the spatio-temporal alignment of multi sensor on the same platform for space target observation, a joint spatio-temporal alignment method is proposed. To calibrate the parameters and measure the attitude of cameras, an astronomical calibration method is proposed based on star chart simulation and collinear invariant features of quadrilateral diagonal between the observed star chart. In order to satisfy a temporal correspondence and spatial alignment similarity simultaneously, the method based on the astronomical calibration and attitude measurement in this paper formulates the video alignment to fold the spatial and temporal alignment into a joint alignment framework. The advantage of this method is reinforced by exploiting the similarities and prior knowledge of velocity vector field between adjacent frames, which is calculated by the SIFT Flow algorithm. The proposed method provides the highest spatio-temporal alignment accuracy compared to the state-of-the-art methods on sequences recorded from multi sensor at different times.

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schroedinger equation

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

Leung Shingyu, E-mail: masyleung@ust.h; Qian Jianliang, E-mail: qian@math.msu.ed

2010-11-20

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schroedinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in . In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying themore » FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.« less

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schrödinger equation

NASA Astrophysics Data System (ADS)

Leung, Shingyu; Qian, Jianliang

2010-11-01

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schrödinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in [12]. In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying the FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PubMed

Chen, Shuo-Tsung; Wang, Tzung-Dau; Lee, Wen-Jeng; Huang, Tsai-Wei; Hung, Pei-Kai; Wei, Cheng-Yu; Chen, Chung-Ming; Kung, Woon-Man

2015-01-01

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

Analysis of full disc Ca II K spectroheliograms. I. Photometric calibration and centre-to-limb variation compensation

NASA Astrophysics Data System (ADS)

Chatzistergos, Theodosios; Ermolli, Ilaria; Solanki, Sami K.; Krivova, Natalie A.

2018-01-01

Context. Historical Ca II K spectroheliograms (SHG) are unique in representing long-term variations of the solar chromospheric magnetic field. They usually suffer from numerous problems and lack photometric calibration. Thus accurate processing of these data is required to get meaningful results from their analysis. Aims: In this paper we aim at developing an automatic processing and photometric calibration method that provides precise and consistent results when applied to historical SHG. Methods: The proposed method is based on the assumption that the centre-to-limb variation of the intensity in quiet Sun regions does not vary with time. We tested the accuracy of the proposed method on various sets of synthetic images that mimic problems encountered in historical observations. We also tested our approach on a large sample of images randomly extracted from seven different SHG archives. Results: The tests carried out on the synthetic data show that the maximum relative errors of the method are generally <6.5%, while the average error is <1%, even if rather poor quality observations are considered. In the absence of strong artefacts the method returns images that differ from the ideal ones by <2% in any pixel. The method gives consistent values for both plage and network areas. We also show that our method returns consistent results for images from different SHG archives. Conclusions: Our tests show that the proposed method is more accurate than other methods presented in the literature. Our method can also be applied to process images from photographic archives of solar observations at other wavelengths than Ca II K.

Intelligent Distribution Voltage Control with Distributed Generation =

NASA Astrophysics Data System (ADS)

Castro Mendieta, Jose

In this thesis, three methods for the optimal participation of the reactive power of distributed generations (DGs) in unbalanced distributed network have been proposed, developed, and tested. These new methods were developed with the objectives of maintain voltage within permissible limits and reduce losses. The first method proposes an optimal participation of reactive power of all devices available in the network. The propose approach is validated by comparing the results with other methods reported in the literature. The proposed method was implemented using Simulink of Matlab and OpenDSS. Optimization techniques and the presentation of results are from Matlab. The co-simulation of Electric Power Research Institute's (EPRI) OpenDSS program solves a three-phase optimal power flow problem in the unbalanced IEEE 13 and 34-node test feeders. The results from this work showed a better loss reduction compared to the Coordinated Voltage Control (CVC) method. The second method aims to minimize the voltage variation on the pilot bus on distribution network using DGs. It uses Pareto and Fuzzy-PID logic to reduce the voltage variation. Results indicate that the proposed method reduces the voltage variation more than the other methods. Simulink of Matlab and OpenDSS is used in the development of the proposed approach. The performance of the method is evaluated on IEEE 13-node test feeder with one and three DGs. Variables and unbalanced loads are used, based on real consumption data, over a time window of 48 hours. The third method aims to minimize the reactive losses using DGs on distribution networks. This method analyzes the problem using the IEEE 13-node test feeder with three different loads and the IEEE 123-node test feeder with four DGs. The DGs can be fixed or variables. Results indicate that integration of DGs to optimize the reactive power of the network helps to maintain the voltage within the allowed limits and to reduce the reactive power losses. The thesis is presented in the form of the three articles. The first article is published in the journal Electrical Power and Energy System, the second is published in the international journal Energies and the third was submitted to the journal Electrical Power and Energy System. Two other articles have been published in conferences with reviewing committee. This work is based on six chapters, which are detailed in the various sections of the thesis.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target.

PubMed

Chiba, Shuntaro; Ikeda, Kazuyoshi; Ishida, Takashi; Gromiha, M Michael; Taguchi, Y-H; Iwadate, Mitsuo; Umeyama, Hideaki; Hsin, Kun-Yi; Kitano, Hiroaki; Yamamoto, Kazuki; Sugaya, Nobuyoshi; Kato, Koya; Okuno, Tatsuya; Chikenji, George; Mochizuki, Masahiro; Yasuo, Nobuaki; Yoshino, Ryunosuke; Yanagisawa, Keisuke; Ban, Tomohiro; Teramoto, Reiji; Ramakrishnan, Chandrasekaran; Thangakani, A Mary; Velmurugan, D; Prathipati, Philip; Ito, Junichi; Tsuchiya, Yuko; Mizuguchi, Kenji; Honma, Teruki; Hirokawa, Takatsugu; Akiyama, Yutaka; Sekijima, Masakazu

2015-11-26

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

PubMed Central

Chiba, Shuntaro; Ikeda, Kazuyoshi; Ishida, Takashi; Gromiha, M. Michael; Taguchi, Y-h.; Iwadate, Mitsuo; Umeyama, Hideaki; Hsin, Kun-Yi; Kitano, Hiroaki; Yamamoto, Kazuki; Sugaya, Nobuyoshi; Kato, Koya; Okuno, Tatsuya; Chikenji, George; Mochizuki, Masahiro; Yasuo, Nobuaki; Yoshino, Ryunosuke; Yanagisawa, Keisuke; Ban, Tomohiro; Teramoto, Reiji; Ramakrishnan, Chandrasekaran; Thangakani, A. Mary; Velmurugan, D.; Prathipati, Philip; Ito, Junichi; Tsuchiya, Yuko; Mizuguchi, Kenji; Honma, Teruki; Hirokawa, Takatsugu; Akiyama, Yutaka; Sekijima, Masakazu

2015-01-01

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective. PMID:26607293

Development of methods for analysis of knee articular cartilage degeneration by magnetic resonance imaging data

NASA Astrophysics Data System (ADS)

Suponenkovs, Artjoms; Glazs, Aleksandrs; Platkajis, Ardis

2017-03-01

The aim of this paper is to describe the new methods for analyzing knee articular cartilage degeneration. The most important aspects regarding research about magnetic resonance imaging, knee joint anatomy, stages of knee osteoarthritis, medical image segmentation and relaxation times calculation. This paper proposes new methods for relaxation times calculation and medical image segmentation. The experimental part describes the most important aspect regarding analysing of articular cartilage relaxation times changing. This part contains experimental results, which show the codependence between relaxation times and organic structure. These experimental results and proposed methods can be helpful for early osteoarthritis diagnostics.

Developing a Self-Report-Based Sequential Analysis Method for Educational Technology Systems: A Process-Based Usability Evaluation

ERIC Educational Resources Information Center

Lin, Yi-Chun; Hsieh, Ya-Hui; Hou, Huei-Tse

2015-01-01

The development of a usability evaluation method for educational systems or applications, called the self-report-based sequential analysis, is described herein. The method aims to extend the current practice by proposing self-report-based sequential analysis as a new usability method, which integrates the advantages of self-report in survey…

Who Needs What? Some Thoughts on the Possibility of Using Psychology in Actor Training.

ERIC Educational Resources Information Center

Vulova, Marina

2001-01-01

Contends that the aim of professional actor training is to reveal and develop an actor's individuality. Proposes that the responsibility of drama teachers is to lead training in such away that students feel accepted, understood, and respected. Proposes that psychodrama is the most appropriate method for student and professional actors' personal…

Cognitive Approaches for Medicine in Cloud Computing.

PubMed

Ogiela, Urszula; Takizawa, Makoto; Ogiela, Lidia

2018-03-03

This paper will present the application potential of the cognitive approach to data interpretation, with special reference to medical areas. The possibilities of using the meaning approach to data description and analysis will be proposed for data analysis tasks in Cloud Computing. The methods of cognitive data management in Cloud Computing are aimed to support the processes of protecting data against unauthorised takeover and they serve to enhance the data management processes. The accomplishment of the proposed tasks will be the definition of algorithms for the execution of meaning data interpretation processes in safe Cloud Computing. • We proposed a cognitive methods for data description. • Proposed a techniques for secure data in Cloud Computing. • Application of cognitive approaches for medicine was described.

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Qualitative approaches to use of the RE-AIM framework: rationale and methods.

PubMed

Holtrop, Jodi Summers; Rabin, Borsika A; Glasgow, Russell E

2018-03-13

There have been over 430 publications using the RE-AIM model for planning and evaluation of health programs and policies, as well as numerous applications of the model in grant proposals and national programs. Full use of the model includes use of qualitative methods to understand why and how results were obtained on different RE-AIM dimensions, however, recent reviews have revealed that qualitative methods have been used infrequently. Having quantitative and qualitative methods and results iteratively inform each other should enhance understanding and lessons learned. Because there have been few published examples of qualitative approaches and methods using RE-AIM for planning or assessment and no guidance on how qualitative approaches can inform these processes, we provide guidance on qualitative methods to address the RE-AIM model and its various dimensions. The intended audience is researchers interested in applying RE-AIM or similar implementation models, but the methods discussed should also be relevant to those in community or clinical settings. We present directions for, examples of, and guidance on how qualitative methods can be used to address each of the five RE-AIM dimensions. Formative qualitative methods can be helpful in planning interventions and designing for dissemination. Summative qualitative methods are useful when used in an iterative, mixed methods approach for understanding how and why different patterns of results occur. In summary, qualitative and mixed methods approaches to RE-AIM help understand complex situations and results, why and how outcomes were obtained, and contextual factors not easily assessed using quantitative measures.

(Un)Earthing a Vocabulary of Values: A Discourse Analysis for Ecocomposition

ERIC Educational Resources Information Center

Walker, Paul

2010-01-01

In this article, the author aims to propose an analytic method through which composition students and others might discover and understand the ecological complexities of prevailing environmental terminology that create "wicked problems." Through this method, students engage in "discursive ecology" by exploring the connections…

Krylov-Subspace Recycling via the POD-Augmented Conjugate-Gradient Method

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

Carlberg, Kevin; Forstall, Virginia; Tuminaro, Ray

This paper presents a new Krylov-subspace-recycling method for efficiently solving sequences of linear systems of equations characterized by varying right-hand sides and symmetric-positive-definite matrices. As opposed to typical truncation strategies used in recycling such as deflation, we propose a truncation method inspired by goal-oriented proper orthogonal decomposition (POD) from model reduction. This idea is based on the observation that model reduction aims to compute a low-dimensional subspace that contains an accurate solution; as such, we expect the proposed method to generate a low-dimensional subspace that is well suited for computing solutions that can satisfy inexact tolerances. In particular, we proposemore » specific goal-oriented POD `ingredients' that align the optimality properties of POD with the objective of Krylov-subspace recycling. To compute solutions in the resulting 'augmented' POD subspace, we propose a hybrid direct/iterative three-stage method that leverages 1) the optimal ordering of POD basis vectors, and 2) well-conditioned reduced matrices. Numerical experiments performed on solid-mechanics problems highlight the benefits of the proposed method over existing approaches for Krylov-subspace recycling.« less

Krylov-Subspace Recycling via the POD-Augmented Conjugate-Gradient Method

DOE PAGES

Carlberg, Kevin; Forstall, Virginia; Tuminaro, Ray

2016-01-01

This paper presents a new Krylov-subspace-recycling method for efficiently solving sequences of linear systems of equations characterized by varying right-hand sides and symmetric-positive-definite matrices. As opposed to typical truncation strategies used in recycling such as deflation, we propose a truncation method inspired by goal-oriented proper orthogonal decomposition (POD) from model reduction. This idea is based on the observation that model reduction aims to compute a low-dimensional subspace that contains an accurate solution; as such, we expect the proposed method to generate a low-dimensional subspace that is well suited for computing solutions that can satisfy inexact tolerances. In particular, we proposemore » specific goal-oriented POD `ingredients' that align the optimality properties of POD with the objective of Krylov-subspace recycling. To compute solutions in the resulting 'augmented' POD subspace, we propose a hybrid direct/iterative three-stage method that leverages 1) the optimal ordering of POD basis vectors, and 2) well-conditioned reduced matrices. Numerical experiments performed on solid-mechanics problems highlight the benefits of the proposed method over existing approaches for Krylov-subspace recycling.« less

A Synthetic Approach to the Transfer Matrix Method in Classical and Quantum Physics

ERIC Educational Resources Information Center

Pujol, O.; Perez, J. P.

2007-01-01

The aim of this paper is to propose a synthetic approach to the transfer matrix method in classical and quantum physics. This method is an efficient tool to deal with complicated physical systems of practical importance in geometrical light or charged particle optics, classical electronics, mechanics, electromagnetics and quantum physics. Teaching…

An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection.

PubMed

M, Soorya; Issac, Ashish; Dutta, Malay Kishore

2018-02-01

Glaucoma is an ocular disease which can cause irreversible blindness. The disease is currently identified using specialized equipment operated by optometrists manually. The proposed work aims to provide an efficient imaging solution which can help in automating the process of Glaucoma diagnosis using computer vision techniques from digital fundus images. The proposed method segments the optic disc using a geometrical feature based strategic framework which improves the detection accuracy and makes the algorithm invariant to illumination and noise. Corner thresholding and point contour joining based novel methods are proposed to construct smooth contours of Optic Disc. Based on a clinical approach as used by ophthalmologist, the proposed algorithm tracks blood vessels inside the disc region and identifies the points at which first vessel bend from the optic disc boundary and connects them to obtain the contours of Optic Cup. The proposed method has been compared with the ground truth marked by the medical experts and the similarity parameters, used to determine the performance of the proposed method, have yield a high similarity of segmentation. The proposed method has achieved a macro-averaged f-score of 0.9485 and accuracy of 97.01% in correctly classifying fundus images. The proposed method is clinically significant and can be used for Glaucoma screening over a large population which will work in a real time. Copyright © 2017 Elsevier B.V. All rights reserved.

Eddy current loss analysis of open-slot fault-tolerant permanent-magnet machines based on conformal mapping method

NASA Astrophysics Data System (ADS)

Ji, Jinghua; Luo, Jianhua; Lei, Qian; Bian, Fangfang

2017-05-01

This paper proposed an analytical method, based on conformal mapping (CM) method, for the accurate evaluation of magnetic field and eddy current (EC) loss in fault-tolerant permanent-magnet (FTPM) machines. The aim of modulation function, applied in CM method, is to change the open-slot structure into fully closed-slot structure, whose air-gap flux density is easy to calculate analytically. Therefore, with the help of Matlab Schwarz-Christoffel (SC) Toolbox, both the magnetic flux density and EC density of FTPM machine are obtained accurately. Finally, time-stepped transient finite-element method (FEM) is used to verify the theoretical analysis, showing that the proposed method is able to predict the magnetic flux density and EC loss precisely.

A hybrid method for evaluating enterprise architecture implementation.

PubMed

Nikpay, Fatemeh; Ahmad, Rodina; Yin Kia, Chiam

2017-02-01

Enterprise Architecture (EA) implementation evaluation provides a set of methods and practices for evaluating the EA implementation artefacts within an EA implementation project. There are insufficient practices in existing EA evaluation models in terms of considering all EA functions and processes, using structured methods in developing EA implementation, employing matured practices, and using appropriate metrics to achieve proper evaluation. The aim of this research is to develop a hybrid evaluation method that supports achieving the objectives of EA implementation. To attain this aim, the first step is to identify EA implementation evaluation practices. To this end, a Systematic Literature Review (SLR) was conducted. Second, the proposed hybrid method was developed based on the foundation and information extracted from the SLR, semi-structured interviews with EA practitioners, program theory evaluation and Information Systems (ISs) evaluation. Finally, the proposed method was validated by means of a case study and expert reviews. This research provides a suitable foundation for researchers who wish to extend and continue this research topic with further analysis and exploration, and for practitioners who would like to employ an effective and lightweight evaluation method for EA projects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining large number of weak biomarkers based on AUC.

PubMed

Yan, Li; Tian, Lili; Liu, Song

2015-12-20

Combining multiple biomarkers to improve diagnosis and/or prognosis accuracy is a common practice in clinical medicine. Both parametric and non-parametric methods have been developed for finding the optimal linear combination of biomarkers to maximize the area under the receiver operating characteristic curve (AUC), primarily focusing on the setting with a small number of well-defined biomarkers. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Such settings are not uncommon in certain applied fields. The first aim of this paper is to empirically evaluate the performance of existing linear combination methods under such settings. The second aim is to propose a new combination method, namely, the pairwise approach, to maximize AUC. Our simulation studies demonstrated that the performance of several existing methods can become unsatisfactory as the number of markers becomes large, while the newly proposed pairwise method performs reasonably well. Furthermore, we apply all the combination methods to real datasets used for the development and validation of MammaPrint. The implication of our study for the design of optimal linear combination methods is discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Combining large number of weak biomarkers based on AUC

PubMed Central

Yan, Li; Tian, Lili; Liu, Song

2018-01-01

Combining multiple biomarkers to improve diagnosis and/or prognosis accuracy is a common practice in clinical medicine. Both parametric and non-parametric methods have been developed for finding the optimal linear combination of biomarkers to maximize the area under the receiver operating characteristic curve (AUC), primarily focusing on the setting with a small number of well-defined biomarkers. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Such settings are not uncommon in certain applied fields. The first aim of this paper is to empirically evaluate the performance of existing linear combination methods under such settings. The second aim is to propose a new combination method, namely, the pairwise approach, to maximize AUC. Our simulation studies demonstrated that the performance of several existing methods can become unsatisfactory as the number of markers becomes large, while the newly proposed pairwise method performs reasonably well. Furthermore, we apply all the combination methods to real datasets used for the development and validation of MammaPrint. The implication of our study for the design of optimal linear combination methods is discussed. PMID:26227901

Cooperative parallel adaptive neighbourhood search for the disjunctively constrained knapsack problem

NASA Astrophysics Data System (ADS)

Quan, Zhe; Wu, Lei

2017-09-01

This article investigates the use of parallel computing for solving the disjunctively constrained knapsack problem. The proposed parallel computing model can be viewed as a cooperative algorithm based on a multi-neighbourhood search. The cooperation system is composed of a team manager and a crowd of team members. The team members aim at applying their own search strategies to explore the solution space. The team manager collects the solutions from the members and shares the best one with them. The performance of the proposed method is evaluated on a group of benchmark data sets. The results obtained are compared to those reached by the best methods from the literature. The results show that the proposed method is able to provide the best solutions in most cases. In order to highlight the robustness of the proposed parallel computing model, a new set of large-scale instances is introduced. Encouraging results have been obtained.

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

NASA Astrophysics Data System (ADS)

Liu, Qiong; Wang, Wen-xi; Zhu, Ke-ren; Zhang, Chao-yong; Rao, Yun-qing

2014-11-01

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.

Microwave-assisted Derivatization of Fatty Acids for Its Measurement in Milk Using High-Performance Liquid Chromatography.

PubMed

Shrestha, Rojeet; Miura, Yusuke; Hirano, Ken-Ichi; Chen, Zhen; Okabe, Hiroaki; Chiba, Hitoshi; Hui, Shu-Ping

2018-01-01

Fatty acid (FA) profiling of milk has important applications in human health and nutrition. Conventional methods for the saponification and derivatization of FA are time-consuming and laborious. We aimed to develop a simple, rapid, and economical method for the determination of FA in milk. We applied a beneficial approach of microwave-assisted saponification (MAS) of milk fats and microwave-assisted derivatization (MAD) of FA to its hydrazides, integrated with HPLC-based analysis. The optimal conditions for MAS and MAD were determined. Microwave irradiation significantly reduced the sample preparation time from 80 min in the conventional method to less than 3 min. We used three internal standards for the measurement of short-, medium- and long-chain FA. The proposed method showed satisfactory analytical sensitivity, recovery and reproducibility. There was a significant correlation in the milk FA concentrations between the proposed and conventional methods. Being quick, economic, and convenient, the proposed method for the milk FA measurement can be substitute for the convention method.

Learning locality preserving graph from data.

PubMed

Zhang, Yan-Ming; Huang, Kaizhu; Hou, Xinwen; Liu, Cheng-Lin

2014-11-01

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

An Improved Distributed Secondary Control Method for DC Microgrids With Enhanced Dynamic Current Sharing Performance

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

Wang, Panbao; Lu, Xiaonan; Yang, Xu

This paper proposes an improved distributed secondary control scheme for dc microgrids (MGs), aiming at overcoming the drawbacks of conventional droop control method. The proposed secondary control scheme can remove the dc voltage deviation and improve the current sharing accuracy by using voltage-shifting and slope-adjusting approaches simultaneously. Meanwhile, the average value of droop coefficients is calculated, and then it is controlled by an additional controller included in the distributed secondary control layer to ensure that each droop coefficient converges at a reasonable value. Hence, by adjusting the droop coefficient, each participating converter has equal output impedance, and the accurate proportionalmore » load current sharing can be achieved with different line resistances. Furthermore, the current sharing performance in steady and transient states can be enhanced by using the proposed method. The effectiveness of the proposed method is verified by detailed experimental tests based on a 3 × 1 kW prototype with three interface converters.« less

Synthesis and use of 2-[ 18F]fluoromalondialdehyde, an accessible synthon for bioconjugation

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

Hooker, Jacob M.

We proposed methods for the synthesis and purification of 2-[ 18F]fluoromalondialdehyde, which will be a readily accessible synthon for bioconjugation. Our achievements in these areas will specifically address a stated goal of the DOE providing a transformational technology for macromolecule radiolabeling. Accomplishment of our aims will serve both DOE mission-related research as well as nuclear medicine research supported by the NIH and industry. At the heart of our proposal is the aim to “improve synthetic methodology for rapidly and efficiently incorporating radionuclides into a wide range of organic compounds.”

The Hull Method for Selecting the Number of Common Factors

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Timmerman, Marieke E.; Kiers, Henk A. L.

2011-01-01

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an…

Diverse task scheduling for individualized requirements in cloud manufacturing

NASA Astrophysics Data System (ADS)

Zhou, Longfei; Zhang, Lin; Zhao, Chun; Laili, Yuanjun; Xu, Lida

2018-03-01

Cloud manufacturing (CMfg) has emerged as a new manufacturing paradigm that provides ubiquitous, on-demand manufacturing services to customers through network and CMfg platforms. In CMfg system, task scheduling as an important means of finding suitable services for specific manufacturing tasks plays a key role in enhancing the system performance. Customers' requirements in CMfg are highly individualized, which leads to diverse manufacturing tasks in terms of execution flows and users' preferences. We focus on diverse manufacturing tasks and aim to address their scheduling issue in CMfg. First of all, a mathematical model of task scheduling is built based on analysis of the scheduling process in CMfg. To solve this scheduling problem, we propose a scheduling method aiming for diverse tasks, which enables each service demander to obtain desired manufacturing services. The candidate service sets are generated according to subtask directed graphs. An improved genetic algorithm is applied to searching for optimal task scheduling solutions. The effectiveness of the scheduling method proposed is verified by a case study with individualized customers' requirements. The results indicate that the proposed task scheduling method is able to achieve better performance than some usual algorithms such as simulated annealing and pattern search.

Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization.

PubMed

Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui

2015-01-19

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation.

Cross-domain latent space projection for person re-identification

NASA Astrophysics Data System (ADS)

Pu, Nan; Wu, Song; Qian, Li; Xiao, Guoqiang

2018-04-01

In this paper, we research the problem of person re-identification and propose a cross-domain latent space projection (CDLSP) method to address the problems of the absence or insufficient labeled data in the target domain. Under the assumption that the visual features in the source domain and target domain share the similar geometric structure, we transform the visual features from source domain and target domain to a common latent space by optimizing the object function defined in the manifold alignment method. Moreover, the proposed object function takes into account the specific knowledge in the re-id with the aim to improve the performance of re-id under complex situations. Extensive experiments conducted on four benchmark datasets show the proposed CDLSP outperforms or is competitive with stateof- the-art methods for person re-identification.

Investigation of KDP crystal surface based on an improved bidimensional empirical mode decomposition method

NASA Astrophysics Data System (ADS)

Lu, Lei; Yan, Jihong; Chen, Wanqun; An, Shi

2018-03-01

This paper proposed a novel spatial frequency analysis method for the investigation of potassium dihydrogen phosphate (KDP) crystal surface based on an improved bidimensional empirical mode decomposition (BEMD) method. Aiming to eliminate end effects of the BEMD method and improve the intrinsic mode functions (IMFs) for the efficient identification of texture features, a denoising process was embedded in the sifting iteration of BEMD method. With removing redundant information in decomposed sub-components of KDP crystal surface, middle spatial frequencies of the cutting and feeding processes were identified. Comparative study with the power spectral density method, two-dimensional wavelet transform (2D-WT), as well as the traditional BEMD method, demonstrated that the method developed in this paper can efficiently extract texture features and reveal gradient development of KDP crystal surface. Furthermore, the proposed method was a self-adaptive data driven technique without prior knowledge, which overcame shortcomings of the 2D-WT model such as the parameters selection. Additionally, the proposed method was a promising tool for the application of online monitoring and optimal control of precision machining process.

Adolescents' Views about a Proposed Rewards Intervention to Promote Healthy Food Choice in Secondary School Canteens

ERIC Educational Resources Information Center

McEvoy, C. T.; Lawton, J.; Kee, F.; Young, I. S.; Woodside, J. V.; McBratney, J.; McKinley, M. C.

2014-01-01

Using rewards may be an effective method to positively influence adolescent eating behaviour, but evidence regarding this approach is limited. The aim of this study was to explore young adolescent views about a proposed reward intervention associated with food choice in school canteens. Focus groups were held in 10 schools located in lower…

Validation of Proposed "DSM-5" Criteria for Autism Spectrum Disorder

ERIC Educational Resources Information Center

Frazier, Thomas W.; Youngstrom, Eric A.; Speer, Leslie; Embacher, Rebecca; Law, Paul; Constantino, John; Findling, Robert L.; Hardan, Antonio Y.; Eng, Charis

2012-01-01

Objective: The primary aim of the present study was to evaluate the validity of proposed "DSM-5" criteria for autism spectrum disorder (ASD). Method: We analyzed symptoms from 14,744 siblings (8,911 ASD and 5,863 non-ASD) included in a national registry, the Interactive Autism Network. Youth 2 through 18 years of age were included if at least one…

Proposal for National Targets in the Framework of the European Reduction Goal for Early School Leaving

ERIC Educational Resources Information Center

Lastra-Bravo, Xavier B.; Tolón-Becerra, Alfredo; Salinas-Andújar, José A.

2013-01-01

According to the European Commission's "Europe 2020" strategy, the early school leaving (ESL) rate in European Union (EU) Member States must be reduced to a maximum of 10 per cent by 2020. This paper proposes a nonlinear distribution method based on dynamic targets for reducing the percentage of early school leavers. The aim of this…

Link Prediction in Evolving Networks Based on Popularity of Nodes.

PubMed

Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

2017-08-02

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

NASA Astrophysics Data System (ADS)

Zhang, Wancheng; Xu, Yejun; Wang, Huimin

2016-01-01

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.

On-line estimation and compensation of measurement delay in GPS/SINS integration

NASA Astrophysics Data System (ADS)

Yang, Tao; Wang, Wei

2008-10-01

The chief aim of this paper is to propose a simple on-line estimation and compensation method of GPS/SINS measurement delay. The causes of time delay for GPS/SINS integration are analyzed in this paper. New Kalman filter state equations augmented by measurement delay and modified measurement equations are derived. Based on an open-loop Kalman filter, several simulations are run, results of which show that by the proposed method, the estimation and compensation error of measurement delay is below 0.1s.

Efficient solution methodology for calibrating the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements.

PubMed

Zambri, Brian; Djellouli, Rabia; Laleg-Kirati, Taous-Meriem

2015-08-01

Our aim is to propose a numerical strategy for retrieving accurately and efficiently the biophysiological parameters as well as the external stimulus characteristics corresponding to the hemodynamic mathematical model that describes changes in blood flow and blood oxygenation during brain activation. The proposed method employs the TNM-CKF method developed in [1], but in a prediction/correction framework. We present numerical results using both real and synthetic functional Magnetic Resonance Imaging (fMRI) measurements to highlight the performance characteristics of this computational methodology.

Monitoring biodiesel reactions of soybean oil and sunflower oil using ultrasonic parameters

NASA Astrophysics Data System (ADS)

Figueiredo, M. K. K.; Silva, C. E. R.; Alvarenga, A. V.; Costa-Félix, R. P. B.

2015-01-01

Biodiesel is an innovation that attempts to substitute diesel oil with biomass. The aim of this paper is to show the development of a real-time method to monitor transesterification reactions by using low-power ultrasound and pulse/echo techniques. The results showed that it is possible to identify different events during the transesterification process by using the proposed parameters, showing that the proposed method is a feasible way to monitor the reactions of biodiesel during its fabrication, in real time, and with relatively low- cost equipment.

Forecasting Construction Cost Index based on visibility graph: A network approach

NASA Astrophysics Data System (ADS)

Zhang, Rong; Ashuri, Baabak; Shyr, Yu; Deng, Yong

2018-03-01

Engineering News-Record (ENR), a professional magazine in the field of global construction engineering, publishes Construction Cost Index (CCI) every month. Cost estimators and contractors assess projects, arrange budgets and prepare bids by forecasting CCI. However, fluctuations and uncertainties of CCI cause irrational estimations now and then. This paper aims at achieving more accurate predictions of CCI based on a network approach in which time series is firstly converted into a visibility graph and future values are forecasted relied on link prediction. According to the experimental results, the proposed method shows satisfactory performance since the error measures are acceptable. Compared with other methods, the proposed method is easier to implement and is able to forecast CCI with less errors. It is convinced that the proposed method is efficient to provide considerably accurate CCI predictions, which will make contributions to the construction engineering by assisting individuals and organizations in reducing costs and making project schedules.

Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

PubMed Central

Islam, Md. Rabiul

2014-01-01

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al. PMID:25114676

Feature and score fusion based multiple classifier selection for iris recognition.

PubMed

Islam, Md Rabiul

2014-01-01

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

A Study on Human Oriented Autonomous Distributed Manufacturing System —Real-time Scheduling Method Based on Preference of Human Operators

NASA Astrophysics Data System (ADS)

Iwamura, Koji; Kuwahara, Shinya; Tanimizu, Yoshitaka; Sugimura, Nobuhiro

Recently, new distributed architectures of manufacturing systems are proposed, aiming at realizing more flexible control structures of the manufacturing systems. Many researches have been carried out to deal with the distributed architectures for planning and control of the manufacturing systems. However, the human operators have not yet been discussed for the autonomous components of the distributed manufacturing systems. A real-time scheduling method is proposed, in this research, to select suitable combinations of the human operators, the resources and the jobs for the manufacturing processes. The proposed scheduling method consists of following three steps. In the first step, the human operators select their favorite manufacturing processes which they will carry out in the next time period, based on their preferences. In the second step, the machine tools and the jobs select suitable combinations for the next machining processes. In the third step, the automated guided vehicles and the jobs select suitable combinations for the next transportation processes. The second and third steps are carried out by using the utility value based method and the dispatching rule-based method proposed in the previous researches. Some case studies have been carried out to verify the effectiveness of the proposed method.

Automated control of robotic camera tacheometers for measurements of industrial large scale objects

NASA Astrophysics Data System (ADS)

Heimonen, Teuvo; Leinonen, Jukka; Sipola, Jani

2013-04-01

The modern robotic tacheometers equipped with digital cameras (called also imaging total stations) and capable to measure reflectorless offer new possibilities to gather 3d data. In this paper an automated approach for the tacheometer measurements needed in the dimensional control of industrial large scale objects is proposed. There are two new contributions in the approach: the automated extraction of the vital points (i.e. the points to be measured) and the automated fine aiming of the tacheometer. The proposed approach proceeds through the following steps: First the coordinates of the vital points are automatically extracted from the computer aided design (CAD) data. The extracted design coordinates are then used to aim the tacheometer to point out to the designed location of the points, one after another. However, due to the deviations between the designed and the actual location of the points, the aiming need to be adjusted. An automated dynamic image-based look-and-move type servoing architecture is proposed to be used for this task. After a successful fine aiming, the actual coordinates of the point in question can be automatically measured by using the measuring functionalities of the tacheometer. The approach was validated experimentally and noted to be feasible. On average 97 % of the points actually measured in four different shipbuilding measurement cases were indeed proposed to be vital points by the automated extraction algorithm. The accuracy of the results obtained with the automatic control method of the tachoemeter were comparable to the results obtained with the manual control, and also the reliability of the image processing step of the method was found to be high in the laboratory experiments.

An Accurate Transmitting Power Control Method in Wireless Communication Transceivers

NASA Astrophysics Data System (ADS)

Zhang, Naikang; Wen, Zhiping; Hou, Xunping; Bi, Bo

2018-01-01

Power control circuits are widely used in transceivers aiming at stabilizing the transmitted signal power to a specified value, thereby reducing power consumption and interference to other frequency bands. In order to overcome the shortcomings of traditional modes of power control, this paper proposes an accurate signal power detection method by multiplexing the receiver and realizes transmitting power control in the digital domain. The simulation results show that this novel digital power control approach has advantages of small delay, high precision and simplified design procedure. The proposed method is applicable to transceivers working at large frequency dynamic range, and has good engineering practicability.

Electrochemical model based charge optimization for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Pramanik, Sourav; Anwar, Sohel

2016-05-01

In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.

Fast methodology of analysing major steviol glycosides from Stevia rebaudiana leaves.

PubMed

Lorenzo, Cándida; Serrano-Díaz, Jéssica; Plaza, Miguel; Quintanilla, Carmen; Alonso, Gonzalo L

2014-08-15

The aim of this work is to propose an HPLC method for analysing major steviol glycosides as well as to optimise the extraction and clarification conditions for obtaining these compounds. Toward this aim, standards of stevioside and rebaudioside A with purities ⩾99.0%, commercial samples from different companies and Stevia rebaudiana Bertoni leaves from Paraguay supplied by Insobol, S.L., were used. The analytical method proposed is adequate in terms of selectivity, sensitivity and accuracy. Optimum extraction conditions and adequate clarification conditions have been set. Moreover, this methodology is safe and eco-friendly, as we use only water for extraction and do not use solid-phase extraction, which requires solvents that are banned in the food industry to condition the cartridge and elute the steviol glycosides. In addition, this methodology consumes little time as leaves are not ground and the filtration is faster, and the peak resolution is better as we used an HPLC method with gradient elution. Copyright © 2014 Elsevier Ltd. All rights reserved.

Listening to Children: Exploring Intuitive Strategies and Interactive Methods in a Study of Children's Special Places

ERIC Educational Resources Information Center

Green, Carie

2012-01-01

Stemming from the UNCRC, childhood researchers have proposed a variety of methodological strategies for upholding children's rights and understanding their perspectives. This paper aims to advance the conversation on engaging children's perspectives by presenting data collection methods used in a qualitative study exploring children's special…

An Impact-Based Filtering Approach for Literature Searches

ERIC Educational Resources Information Center

Vista, Alvin

2013-01-01

This paper aims to present an alternative and simple method to improve the filtering of search results so as to increase the efficiency of literature searches, particularly for individual researchers who have limited logistical resources. The method proposed here is scope restriction using an impact-based filter, made possible by the emergence of…

Alternative evaluation of innovations’ effectiveness in mechanical engineering

NASA Astrophysics Data System (ADS)

Puryaev, A. S.

2017-09-01

The aim of present work is approbation of the developed technique for assessing innovations’ effectiveness. We demonstrate an alternative assessment of innovations’ effectiveness (innovation projects) in mechanical engineering on illustrative example. It is proposed as an alternative to the traditional method technique based on the value concept and the method of “Cash flow”.

Research Knowledge Transfer through Business-Driven Student Assignment

ERIC Educational Resources Information Center

Sas, Corina

2009-01-01

Purpose: The purpose of this paper is to present a knowledge transfer method that capitalizes on both research and teaching dimensions of academic work. It also aims to propose a framework for evaluating the impact of such a method on the involved stakeholders. Design/methodology/approach: The case study outlines and evaluates the six-stage…

A Proposed Methodology for Contextualised Evaluation in Higher Education

ERIC Educational Resources Information Center

Nygaard, Claus; Belluigi, Dina Zoe

2011-01-01

This paper aims to inspire stakeholders working with quality of higher education (such as members of study boards, study programme directors, curriculum developers and teachers) to critically consider their evaluation methods in relation to a focus on student learning. We argue that many of the existing methods of evaluation in higher education…

Exchanging large data object in multi-agent systems

NASA Astrophysics Data System (ADS)

Al-Yaseen, Wathiq Laftah; Othman, Zulaiha Ali; Nazri, Mohd Zakree Ahmad

2016-08-01

One of the Business Intelligent solutions that is currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different location. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was not significantly different compared to the use of the Java Socket method (non-agent and less secure). With such results, it was concluded that the proposed method can be used to securely send large data between agents.

Application of concepts from cross-recurrence analysis in speech production: an overview and comparison with other nonlinear methods.

PubMed

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-06-01

The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space reconstruction is conducted, and a cleaning algorithm removes the artifacts from the recurrence plot. The main features of the proposed approach are robustness to nonstationarity and efficient separation of amplitude variability from temporal variability. The authors tested these claims by applying their method to synthetic stimuli whose variability had been carefully controlled. The proposed method was also demonstrated in a practical application: It was used to investigate the role of biomechanical constraints in articulatory reorganization as a consequence of speeded repetition of CVCV utterances containing a labial and a coronal consonant. Overall, the proposed approach provided more reliable results than other methods, particularly in the presence of high variability. The proposed method is a useful and appropriate tool for quantifying similarity and dissimilarity in patterns of speech articulator movement, especially in such research areas as speech errors and pathologies, where unpredictable divergent behavior is expected.

Novel maximum-margin training algorithms for supervised neural networks.

PubMed

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by MICI, MMGDX, and Levenberg-Marquard (LM), respectively. The resulting neural network was named assembled neural network (ASNN). Benchmark data sets of real-world problems have been used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and balanced error rate.

The Proposed Creation of University System in the Province of Ilocos Sur, Philippines

ERIC Educational Resources Information Center

Alviento, Severino G.

2017-01-01

This study aimed to determine the level of acceptability of the proposed amalgamation of the three Public Higher Education Institutions in Ilocos Sur. It employed the descriptive method of research. The respondents of the study are the 3rd and 4th year AB Political Science and BSE major in Social Science students and the 4th year BEED students of…

Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection.

PubMed

Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

2016-01-01

Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed.

Dynamic video encryption algorithm for H.264/AVC based on a spatiotemporal chaos system.

PubMed

Xu, Hui; Tong, Xiao-Jun; Zhang, Miao; Wang, Zhu; Li, Ling-Hao

2016-06-01

Video encryption schemes mostly employ the selective encryption method to encrypt parts of important and sensitive video information, aiming to ensure the real-time performance and encryption efficiency. The classic block cipher is not applicable to video encryption due to the high computational overhead. In this paper, we propose the encryption selection control module to encrypt video syntax elements dynamically which is controlled by the chaotic pseudorandom sequence. A novel spatiotemporal chaos system and binarization method is used to generate a key stream for encrypting the chosen syntax elements. The proposed scheme enhances the resistance against attacks through the dynamic encryption process and high-security stream cipher. Experimental results show that the proposed method exhibits high security and high efficiency with little effect on the compression ratio and time cost.

Acoustic pressure measurement of pulsed ultrasound using acousto-optic diffraction

NASA Astrophysics Data System (ADS)

Jia, Lecheng; Chen, Shili; Xue, Bin; Wu, Hanzhong; Zhang, Kai; Yang, Xiaoxia; Zeng, Zhoumo

2018-01-01

Compared with continuous ultrasound wave, pulsed ultrasound has been widely used in ultrasound imaging. The aim of this work is to show the applicability of acousto-optic diffraction on pulsed ultrasound transducer. In this paper, acoustic pressure of two ultrasound transducers is measured based on Raman-Nath diffraction. The frequencies of transducers are 5MHz and 10MHz. The pulse-echo method and simulation data are used to evaluate the results. The results show that the proposed method is capable to measure the absolute sound pressure. We get a sectional view of acoustic pressure using a displacement platform as an auxiliary. Compared with the traditional sound pressure measurement methods, the proposed method is non-invasive with high sensitivity and spatial resolution.

Development of Fabrication Methods of Filler/Polymer Nanocomposites: With Focus on Simple Melt-Compounding-Based Approach without Surface Modification of Nanofillers

PubMed Central

Tanahashi, Mitsuru

2010-01-01

Many attempts have been made to fabricate various types of inorganic nanoparticle-filled polymers (filler/polymer nanocomposites) by a mechanical or chemical approach. However, these approaches require modification of the nanofiller surfaces and/or complicated polymerization reactions, making them unsuitable for industrial-scale production of the nanocomposites. The author and coworkers have proposed a simple melt-compounding method for the fabrication of silica/polymer nanocomposites, wherein silica nanoparticles without surface modification were dispersed through the breakdown of loose agglomerates of colloidal nano-silica spheres in a kneaded polymer melt. This review aims to discuss experimental techniques of the proposed method and its advantages over other developed methods.

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

PubMed Central

Shi, Lei; Wan, Youchuan; Gao, Xianjun

2018-01-01

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721

Extended depth of field integral imaging using multi-focus fusion

NASA Astrophysics Data System (ADS)

Piao, Yongri; Zhang, Miao; Wang, Xiaohui; Li, Peihua

2018-03-01

In this paper, we propose a new method for depth of field extension in integral imaging by realizing the image fusion method on the multi-focus elemental images. In the proposed method, a camera is translated on a 2D grid to take multi-focus elemental images by sweeping the focus plane across the scene. Simply applying an image fusion method on the elemental images holding rich parallax information does not work effectively because registration accuracy of images is the prerequisite for image fusion. To solve this problem an elemental image generalization method is proposed. The aim of this generalization process is to geometrically align the objects in all elemental images so that the correct regions of multi-focus elemental images can be exacted. The all-in focus elemental images are then generated by fusing the generalized elemental images using the block based fusion method. The experimental results demonstrate that the depth of field of synthetic aperture integral imaging system has been extended by realizing the generation method combined with the image fusion on multi-focus elemental images in synthetic aperture integral imaging system.

Experimental Quasi-Microwave Whole-Body Averaged SAR Estimation Method Using Cylindrical-External Field Scanning

NASA Astrophysics Data System (ADS)

Kawamura, Yoshifumi; Hikage, Takashi; Nojima, Toshio

The aim of this study is to develop a new whole-body averaged specific absorption rate (SAR) estimation method based on the external-cylindrical field scanning technique. This technique is adopted with the goal of simplifying the dosimetry estimation of human phantoms that have different postures or sizes. An experimental scaled model system is constructed. In order to examine the validity of the proposed method for realistic human models, we discuss the pros and cons of measurements and numerical analyses based on the finite-difference time-domain (FDTD) method. We consider the anatomical European human phantoms and plane-wave in the 2GHz mobile phone frequency band. The measured whole-body averaged SAR results obtained by the proposed method are compared with the results of the FDTD analyses.

Modeling crime events by d-separation method

NASA Astrophysics Data System (ADS)

Aarthee, R.; Ezhilmaran, D.

2017-11-01

Problematic legal cases have recently called for a scientifically founded method of dealing with the qualitative and quantitative roles of evidence in a case [1].To deal with quantitative, we proposed a d-separation method for modeling the crime events. A d-separation is a graphical criterion for identifying independence in a directed acyclic graph. By developing a d-separation method, we aim to lay the foundations for the development of a software support tool that can deal with the evidential reasoning in legal cases. Such a tool is meant to be used by a judge or juror, in alliance with various experts who can provide information about the details. This will hopefully improve the communication between judges or jurors and experts. The proposed method used to uncover more valid independencies than any other graphical criterion.

A Study on Real-Time Scheduling Methods in Holonic Manufacturing Systems

NASA Astrophysics Data System (ADS)

Iwamura, Koji; Taimizu, Yoshitaka; Sugimura, Nobuhiro

Recently, new architectures of manufacturing systems have been proposed to realize flexible control structures of the manufacturing systems, which can cope with the dynamic changes in the volume and the variety of the products and also the unforeseen disruptions, such as failures of manufacturing resources and interruptions by high priority jobs. They are so called as the autonomous distributed manufacturing system, the biological manufacturing system and the holonic manufacturing system. Rule-based scheduling methods were proposed and applied to the real-time production scheduling problems of the HMS (Holonic Manufacturing System) in the previous report. However, there are still remaining problems from the viewpoint of the optimization of the whole production schedules. New procedures are proposed, in the present paper, to select the production schedules, aimed at generating effective production schedules in real-time. The proposed methods enable the individual holons to select suitable machining operations to be carried out in the next time period. Coordination process among the holons is also proposed to carry out the coordination based on the effectiveness values of the individual holons.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

A New Group-Formation Method for Student Projects

ERIC Educational Resources Information Center

Borges, Jose; Dias, Teresa Galvao; Cunha, Joao Falcao E.

2009-01-01

In BSc/MSc engineering programmes at Faculty of Engineering of the University of Porto (FEUP), the need to provide students with teamwork experiences close to a real world environment was identified as an important issue. A new group-formation method that aims to provide an enriching teamwork experience is proposed. Students are asked to answer a…

Data-Driven Hint Generation from Peer Debugging Solutions

ERIC Educational Resources Information Center

Liu, Zhongxiu

2015-01-01

Data-driven methods have been a successful approach to generating hints for programming problems. However, the majority of previous studies are focused on procedural hints that aim at moving students to the next closest state to the solution. In this paper, I propose a data-driven method to generate remedy hints for BOTS, a game that teaches…

Case Method in the Teaching of Food Safety

ERIC Educational Resources Information Center

Gallego, Alfredo; Fortunato, Maria S.; Rossi, Susana L.; Korol, Sonia E.; Moretton, Juan A.

2013-01-01

One of the fundamental aims of education is the integration of theory and practice. The case method is a teaching strategy in which students must apply their knowledge to solve real-life situations. They have to analyze the case described and propose the best possible solution. Although the case may be written, the use of new information and…

Dynamic Scheduling for Web Monitoring Crawler

DTIC Science & Technology

2009-02-27

researches on static scheduling methods , but they are not included in this project, because this project mainly focuses on the event-driven...pages from public search engines. This research aims to propose various query generation methods using MCRDR knowledge base and evaluates them to...South Wales Professor Hiroshi Motoda/Osaka University Dr. John Salerno, Air Force Research Laboratory/Information Directorate Report

Locating Structural Centers: A Density-Based Clustering Method for Community Detection

PubMed Central

Liu, Gongshen; Li, Jianhua; Nees, Jan P.

2017-01-01

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods. PMID:28046030

Analysis of optimisation method for a two-stroke piston ring using the Finite Element Method and the Simulated Annealing Method

NASA Astrophysics Data System (ADS)

Kaliszewski, M.; Mazuro, P.

2016-09-01

Simulated Annealing Method of optimisation for the sealing piston ring geometry is tested. The aim of optimisation is to develop ring geometry which would exert demanded pressure on a cylinder just while being bended to fit the cylinder. Method of FEM analysis of an arbitrary piston ring geometry is applied in an ANSYS software. The demanded pressure function (basing on formulae presented by A. Iskra) as well as objective function are introduced. Geometry definition constructed by polynomials in radial coordinate system is delivered and discussed. Possible application of Simulated Annealing Method in a piston ring optimisation task is proposed and visualised. Difficulties leading to possible lack of convergence of optimisation are presented. An example of an unsuccessful optimisation performed in APDL is discussed. Possible line of further optimisation improvement is proposed.

Method Engineering: A Service-Oriented Approach

NASA Astrophysics Data System (ADS)

Cauvet, Corine

In the past, a large variety of methods have been published ranging from very generic frameworks to methods for specific information systems. Method Engineering has emerged as a research discipline for designing, constructing and adapting methods for Information Systems development. Several approaches have been proposed as paradigms in method engineering. The meta modeling approach provides means for building methods by instantiation, the component-based approach aims at supporting the development of methods by using modularization constructs such as method fragments, method chunks and method components. This chapter presents an approach (SO2M) for method engineering based on the service paradigm. We consider services as autonomous computational entities that are self-describing, self-configuring and self-adapting. They can be described, published, discovered and dynamically composed for processing a consumer's demand (a developer's requirement). The method service concept is proposed to capture a development process fragment for achieving a goal. Goal orientation in service specification and the principle of service dynamic composition support method construction and method adaptation to different development contexts.

Intelligent design of permanent magnet synchronous motor based on CBR

NASA Astrophysics Data System (ADS)

Li, Cong; Fan, Beibei

2018-05-01

Aiming at many problems in the design process of Permanent magnet synchronous motor (PMSM), such as the complexity of design process, the over reliance on designers' experience and the lack of accumulation and inheritance of design knowledge, a design method of PMSM Based on CBR is proposed in order to solve those problems. In this paper, case-based reasoning (CBR) methods of cases similarity calculation is proposed for reasoning suitable initial scheme. This method could help designers, by referencing previous design cases, to make a conceptual PMSM solution quickly. The case retain process gives the system self-enrich function which will improve the design ability of the system with the continuous use of the system.

Plenoptic image watermarking to preserve copyright

NASA Astrophysics Data System (ADS)

Ansari, A.; Dorado, A.; Saavedra, G.; Martinez Corral, M.

2017-05-01

Common camera loses a huge amount of information obtainable from scene as it does not record the value of individual rays passing a point and it merely keeps the summation of intensities of all the rays passing a point. Plenoptic images can be exploited to provide a 3D representation of the scene and watermarking such images can be helpful to protect the ownership of these images. In this paper we propose a method for watermarking the plenoptic images to achieve this aim. The performance of the proposed method is validated by experimental results and a compromise is held between imperceptibility and robustness.

Evaluating a normalized conceptual representation produced from natural language patient discharge summaries.

PubMed Central

Zweigenbaum, P.; Bouaud, J.; Bachimont, B.; Charlet, J.; Boisvieux, J. F.

1997-01-01

The Menelas project aimed to produce a normalized conceptual representation from natural language patient discharge summaries. Because of the complex and detailed nature of conceptual representations, evaluating the quality of output of such a system is difficult. We present the method designed to measure the quality of Menelas output, and its application to the state of the French Menelas prototype as of the end of the project. We examine this method in the framework recently proposed by Friedman and Hripcsak. We also propose two conditions which enable to reduce the evaluation preparation workload. PMID:9357694

3D Measurement of Anatomical Cross-sections of Foot while Walking

NASA Astrophysics Data System (ADS)

Kimura, Makoto; Mochimaru, Masaaki; Kanade, Takeo

Recently, techniques for measuring and modeling of human body are taking attention, because human models are useful for ergonomic design in manufacturing. We aim to measure accurate shape of human foot that will be useful for the design of shoes. For such purpose, shape measurement of foot in motion is obviously important, because foot shape in the shoe is deformed while walking or running. In this paper, we propose a method to measure anatomical cross-sections of foot while walking. No one had ever measured dynamic shape of anatomical cross-sections, though they are very basic and popular in the field of biomechanics. Our proposed method is based on multi-view stereo method. The target cross-sections are painted in individual colors (red, green, yellow and blue), and the proposed method utilizes the characteristic of target shape in the camera captured images. Several nonlinear conditions are introduced in the process to find the consistent correspondence in all images. Our desired accuracy is less than 1mm error, which is similar to the existing 3D scanners for static foot measurement. In our experiments, the proposed method achieved the desired accuracy.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.

PubMed

Park, Hyunseok; Magee, Christopher L

2017-01-01

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.

Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach

PubMed Central

2017-01-01

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents. PMID:28135304

The Application of Auto-Disturbance Rejection Control Optimized by Least Squares Support Vector Machines Method and Time-Frequency Representation in Voltage Source Converter-High Voltage Direct Current System.

PubMed

Liu, Ying-Pei; Liang, Hai-Ping; Gao, Zhong-Ke

2015-01-01

In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD) and adaptive optimal kernel (AOK) time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.

The Application of Auto-Disturbance Rejection Control Optimized by Least Squares Support Vector Machines Method and Time-Frequency Representation in Voltage Source Converter-High Voltage Direct Current System

PubMed Central

Gao, Zhong-Ke

2015-01-01

In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD) and adaptive optimal kernel (AOK) time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane. PMID:26098556

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

On the use of the fourier modal method for calculation of localized eigenmodes of integrated optical resonators