Standardized residual as response function for order identification of multi input intervention analysis

NASA Astrophysics Data System (ADS)

Suhartono, Lee, Muhammad Hisyam; Rezeki, Sri

2017-05-01

Intervention analysis is a statistical model in the group of time series analysis which is widely used to describe the effect of an intervention caused by external or internal factors. An example of external factors that often occurs in Indonesia is a disaster, both natural or man-made disaster. The main purpose of this paper is to provide the results of theoretical studies on identification step for determining the order of multi inputs intervention analysis for evaluating the magnitude and duration of the impact of interventions on time series data. The theoretical result showed that the standardized residuals could be used properly as response function for determining the order of multi inputs intervention model. Then, these results are applied for evaluating the impact of a disaster on a real case in Indonesia, i.e. the magnitude and duration of the impact of the Lapindo mud on the volume of vehicles on the highway. Moreover, the empirical results showed that the multi inputs intervention model can describe and explain accurately the magnitude and duration of the impact of disasters on a time series data.

Development and testing of controller performance evaluation methodology for multi-input/multi-output digital control systems

NASA Technical Reports Server (NTRS)

Pototzky, Anthony; Wieseman, Carol; Hoadley, Sherwood Tiffany; Mukhopadhyay, Vivek

1991-01-01

Described here is the development and implementation of on-line, near real time controller performance evaluation (CPE) methods capability. Briefly discussed are the structure of data flow, the signal processing methods used to process the data, and the software developed to generate the transfer functions. This methodology is generic in nature and can be used in any type of multi-input/multi-output (MIMO) digital controller application, including digital flight control systems, digitally controlled spacecraft structures, and actively controlled wind tunnel models. Results of applying the CPE methodology to evaluate (in near real time) MIMO digital flutter suppression systems being tested on the Rockwell Active Flexible Wing (AFW) wind tunnel model are presented to demonstrate the CPE capability.

MMX-I: data-processing software for multimodal X-ray imaging and tomography.

PubMed

Bergamaschi, Antoine; Medjoubi, Kadda; Messaoudi, Cédric; Marco, Sergio; Somogyi, Andrea

2016-05-01

A new multi-platform freeware has been developed for the processing and reconstruction of scanning multi-technique X-ray imaging and tomography datasets. The software platform aims to treat different scanning imaging techniques: X-ray fluorescence, phase, absorption and dark field and any of their combinations, thus providing an easy-to-use data processing tool for the X-ray imaging user community. A dedicated data input stream copes with the input and management of large datasets (several hundred GB) collected during a typical multi-technique fast scan at the Nanoscopium beamline and even on a standard PC. To the authors' knowledge, this is the first software tool that aims at treating all of the modalities of scanning multi-technique imaging and tomography experiments.

Transport and retention of multi-walled carbon nanotubes in saturated porous media: Effects of input concentration and grain size

USDA-ARS?s Scientific Manuscript database

Water-saturated column experiments were conducted to investigate the effect of input concentration (Co) and sand grain size on the transport and retention of low concentrations (1, 0.01, and 0.005 mg L/1) of functionalized 14C-labeled multi-walled carbon nanotubes (MWCNT) under repulsive electrostat...

Statistical linearization for multi-input/multi-output nonlinearities

NASA Technical Reports Server (NTRS)

Lin, Ching-An; Cheng, Victor H. L.

1991-01-01

Formulas are derived for the computation of the random input-describing functions for MIMO nonlinearities; these straightforward and rigorous derivations are based on the optimal mean square linear approximation. The computations involve evaluations of multiple integrals. It is shown that, for certain classes of nonlinearities, multiple-integral evaluations are obviated and the computations are significantly simplified.

MMX-I: data-processing software for multimodal X-ray imaging and tomography

PubMed Central

Bergamaschi, Antoine; Medjoubi, Kadda; Messaoudi, Cédric; Marco, Sergio; Somogyi, Andrea

2016-01-01

A new multi-platform freeware has been developed for the processing and reconstruction of scanning multi-technique X-ray imaging and tomography datasets. The software platform aims to treat different scanning imaging techniques: X-ray fluorescence, phase, absorption and dark field and any of their combinations, thus providing an easy-to-use data processing tool for the X-ray imaging user community. A dedicated data input stream copes with the input and management of large datasets (several hundred GB) collected during a typical multi-technique fast scan at the Nanoscopium beamline and even on a standard PC. To the authors’ knowledge, this is the first software tool that aims at treating all of the modalities of scanning multi-technique imaging and tomography experiments. PMID:27140159

Time series modeling of human operator dynamics in manual control tasks

NASA Technical Reports Server (NTRS)

Biezad, D. J.; Schmidt, D. K.

1984-01-01

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency responses of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that has not been previously modeled to demonstrate the strengths of the method.

Time Series Modeling of Human Operator Dynamics in Manual Control Tasks

NASA Technical Reports Server (NTRS)

Biezad, D. J.; Schmidt, D. K.

1984-01-01

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

PubMed

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Multi-party quantum summation without a trusted third party based on single particles

NASA Astrophysics Data System (ADS)

Zhang, Cai; Situ, Haozhen; Huang, Qiong; Yang, Pingle

We propose multi-party quantum summation protocols based on single particles, in which participants are allowed to compute the summation of their inputs without the help of a trusted third party and preserve the privacy of their inputs. Only one participant who generates the source particles needs to perform unitary operations and only single particles are needed in the beginning of the protocols.

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

A Reconfiguration Scheme for Accommodating Actuator Failures in Multi-Input, Multi-Output Flight Control Systems

NASA Technical Reports Server (NTRS)

Siwakosit, W.; Hess, R. A.; Bacon, Bart (Technical Monitor); Burken, John (Technical Monitor)

2000-01-01

A multi-input, multi-output reconfigurable flight control system design utilizing a robust controller and an adaptive filter is presented. The robust control design consists of a reduced-order, linear dynamic inversion controller with an outer-loop compensation matrix derived from Quantitative Feedback Theory (QFT). A principle feature of the scheme is placement of the adaptive filter in series with the QFT compensator thus exploiting the inherent robustness of the nominal flight control system in the presence of plant uncertainties. An example of the scheme is presented in a pilot-in-the-loop computer simulation using a simplified model of the lateral-directional dynamics of the NASA F18 High Angle of Attack Research Vehicle (HARV) that included nonlinear anti-wind up logic and actuator limitations. Prediction of handling qualities and pilot-induced oscillation tendencies in the presence of these nonlinearities is included in the example.

A multi-channel isolated power supply in non-equipotential circuit

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhao, Bo-Wen; Zhang, Yan-Chi; Xie, Da

2018-04-01

A multi-channel isolation power supply is designed for the problems of different MOSFET or IGBT in the non-equipotential circuit in this paper. It mainly includes the square wave generation circuit, the high-frequency transformer and the three-terminal stabilized circuit. The first part is used to generate the 24V square wave, and as the input of the magnetic ring transformer. In the second part, the magnetic ring transformer consists of one input and three outputs to realize multi-channel isolation output. The third part can output different potential and realize non-equal potential function through the three-terminal stabilized chip. In addition, the multi-channel isolation power source proposed in this paper is Small size, high reliability and low price, and it is convenient for power electronic switches that operate on multiple different potentials. Therefore, the research on power supply of power electronic circuit has practical significance.

Focal ratio degradation in lightly fused hexabundles

NASA Astrophysics Data System (ADS)

Bryant, J. J.; Bland-Hawthorn, J.; Fogarty, L. M. R.; Lawrence, J. S.; Croom, S. M.

2014-02-01

We are now moving into an era where multi-object wide-field surveys, which traditionally use single fibres to observe many targets simultaneously, can exploit compact integral field units (IFUs) in place of single fibres. Current multi-object integral field instruments such as Sydney-AAO Multi-object Integral field spectrograph have driven the development of new imaging fibre bundles (hexabundles) for multi-object spectrographs. We have characterized the performance of hexabundles with different cladding thicknesses and compared them to that of the same type of bare fibre, across the range of fill fractions and input f-ratios likely in an IFU instrument. Hexabundles with 7-cores and 61-cores were tested for focal ratio degradation (FRD), throughput and cross-talk when fed with inputs from F/3.4 to >F/8. The five 7-core bundles have cladding thickness ranging from 1 to 8 μm, and the 61-core bundles have 5 μm cladding. As expected, the FRD improves as the input focal ratio decreases. We find that the FRD and throughput of the cores in the hexabundles match the performance of single fibres of the same material at low input f-ratios. The performance results presented can be used to set a limit on the f-ratio of a system based on the maximum loss allowable for a planned instrument. Our results confirm that hexabundles are a successful alternative for fibre imaging devices for multi-object spectroscopy on wide-field telescopes and have prompted further development of hexabundle designs with hexagonal packing and square cores.

Dual frequency parametric excitation of a nonlinear, multi degree of freedom mechanical amplifier with electronically modified topology

NASA Astrophysics Data System (ADS)

Dolev, A.; Bucher, I.

2018-04-01

Mechanical or electromechanical amplifiers can exploit the high-Q and low noise features of mechanical resonance, in particular when parametric excitation is employed. Multi-frequency parametric excitation introduces tunability and is able to project weak input signals on a selected resonance. The present paper addresses multi degree of freedom mechanical amplifiers or resonators whose analysis and features require treatment of the spatial as well as temporal behavior. In some cases, virtual electronic coupling can alter the given topology of the resonator to better amplify specific inputs. An analytical development is followed by a numerical and experimental sensitivity and performance verifications, illustrating the advantages and disadvantages of such topologies.

BaTMAn: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2016-12-01

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

Low-carbon building assessment and multi-scale input-output analysis

NASA Astrophysics Data System (ADS)

Chen, G. Q.; Chen, H.; Chen, Z. M.; Zhang, Bo; Shao, L.; Guo, S.; Zhou, S. Y.; Jiang, M. M.

2011-01-01

Presented as a low-carbon building evaluation framework in this paper are detailed carbon emission account procedures for the life cycle of buildings in terms of nine stages as building construction, fitment, outdoor facility construction, transportation, operation, waste treatment, property management, demolition, and disposal for buildings, supported by integrated carbon intensity databases based on multi-scale input-output analysis, essential for low-carbon planning, procurement and supply chain design, and logistics management.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

PubMed Central

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Improved disturbance rejection for predictor-based control of MIMO linear systems with input delay

NASA Astrophysics Data System (ADS)

Shi, Shang; Liu, Wenhui; Lu, Junwei; Chu, Yuming

2018-02-01

In this paper, we are concerned with the predictor-based control of multi-input multi-output (MIMO) linear systems with input delay and disturbances. By taking the future values of disturbances into consideration, a new improved predictive scheme is proposed. Compared with the existing predictive schemes, our proposed predictive scheme can achieve a finite-time exact state prediction for some smooth disturbances including the constant disturbances, and a better disturbance attenuation can also be achieved for a large class of other time-varying disturbances. The attenuation of mismatched disturbances for second-order linear systems with input delay is also investigated by using our proposed predictor-based controller.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

NASA Astrophysics Data System (ADS)

Cui, Guozeng; Xu, Shengyuan; Ma, Qian; Li, Yongmin; Zhang, Zhengqiang

2018-05-01

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

NASA Astrophysics Data System (ADS)

Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

2018-03-01

Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

Development of a compact and cost effective multi-input digital signal processing system

NASA Astrophysics Data System (ADS)

Darvish-Molla, Sahar; Chin, Kenrick; Prestwich, William V.; Byun, Soo Hyun

2018-01-01

A prototype digital signal processing system (DSP) was developed using a microcontroller interfaced with a 12-bit sampling ADC, which offers a considerably inexpensive solution for processing multiple detectors with high throughput. After digitization of the incoming pulses, in order to maximize the output counting rate, a simple algorithm was employed for pulse height analysis. Moreover, an algorithm aiming at the real-time pulse pile-up deconvolution was implemented. The system was tested using a NaI(Tl) detector in comparison with a traditional analogue and commercial digital systems for a variety of count rates. The performance of the prototype system was consistently superior to the analogue and the commercial digital systems up to the input count rate of 61 kcps while was slightly inferior to the commercial digital system but still superior to the analogue system in the higher input rates. Considering overall cost, size and flexibility, this custom made multi-input digital signal processing system (MMI-DSP) was the best reliable choice for the purpose of the 2D microdosimetric data collection, or for any measurement in which simultaneous multi-data collection is required.

Continuous-Time Bilinear System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

2003-01-01

The objective of this paper is to describe a new method for identification of a continuous-time multi-input and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constant input of one sample period to identify the state matrix, the output matrix, and the direct transmission matrix. The second step is to use another set of pulse responses with the same constant input over multiple sample periods to identify the input matrix and the coefficient matrices associated with the coupling terms between the state and the inputs. Numerical examples are given to illustrate the concept and the computational algorithm for the identification method.

Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography

NASA Astrophysics Data System (ADS)

Tachibana, Rie; Näppi, Janne J.; Hironaka, Toru; Kim, Se Hyung; Yoshida, Hiroyuki

2017-03-01

We developed a novel electronic cleansing (EC) method for dual-energy CT colonography (DE-CTC) based on an ensemble deep convolution neural network (DCNN) and multi-spectral multi-slice image patches. In the method, an ensemble DCNN is used to classify each voxel of a DE-CTC image volume into five classes: luminal air, soft tissue, tagged fecal materials, and partial-volume boundaries between air and tagging and those between soft tissue and tagging. Each DCNN acts as a voxel classifier, where an input image patch centered at the voxel is generated as input to the DCNNs. An image patch has three channels that are mapped from a region-of-interest containing the image plane of the voxel and the two adjacent image planes. Six different types of spectral input image datasets were derived using two dual-energy CT images, two virtual monochromatic images, and two material images. An ensemble DCNN was constructed by use of a meta-classifier that combines the output of multiple DCNNs, each of which was trained with a different type of multi-spectral image patches. The electronically cleansed CTC images were calculated by removal of regions classified as other than soft tissue, followed by a colon surface reconstruction. For pilot evaluation, 359 volumes of interest (VOIs) representing sources of subtraction artifacts observed in current EC schemes were sampled from 30 clinical CTC cases. Preliminary results showed that the ensemble DCNN can yield high accuracy in labeling of the VOIs, indicating that deep learning of multi-spectral EC with multi-slice imaging could accurately remove residual fecal materials from CTC images without generating major EC artifacts.

Life Cycle Assessment of Mixed Municipal Solid Waste: Multi-input versus multi-output perspective.

PubMed

Fiorentino, G; Ripa, M; Protano, G; Hornsby, C; Ulgiati, S

2015-12-01

This paper analyses four strategies for managing the Mixed Municipal Solid Waste (MMSW) in terms of their environmental impacts and potential advantages by means of Life Cycle Assessment (LCA) methodology. To this aim, both a multi-input and a multi-output approach are applied to evaluate the effect of these perspectives on selected impact categories. The analyzed management options include direct landfilling with energy recovery (S-1), Mechanical-Biological Treatment (MBT) followed by Waste-to-Energy (WtE) conversion (S-2), a combination of an innovative MBT/MARSS (Material Advanced Recovery Sustainable Systems) process and landfill disposal (S-3), and finally a combination of the MBT/MARSS process with WtE conversion (S-4). The MARSS technology, developed within an European LIFE PLUS framework and currently implemented at pilot plant scale, is an innovative MBT plant having the main goal to yield a Renewable Refined Biomass Fuel (RRBF) to be used for combined heat and power production (CHP) under the regulations enforced for biomass-based plants instead of Waste-to-Energy systems, for increased environmental performance. The four scenarios are characterized by different resource investment for plant and infrastructure construction and different quantities of matter, heat and electricity recovery and recycling. Results, calculated per unit mass of waste treated and per unit exergy delivered, under both multi-input and multi-output LCA perspectives, point out improved performance for scenarios characterized by increased matter and energy recovery. Although none of the investigated scenarios is capable to provide the best performance in all the analyzed impact categories, the scenario S-4 shows the best LCA results in the human toxicity and freshwater eutrophication categories, i.e. the ones with highest impacts in all waste management processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multi-valued logic gates based on ballistic transport in quantum point contacts.

PubMed

Seo, M; Hong, C; Lee, S-Y; Choi, H K; Kim, N; Chung, Y; Umansky, V; Mahalu, D

2014-01-22

Multi-valued logic gates, which can handle quaternary numbers as inputs, are developed by exploiting the ballistic transport properties of quantum point contacts in series. The principle of a logic gate that finds the minimum of two quaternary number inputs is demonstrated. The device is scalable to allow multiple inputs, which makes it possible to find the minimum of multiple inputs in a single gate operation. Also, the principle of a half-adder for quaternary number inputs is demonstrated. First, an adder that adds up two quaternary numbers and outputs the sum of inputs is demonstrated. Second, a device to express the sum of the adder into two quaternary digits [Carry (first digit) and Sum (second digit)] is demonstrated. All the logic gates presented in this paper can in principle be extended to allow decimal number inputs with high quality QPCs.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Network module detection: Affinity search technique with the multi-node topological overlap measure

PubMed Central

Li, Ai; Horvath, Steve

2009-01-01

Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: PMID:19619323

Network module detection: Affinity search technique with the multi-node topological overlap measure.

PubMed

Li, Ai; Horvath, Steve

2009-07-20

Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

Decision aids for multiple-decision disease management as affected by weather input errors.

PubMed

Pfender, W F; Gent, D H; Mahaffee, W F; Coop, L B; Fox, A D

2011-06-01

Many disease management decision support systems (DSSs) rely, exclusively or in part, on weather inputs to calculate an indicator for disease hazard. Error in the weather inputs, typically due to forecasting, interpolation, or estimation from off-site sources, may affect model calculations and management decision recommendations. The extent to which errors in weather inputs affect the quality of the final management outcome depends on a number of aspects of the disease management context, including whether management consists of a single dichotomous decision, or of a multi-decision process extending over the cropping season(s). Decision aids for multi-decision disease management typically are based on simple or complex algorithms of weather data which may be accumulated over several days or weeks. It is difficult to quantify accuracy of multi-decision DSSs due to temporally overlapping disease events, existence of more than one solution to optimizing the outcome, opportunities to take later recourse to modify earlier decisions, and the ongoing, complex decision process in which the DSS is only one component. One approach to assessing importance of weather input errors is to conduct an error analysis in which the DSS outcome from high-quality weather data is compared with that from weather data with various levels of bias and/or variance from the original data. We illustrate this analytical approach for two types of DSS, an infection risk index for hop powdery mildew and a simulation model for grass stem rust. Further exploration of analysis methods is needed to address problems associated with assessing uncertainty in multi-decision DSSs.

Self tuning control of wind-diesel power systems

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

Mufti, M.D.; Balasubramanian, R.; Tripathy, S.C.

1995-12-31

This paper proposes some effective self-tuning control strategies for isolated Wind-Diesel power generation systems. Detailed modeling and studies on both single-input single-output (SISO) as well as multi-input multi-output (MIMO) self tuning regulators, applied to a typical system, are reported. Further, the effect of introducing a Super-conducting Magnetic Energy Storage (SMES) unit on the system performance has been investigated. The MIMO self-tuning regulator controlling the hybrid system and the SMES in a coordinated manner exhibits the best performance.

Application of Multi-Input Multi-Output Feedback Control for F-16 Ventral Fin Buffet Alleviation Using Piezoelectric Actuators

DTIC Science & Technology

2012-03-22

Power Amplifier (7). A power amplifier was required to drive the actuators. For this research a Trek , Inc. Model PZD 700 Dual Channel Amplifier was used...while the flight test amplifier was being built. The Trek amplifier was capable of amplifying 32 Figure 3.19: dSpace MicroAutoBox II Digital...averaging of 25% was used to reduce the errors caused by noise but still maintain accuracy. For the laboratory Trek amplifier, a 100 millivolt input

On controllability of homogeneous and inhomogeneous discrete-time multi-input bilinear systems in dimension two

NASA Astrophysics Data System (ADS)

Tie, Lin

2017-08-01

In this paper, the controllability problem of two-dimensional discrete-time multi-input bilinear systems is completely solved. The homogeneous and the inhomogeneous cases are studied separately and necessary and sufficient conditions for controllability are established by using a linear algebraic method, which are easy to apply. Moreover, for the uncontrollable systems, near-controllability is considered and similar necessary and sufficient conditions are also obtained. Finally, examples are provided to demonstrate the results of this paper.

MIMO system identification using frequency response data

NASA Technical Reports Server (NTRS)

Medina, Enrique A.; Irwin, R. D.; Mitchell, Jerrel R.; Bukley, Angelia P.

1992-01-01

A solution to the problem of obtaining a multi-input, multi-output statespace model of a system from its individual input/output frequency responses is presented. The Residue Identification Algorithm (RID) identifies the system poles from a transfer function model of the determinant of the frequency response data matrix. Next, the residue matrices of the modes are computed guaranteeing that each input/output frequency response is fitted in the least squares sense. Finally, a realization of the system is computed. Results of the application of RID to experimental frequency responses of a large space structure ground test facility are presented and compared to those obtained via the Eigensystem Realization Algorithm.

Noise Suppression Based on Multi-Model Compositions Using Multi-Pass Search with Multi-Label N-gram Models

NASA Astrophysics Data System (ADS)

Jitsuhiro, Takatoshi; Toriyama, Tomoji; Kogure, Kiyoshi

We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.

Single- and multi-channel underwater acoustic communication channel capacity: a computational study.

PubMed

Hayward, Thomas J; Yang, T C

2007-09-01

Acoustic communication channel capacity determines the maximum data rate that can be supported by an acoustic channel for a given source power and source/receiver configuration. In this paper, broadband acoustic propagation modeling is applied to estimate the channel capacity for a time-invariant shallow-water waveguide for a single source-receiver pair and for vertical source and receiver arrays. Without bandwidth constraints, estimated single-input, single-output (SISO) capacities approach 10 megabitss at 1 km range, but beyond 2 km range they decay at a rate consistent with previous estimates by Peloquin and Leinhos (unpublished, 1997), which were based on a sonar equation calculation. Channel capacities subject to source bandwidth constraints are approximately 30-90% lower than for the unconstrained case, and exhibit a significant wind speed dependence. Channel capacity is investigated for single-input, multi-output (SIMO) and multi-input, multi-output (MIMO) systems, both for finite arrays and in the limit of a dense array spanning the entire water column. The limiting values of the SIMO and MIMO channel capacities for the modeled environment are found to be about four times higher and up to 200-400 times higher, respectively, than for the SISO case. Implications for underwater acoustic communication systems are discussed.

Investigation of Multi-Input Multi-Output Robust Control Methods to Handle Parametric Uncertainties in Autopilot Design.

PubMed

Kasnakoğlu, Coşku

2016-01-01

Some level of uncertainty is unavoidable in acquiring the mass, geometry parameters and stability derivatives of an aerial vehicle. In certain instances tiny perturbations of these could potentially cause considerable variations in flight characteristics. This research considers the impact of varying these parameters altogether. This is a generalization of examining the effects of particular parameters on selected modes present in existing literature. Conventional autopilot designs commonly assume that each flight channel is independent and develop single-input single-output (SISO) controllers for every one, that are utilized in parallel for actual flight. It is demonstrated that an attitude controller built like this can function flawlessly on separate nominal cases, but can become unstable with a perturbation no more than 2%. Two robust multi-input multi-output (MIMO) design strategies, specifically loop-shaping and μ-synthesis are outlined as potential substitutes and are observed to handle large parametric changes of 30% while preserving decent performance. Duplicating the loop-shaping procedure for the outer loop, a complete flight control system is formed. It is confirmed through software-in-the-loop (SIL) verifications utilizing blade element theory (BET) that the autopilot is capable of navigation and landing exposed to high parametric variations and powerful winds.

Investigation of Multi-Input Multi-Output Robust Control Methods to Handle Parametric Uncertainties in Autopilot Design

PubMed Central

Kasnakoğlu, Coşku

2016-01-01

Some level of uncertainty is unavoidable in acquiring the mass, geometry parameters and stability derivatives of an aerial vehicle. In certain instances tiny perturbations of these could potentially cause considerable variations in flight characteristics. This research considers the impact of varying these parameters altogether. This is a generalization of examining the effects of particular parameters on selected modes present in existing literature. Conventional autopilot designs commonly assume that each flight channel is independent and develop single-input single-output (SISO) controllers for every one, that are utilized in parallel for actual flight. It is demonstrated that an attitude controller built like this can function flawlessly on separate nominal cases, but can become unstable with a perturbation no more than 2%. Two robust multi-input multi-output (MIMO) design strategies, specifically loop-shaping and μ-synthesis are outlined as potential substitutes and are observed to handle large parametric changes of 30% while preserving decent performance. Duplicating the loop-shaping procedure for the outer loop, a complete flight control system is formed. It is confirmed through software-in-the-loop (SIL) verifications utilizing blade element theory (BET) that the autopilot is capable of navigation and landing exposed to high parametric variations and powerful winds. PMID:27783706

Multi-service highly sensitive rectifier for enhanced RF energy scavenging.

PubMed

Shariati, Negin; Rowe, Wayne S T; Scott, James R; Ghorbani, Kamran

2015-05-07

Due to the growing implications of energy costs and carbon footprints, the need to adopt inexpensive, green energy harvesting strategies are of paramount importance for the long-term conservation of the environment and the global economy. To address this, the feasibility of harvesting low power density ambient RF energy simultaneously from multiple sources is examined. A high efficiency multi-resonant rectifier is proposed, which operates at two frequency bands (478-496 and 852-869 MHz) and exhibits favorable impedance matching over a broad input power range (-40 to -10 dBm). Simulation and experimental results of input reflection coefficient and rectified output power are in excellent agreement, demonstrating the usefulness of this innovative low-power rectification technique. Measurement results indicate an effective efficiency of 54.3%, and an output DC voltage of 772.8 mV is achieved for a multi-tone input power of -10 dBm. Furthermore, the measured output DC power from harvesting RF energy from multiple services concurrently exhibits a 3.14 and 7.24 fold increase over single frequency rectification at 490 and 860 MHz respectively. Therefore, the proposed multi-service highly sensitive rectifier is a promising technique for providing a sustainable energy source for low power applications in urban environments.

Multi-Service Highly Sensitive Rectifier for Enhanced RF Energy Scavenging

PubMed Central

Shariati, Negin; Rowe, Wayne S. T.; Scott, James R.; Ghorbani, Kamran

2015-01-01

Due to the growing implications of energy costs and carbon footprints, the need to adopt inexpensive, green energy harvesting strategies are of paramount importance for the long-term conservation of the environment and the global economy. To address this, the feasibility of harvesting low power density ambient RF energy simultaneously from multiple sources is examined. A high efficiency multi-resonant rectifier is proposed, which operates at two frequency bands (478–496 and 852–869 MHz) and exhibits favorable impedance matching over a broad input power range (−40 to −10 dBm). Simulation and experimental results of input reflection coefficient and rectified output power are in excellent agreement, demonstrating the usefulness of this innovative low-power rectification technique. Measurement results indicate an effective efficiency of 54.3%, and an output DC voltage of 772.8 mV is achieved for a multi-tone input power of −10 dBm. Furthermore, the measured output DC power from harvesting RF energy from multiple services concurrently exhibits a 3.14 and 7.24 fold increase over single frequency rectification at 490 and 860 MHz respectively. Therefore, the proposed multi-service highly sensitive rectifier is a promising technique for providing a sustainable energy source for low power applications in urban environments. PMID:25951137

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335

Canonical multi-valued input Reed-Muller trees and forms

NASA Technical Reports Server (NTRS)

Perkowski, M. A.; Johnson, P. D.

1991-01-01

There is recently an increased interest in logic synthesis using EXOR gates. The paper introduces the fundamental concept of Orthogonal Expansion, which generalizes the ring form of the Shannon expansion to the logic with multiple-valued (mv) inputs. Based on this concept we are able to define a family of canonical tree circuits. Such circuits can be considered for binary and multiple-valued input cases. They can be multi-level (trees and DAG's) or flattened to two-level AND-EXOR circuits. Input decoders similar to those used in Sum of Products (SOP) PLA's are used in realizations of multiple-valued input functions. In the case of the binary logic the family of flattened AND-EXOR circuits includes several forms discussed by Davio and Green. For the case of the logic with multiple-valued inputs, the family of the flattened mv AND-EXOR circuits includes three expansions known from literature and two new expansions.

Comparing the High School English Curriculum in Turkey through Multi-Analysis

ERIC Educational Resources Information Center

Batdi, Veli

2017-01-01

This study aimed to compare the High School English Curriculum (HSEC) in accordance with Stufflebeam's context, input, process and product (CIPP) model through multi-analysis. The research includes both quantitative and qualitative aspects. A descriptive analysis was operated through Rasch Measurement Model; SPSS program for the quantitative…

Optimizing Indicator Choosing for Canal Control System and Simulation Study

USDA-ARS?s Scientific Manuscript database

One Key problem for canal system control is how to select appropriate performance indicators and how to tune the controller with these indicators. A canal system is a multi-input and multi-output (MIMO) system. The judging of control performance can be extremely complicated. In this paper, frequentl...

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Optical frequency comb based multi-band microwave frequency conversion for satellite applications.

PubMed

Yang, Xinwu; Xu, Kun; Yin, Jie; Dai, Yitang; Yin, Feifei; Li, Jianqiang; Lu, Hua; Liu, Tao; Ji, Yuefeng

2014-01-13

Based on optical frequency combs (OFC), we propose an efficient and flexible multi-band frequency conversion scheme for satellite repeater applications. The underlying principle is to mix dual coherent OFCs with one of which carrying the input signal. By optically channelizing the mixed OFCs, the converted signal in different bands can be obtained in different channels. Alternatively, the scheme can be configured to generate multi-band local oscillators (LO) for widely distribution. Moreover, the scheme realizes simultaneous inter- and intra-band frequency conversion just in a single structure and needs only three frequency-fixed microwave sources. We carry out a proof of concept experiment in which multiple LOs with 2 GHz, 10 GHz, 18 GHz, and 26 GHz are generated. A C-band signal of 6.1 GHz input to the proposed scheme is successfully converted to 4.1 GHz (C band), 3.9 GHz (C band) and 11.9 GHz (X band), etc. Compared with the back-to-back (B2B) case measured at 0 dBm input power, the proposed scheme shows a 9.3% error vector magnitude (EVM) degradation at each output channel. Furthermore, all channels satisfy the EVM limit in a very wide input power range.

Circuit for high resolution decoding of multi-anode microchannel array detectors

NASA Technical Reports Server (NTRS)

Kasle, David B. (Inventor)

1995-01-01

A circuit for high resolution decoding of multi-anode microchannel array detectors consisting of input registers accepting transient inputs from the anode array; anode encoding logic circuits connected to the input registers; midpoint pipeline registers connected to the anode encoding logic circuits; and pixel decoding logic circuits connected to the midpoint pipeline registers is described. A high resolution algorithm circuit operates in parallel with the pixel decoding logic circuit and computes a high resolution least significant bit to enhance the multianode microchannel array detector's spatial resolution by halving the pixel size and doubling the number of pixels in each axis of the anode array. A multiplexer is connected to the pixel decoding logic circuit and allows a user selectable pixel address output according to the actual multi-anode microchannel array detector anode array size. An output register concatenates the high resolution least significant bit onto the standard ten bit pixel address location to provide an eleven bit pixel address, and also stores the full eleven bit pixel address. A timing and control state machine is connected to the input registers, the anode encoding logic circuits, and the output register for managing the overall operation of the circuit.

A fully dynamic model of a multi-layer piezoelectric actuator incorporating the power amplifier

NASA Astrophysics Data System (ADS)

Zhu, Wei; Yang, Fufeng; Rui, Xiaoting

2017-12-01

The dynamic input-output characteristics of the multi-layer piezoelectric actuator (PA) are intrinsically rate-dependent and hysteresis. Meanwhile, aiming at the strong capacitive impedance of multi-layer PA, the power amplifier of the actuator can greatly affect the dynamic performances of the actuator. In this paper, a novel dynamic model that includes a model of the electric circuit providing voltage to the actuator, an inverse piezoelectric effect model describing the hysteresis and creep behavior of the actuator, and a mechanical model, in which the vibration characteristics of the multi-layer PA is described, is put forward. Validation experimental tests are conducted. Experimental results show that the proposed dynamic model can accurately predict the fully dynamic behavior of the multi-layer PA with different driving power.

18.6 K single-stage high frequency multi-bypass coaxial pulse tube cryocooler

NASA Astrophysics Data System (ADS)

Chen, Liubiao; Jin, Hai; Wang, Junjie; Zhou, Yuan; Zhu, Wenxiu; Zhou, Qiang

2013-02-01

A single-stage high frequency multi-bypass coaxial pulse tube cryocooler (PTC) has been developed for physical experiments. The performance characteristics are presented. At present, the cooler has reached the lowest temperature of 18.6 K with an electric input power of 268 W, which is the reported lowest temperature for single-stage high frequency PTC. The cooler typically provides 0.2 W at 20.6 K and 0.5 W at 24.1 K with the input power of 260 W at 300 K ambient temperature. The cooperation phase adjustment method of multi-bypass and double-inlet shows its advantages in experiments, they might be the best way to get temperature below 20 K for single-stage high frequency PTC. The temperature stability of the developed PTC is also observed.

A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Lund, Jay R.

2011-05-01

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California's Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes.

Analysis of the performance of a wireless optical multi-input to multi-output communication system.

PubMed

Bushuev, Denis; Arnon, Shlomi

2006-07-01

We investigate robust optical wireless communication in a highly scattering propagation medium using multielement optical detector arrays. The communication setup consists of synchronized multiple transmitters that send information to a receiver array and an atmospheric propagation channel. The mathematical model that best describes this scenario is multi-input to multi-output communication through stochastic slow changing channels. In this model, signals from m transmitters are received by n receiver-detectors. The channel transfer function matrix is G, and its size is n x m. G(i,j) is the transfer function from transmitter i to detector j, and m > or = n. We adopt a quasi-stationary approach in which the channel time variation has a negligible effect on communication performance over a burst. The G matrix is calculated on the basis of the optical transfer function of the atmospheric channel (composed of aerosol and turbulence elements) and the receiver's optics. In this work we derive a performance model using environmental data, such as documented turbulence and aerosol models and noise statistics. We also present the results of simulations conducted for the proposed detection algorithm.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

PubMed

Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

2016-01-01

This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers.

PubMed

Halámek, Jan; Bocharova, Vera; Chinnapareddy, Soujanya; Windmiller, Joshua Ray; Strack, Guinevere; Chuang, Min-Chieh; Zhou, Jian; Santhosh, Padmanabhan; Ramirez, Gabriela V; Arugula, Mary A; Wang, Joseph; Katz, Evgeny

2010-12-01

A multi-enzyme biocatalytic cascade processing simultaneously five biomarkers characteristic of traumatic brain injury (TBI) and soft tissue injury (STI) was developed. The system operates as a digital biosensor based on concerted function of 8 Boolean AND logic gates, resulting in the decision about the physiological conditions based on the logic analysis of complex patterns of the biomarkers. The system represents the first example of a multi-step/multi-enzyme biosensor with the built-in logic for the analysis of complex combinations of biochemical inputs. The approach is based on recent advances in enzyme-based biocomputing systems and the present paper demonstrates the potential applicability of biocomputing for developing novel digital biosensor networks.

Secure content objects

DOEpatents

Evans, William D [Cupertino, CA

2009-02-24

A secure content object protects electronic documents from unauthorized use. The secure content object includes an encrypted electronic document, a multi-key encryption table having at least one multi-key component, an encrypted header and a user interface device. The encrypted document is encrypted using a document encryption key associated with a multi-key encryption method. The encrypted header includes an encryption marker formed by a random number followed by a derivable variation of the same random number. The user interface device enables a user to input a user authorization. The user authorization is combined with each of the multi-key components in the multi-key encryption key table and used to try to decrypt the encrypted header. If the encryption marker is successfully decrypted, the electronic document may be decrypted. Multiple electronic documents or a document and annotations may be protected by the secure content object.

Flexible and multi-directional piezoelectric energy harvester for self-powered human motion sensor

NASA Astrophysics Data System (ADS)

Kim, Min-Ook; Pyo, Soonjae; Oh, Yongkeun; Kang, Yunsung; Cho, Kyung-Ho; Choi, Jungwook; Kim, Jongbaeg

2018-03-01

A flexible piezoelectric strain energy harvester that is responsive to multi-directional input forces produced by various human motions is proposed. The structure of the harvester, which includes a polydimethylsiloxane (PDMS) bump, facilitates the effective conversion of strain energy, produced by input forces applied in random directions, into electrical energy. The structural design of the PDMS bump and frame as well as the slits in the piezoelectric polyvinylidene fluoride (PVDF) film provide mechanical flexibility and enhance the strain induced in the PVDF film under input forces applied at various angles. The amount and direction of the strain induced in PVDF can be changed by the direction of the applied force; thus, the generated output power can be varied. The measured maximum output peak voltage is 1.75, 1.29, and 0.98 V when an input force of 4 N (2 Hz) is applied at angles of 0°, 45°, and 90°, and the corresponding maximum output power is 0.064, 0.026, and 0.02 μW, respectively. Moreover, the harvester stably generates output voltage over 1.4 × 104 cycles. Thus, the proposed harvester successfully identifies and converts strain energy produced by multi-directional input forces by various human motions into electrical energy. We demonstrate the potential utility of the proposed flexible energy harvester as a self-powered human motion sensor for wireless healthcare systems.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Design and Implementation of Collaborative Research Approaches

NASA Technical Reports Server (NTRS)

Venti, Mike W.; Berger, David E.

2009-01-01

This poster reviews the collarborative research approaches that NASA has been designing and implementing for the Integrated Vehicle Health Management (IVHM) Project. The inputs for the technical plan are reviewed, the Research Test and Integration Plan (RTIP) WIKI, is used to create and propose a multi-themed and multi-partner research testing opportunities. The outputs are testing opportunities.

Developing a Multi-Dimensional Evaluation Framework for Faculty Teaching and Service Performance

ERIC Educational Resources Information Center

Baker, Diane F.; Neely, Walter P.; Prenshaw, Penelope J.; Taylor, Patrick A.

2015-01-01

A task force was created in a small, AACSB-accredited business school to develop a more comprehensive set of standards for faculty performance. The task force relied heavily on faculty input to identify and describe key dimensions that capture effective teaching and service performance. The result is a multi-dimensional framework that will be used…

Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2017-01-01

This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

A multi-hypothesis tracker for clicking whales.

PubMed

Baggenstoss, Paul M

2015-05-01

This paper describes a tracker specially designed to track clicking beaked whales using widely spaced bottom-mounted hydrophones, although it can be adapted to different species and sensors. The input to the tracker is a sequence of static localization solutions obtained using time difference of arrival information at widely spaced hydrophones. To effectively handle input localizations with high ambiguity, the tracker is based on multi-hypothesis tracker concepts, so it considers all potential association hypotheses and keeps a large number of potential tracks in memory. The method is demonstrated on actual data and shown to successfully track multiple beaked whales at depth.

Statin Selection in Qatar Based on Multi-indication Pharmacotherapeutic Multi-criteria Scoring Model, and Clinician Preference.

PubMed

Al-Badriyeh, Daoud; Fahey, Michael; Alabbadi, Ibrahim; Al-Khal, Abdullatif; Zaidan, Manal

2015-12-01

Statin selection for the largest hospital formulary in Qatar is not systematic, not comparative, and does not consider the multi-indication nature of statins. There are no reports in the literature of multi-indication-based comparative scoring models of statins or of statin selection criteria weights that are based primarily on local clinicians' preferences and experiences. This study sought to comparatively evaluate statins for first-line therapy in Qatar, and to quantify the economic impact of this. An evidence-based, multi-indication, multi-criteria pharmacotherapeutic model was developed for the scoring of statins from the perspective of the main health care provider in Qatar. The literature and an expert panel informed the selection criteria of statins. Relative weighting of selection criteria was based on the input of the relevant local clinician population. Statins were comparatively scored based on literature evidence, with those exceeding a defined scoring threshold being recommended for use. With 95% CI and 5% margin of error, the scoring model was successfully developed. Selection criteria comprised 28 subcriteria under the following main criteria: clinical efficacy, best publish evidence and experience, adverse effects, drug interaction, dosing time, and fixed dose combination availability. Outcome measures for multiple indications were related to effects on LDL cholesterol, HDL cholesterol, triglyceride, total cholesterol, and C-reactive protein. Atorvastatin, pravastatin, and rosuvastatin exceeded defined pharmacotherapeutic thresholds. Atorvastatin and pravastatin were recommended as first-line use and rosuvastatin as a nonformulary alternative. It was estimated that this would produce a 17.6% cost savings in statins expenditure. Sensitivity analyses confirmed the robustness of the evaluation's outcomes against input uncertainties. Incorporating a comparative evaluation of statins in Qatari practices based on a locally developed, transparent, multi-indication, multi-criteria scoring model has the potential to considerably reduce expenditures on statins. Atorvastatin and pravastatin should be the first-line statin therapies in the main Qatari health care provider, with rosuvastatin as an alternative. Copyright © 2015 Elsevier HS Journals, Inc. All rights reserved.

A User''s Guide to the Zwikker-Kosten Transmission Line Code (ZKTL)

NASA Technical Reports Server (NTRS)

Kelly, J. J.; Abu-Khajeel, H.

1997-01-01

This user's guide documents updates to the Zwikker-Kosten Transmission Line Code (ZKTL). This code was developed for analyzing new liner concepts developed to provide increased sound absorption. Contiguous arrays of multi-degree-of-freedom (MDOF) liner elements serve as the model for these liner configurations, and Zwikker and Kosten's theory of sound propagation in channels is used to predict the surface impedance. Transmission matrices for the various liner elements incorporate both analytical and semi-empirical methods. This allows standard matrix techniques to be employed in the code to systematically calculate the composite impedance due to the individual liner elements. The ZKTL code consists of four independent subroutines: 1. Single channel impedance calculation - linear version (SCIC) 2. Single channel impedance calculation - nonlinear version (SCICNL) 3. Multi-channel, multi-segment, multi-layer impedance calculation - linear version (MCMSML) 4. Multi-channel, multi-segment, multi-layer impedance calculation - nonlinear version (MCMSMLNL) Detailed examples, comments, and explanations for each liner impedance computation module are included. Also contained in the guide are depictions of the interactive execution, input files and output files.

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

PubMed

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro

2014-01-01

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials

PubMed Central

Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

2016-01-01

Purpose This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. Methods The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. Results The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Conclusion Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials. PMID:27467882

A Comprehensive Study of Gridding Methods for GPS Horizontal Velocity Fields

NASA Astrophysics Data System (ADS)

Wu, Yanqiang; Jiang, Zaisen; Liu, Xiaoxia; Wei, Wenxin; Zhu, Shuang; Zhang, Long; Zou, Zhenyu; Xiong, Xiaohui; Wang, Qixin; Du, Jiliang

2017-03-01

Four gridding methods for GPS velocities are compared in terms of their precision, applicability and robustness by analyzing simulated data with uncertainties from 0.0 to ±3.0 mm/a. When the input data are 1° × 1° grid sampled and the uncertainty of the additional error is greater than ±1.0 mm/a, the gridding results show that the least-squares collocation method is highly robust while the robustness of the Kriging method is low. In contrast, the spherical harmonics and the multi-surface function are moderately robust, and the regional singular values for the multi-surface function method and the edge effects for the spherical harmonics method become more significant with increasing uncertainty of the input data. When the input data (with additional errors of ±2.0 mm/a) are decimated by 50% from the 1° × 1° grid data and then erased in three 6° × 12° regions, the gridding results in these three regions indicate that the least-squares collocation and the spherical harmonics methods have good performances, while the multi-surface function and the Kriging methods may lead to singular values. The gridding techniques are also applied to GPS horizontal velocities with an average error of ±0.8 mm/a over the Chinese mainland and the surrounding areas, and the results show that the least-squares collocation method has the best performance, followed by the Kriging and multi-surface function methods. Furthermore, the edge effects of the spherical harmonics method are significantly affected by the sparseness and geometric distribution of the input data. In general, the least-squares collocation method is superior in terms of its robustness, edge effect, error distribution and stability, while the other methods have several positive features.

Multishaker modal testing

NASA Technical Reports Server (NTRS)

Craig, Roy R., Jr.

1987-01-01

The major accomplishments of this research are: (1) the refinement and documentation of a multi-input, multi-output modal parameter estimation algorithm which is applicable to general linear, time-invariant dynamic systems; (2) the development and testing of an unsymmetric block-Lanzcos algorithm for reduced-order modeling of linear systems with arbitrary damping; and (3) the development of a control-structure-interaction (CSI) test facility.

Measuring Educational Efficiency at Student Level with Parametric Stochastic Distance Functions: An Application to Spanish PISA Results

ERIC Educational Resources Information Center

Perelman, Sergio; Santin, Daniel

2011-01-01

The aim of the present paper is to examine the observed differences in Students' test performance across public and private-voucher schools in Spain. For this purpose, we explicitly consider that education is a multi-input multi-output production process subject to inefficient behaviors, which can be identified at student level using a parametric…

Real-Time Analysis of a Sensor's Data for Automated Decision Making in an IoT-Based Smart Home.

PubMed

Khan, Nida Saddaf; Ghani, Sayeed; Haider, Sajjad

2018-05-25

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

A Novel Approach to Develop the Lower Order Model of Multi-Input Multi-Output System

NASA Astrophysics Data System (ADS)

Rajalakshmy, P.; Dharmalingam, S.; Jayakumar, J.

2017-10-01

A mathematical model is a virtual entity that uses mathematical language to describe the behavior of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines like physics, biology, and electrical engineering as well as in the social sciences like economics, sociology and political science. Physicists, Engineers, Computer scientists, and Economists use mathematical models most extensively. With the advent of high performance processors and advanced mathematical computations, it is possible to develop high performing simulators for complicated Multi Input Multi Ouptut (MIMO) systems like Quadruple tank systems, Aircrafts, Boilers etc. This paper presents the development of the mathematical model of a 500 MW utility boiler which is a highly complex system. A synergistic combination of operational experience, system identification and lower order modeling philosophy has been effectively used to develop a simplified but accurate model of a circulation system of a utility boiler which is a MIMO system. The results obtained are found to be in good agreement with the physics of the process and with the results obtained through design procedure. The model obtained can be directly used for control system studies and to realize hardware simulators for boiler testing and operator training.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

NASA Astrophysics Data System (ADS)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Arrangement for multiplexing and intensity splitting light beams for interface into fiber optic cables

DOEpatents

Johnson, Steve A.

1990-01-01

An arrangement especially suitable for use in a laser apparatus for converting a plurality of different input light beams, for example copper vapor laser beams, into a plurality of substantially identical light beams is disclosed herein. This arrangement utilizes an optical mixing bar which is preferably integrally formed as a single unit and which includes a main body for mixing light therein, a flat input surface on one end of the main body, and a multi-faceted output face on the opposite end of the main body. This arrangement also includes means for directing the plurality of different input light beams onto the input face of the mixing base, whereby to cause the different beams to mix within the main body of the mixing bar and exit the latter from its multi-faceted output face as the desired plurality of substantially identical output beams.

Identification of an internal combustion engine model by nonlinear multi-input multi-output system identification. Ph.D. Thesis

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

Luh, G.C.

1994-01-01

This thesis presents the application of advanced modeling techniques to construct nonlinear forward and inverse models of internal combustion engines for the detection and isolation of incipient faults. The NARMAX (Nonlinear Auto-Regressive Moving Average modeling with eXogenous inputs) technique of system identification proposed by Leontaritis and Billings was used to derive the nonlinear model of a internal combustion engine, over operating conditions corresponding to the I/M240 cycle. The I/M240 cycle is a standard proposed by the United States Environmental Protection Agency to measure tailpipe emissions in inspection and maintenance programs and consists of a driving schedule developed for the purposemore » of testing compliance with federal vehicle emission standards for carbon monoxide, unburned hydrocarbons, and nitrogen oxides. The experimental work for model identification and validation was performed on a 3.0 liter V6 engine installed in an engine test cell at the Center for Automotive Research at The Ohio State University. In this thesis, different types of model structures were proposed to obtain multi-input multi-output (MIMO) nonlinear NARX models. A modification of the algorithm proposed by He and Asada was used to estimate the robust orders of the derived MIMO nonlinear models. A methodology for the analysis of inverse NARX model was developed. Two methods were proposed to derive the inverse NARX model: (1) inversion from the forward NARX model; and (2) direct identification of inverse model from the output-input data set. In this thesis, invertibility, minimum-phase characteristic of zero dynamics, and stability analysis of NARX forward model are also discussed. Stability in the sense of Lyapunov is also investigated to check the stability of the identified forward and inverse models. This application of inverse problem leads to the estimation of unknown inputs and to actuator fault diagnosis.« less

Genetics-based control of a mimo boiler-turbine plant

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

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Use of multi-node wells in the Groundwater-Management Process of MODFLOW-2005 (GWM-2005)

USGS Publications Warehouse

Ahlfeld, David P.; Barlow, Paul M.

2013-01-01

Many groundwater wells are open to multiple aquifers or to multiple intervals within a single aquifer. These types of wells can be represented in numerical simulations of groundwater flow by use of the Multi-Node Well (MNW) Packages developed for the U.S. Geological Survey’s MODFLOW model. However, previous versions of the Groundwater-Management (GWM) Process for MODFLOW did not allow the use of multi-node wells in groundwater-management formulations. This report describes modifications to the MODFLOW–2005 version of the GWM Process (GWM–2005) to provide for such use with the MNW2 Package. Multi-node wells can be incorporated into a management formulation as flow-rate decision variables for which optimal withdrawal or injection rates will be determined as part of the GWM–2005 solution process. In addition, the heads within multi-node wells can be used as head-type state variables, and, in that capacity, be included in the objective function or constraint set of a management formulation. Simple head bounds also can be defined to constrain water levels at multi-node wells. The report provides instructions for including multi-node wells in the GWM–2005 data-input files and a sample problem that demonstrates use of multi-node wells in a typical groundwater-management problem.

Forest height Mapping using the fusion of Lidar and MULTI-ANGLE spectral data

NASA Astrophysics Data System (ADS)

Pang, Y.; Li, Z.

2016-12-01

Characterizing the complexity of forest ecosystem over large area is highly complex. Light detection and Ranging (LIDAR) approaches have demonstrated a high capacity to accurately estimate forest structural parameters. A number of satellite mission concepts have been proposed to fuse LiDAR with other optical imagery allowing Multi-angle spectral observations to be captured using the Bidirectional Reflectance Distribution Function (BRDF) characteristics of forests. China is developing the concept of Chinese Terrestrial Carbon Mapping Satellite. A multi-beam waveform Lidar is the main sensor. A multi-angle imagery system is considered as the spatial mapping sensor. In this study, we explore the fusion potential of Lidar and multi-angle spectral data to estimate forest height across different scales. We flew intensive airborne Lidar and Multi-angle hyperspectral data in Genhe Forest Ecological Research Station, Northeast China. Then extended the spatial scale with some long transect flights to cover more forest structures. Forest height data derived from airborne lidar data was used as reference data and the multi-angle hyperspectral data was used as model inputs. Our results demonstrate that the multi-angle spectral data can be used to estimate forest height with the RMSE of 1.1 m with an R2 approximately 0.8.

FANS-3D Users Guide (ESTEP Project ER 201031)

DTIC Science & Technology

2016-08-01

governing laminar and turbulent flows in body-fitted curvilinear grids. The code employs multi-block overset ( chimera ) grids, including fully matched...governing incompressible flow in body-fitted grids. The code allows for multi-block overset ( chimera ) grids, which can be fully matched, arbitrarily...interested reader may consult the Chimera Overset Structured Mesh-Interpolation Code (COSMIC) Users’ Manual (Chen, 2009). The input file used for

Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale.

PubMed

Wang, Qi; Xie, Zhiyi; Li, Fangbai

2015-11-01

This study aims to identify and apportion multi-source and multi-phase heavy metal pollution from natural and anthropogenic inputs using ensemble models that include stochastic gradient boosting (SGB) and random forest (RF) in agricultural soils on the local scale. The heavy metal pollution sources were quantitatively assessed, and the results illustrated the suitability of the ensemble models for the assessment of multi-source and multi-phase heavy metal pollution in agricultural soils on the local scale. The results of SGB and RF consistently demonstrated that anthropogenic sources contributed the most to the concentrations of Pb and Cd in agricultural soils in the study region and that SGB performed better than RF. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multi-dimensional simulations of core-collapse supernova explosions with CHIMERA

NASA Astrophysics Data System (ADS)

Messer, O. E. B.; Harris, J. A.; Hix, W. R.; Lentz, E. J.; Bruenn, S. W.; Mezzacappa, A.

2018-04-01

Unraveling the core-collapse supernova (CCSN) mechanism is a problem that remains essentially unsolved despite more than four decades of effort. Spherically symmetric models with otherwise high physical fidelity generally fail to produce explosions, and it is widely accepted that CCSNe are inherently multi-dimensional. Progress in realistic modeling has occurred recently through the availability of petascale platforms and the increasing sophistication of supernova codes. We will discuss our most recent work on understanding neutrino-driven CCSN explosions employing multi-dimensional neutrino-radiation hydrodynamics simulations with the Chimera code. We discuss the inputs and resulting outputs from these simulations, the role of neutrino radiation transport, and the importance of multi-dimensional fluid flows in shaping the explosions. We also highlight the production of 48Ca in long-running Chimera simulations.

A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

NASA Astrophysics Data System (ADS)

Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

2014-11-01

In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

Implications of Preference and Problem Formulation on the Operating Policies of Complex Multi-Reservoir Systems

NASA Astrophysics Data System (ADS)

Quinn, J.; Reed, P. M.; Giuliani, M.; Castelletti, A.

2016-12-01

Optimizing the operations of multi-reservoir systems poses several challenges: 1) the high dimension of the problem's states and controls, 2) the need to balance conflicting multi-sector objectives, and 3) understanding how uncertainties impact system performance. These difficulties motivated the development of the Evolutionary Multi-Objective Direct Policy Search (EMODPS) framework, in which multi-reservoir operating policies are parameterized in a given family of functions and then optimized for multiple objectives through simulation over a set of stochastic inputs. However, properly framing these objectives remains a severe challenge and a neglected source of uncertainty. Here, we use EMODPS to optimize operating policies for a 4-reservoir system in the Red River Basin in Vietnam, exploring the consequences of optimizing to different sets of objectives related to 1) hydropower production, 2) meeting multi-sector water demands, and 3) providing flood protection to the capital city of Hanoi. We show how coordinated operation of the reservoirs can differ markedly depending on how decision makers weigh these concerns. Moreover, we illustrate how formulation choices that emphasize the mean, tail, or variability of performance across objective combinations must be evaluated carefully. Our results show that these choices can significantly improve attainable system performance, or yield severe unintended consequences. Finally, we show that satisfactory validation of the operating policies on a set of out-of-sample stochastic inputs depends as much or more on the formulation of the objectives as on effective optimization of the policies. These observations highlight the importance of carefully considering how we abstract stakeholders' objectives and of iteratively optimizing and visualizing multiple problem formulation hypotheses to ensure that we capture the most important tradeoffs that emerge from different stakeholder preferences.

In situ UV curable 3D printing of multi-material tri-legged soft bot with spider mimicked multi-step forward dynamic gait

NASA Astrophysics Data System (ADS)

Zeb Gul, Jahan; Yang, Bong-Su; Yang, Young Jin; Chang, Dong Eui; Choi, Kyung Hyun

2016-11-01

Soft bots have the expedient ability of adopting intricate postures and fitting in complex shapes compared to mechanical robots. This paper presents a unique in situ UV curing three-dimensional (3D) printed multi-material tri-legged soft bot with spider mimicked multi-step dynamic forward gait using commercial bio metal filament (BMF) as an actuator. The printed soft bot can produce controllable forward motion in response to external signals. The fundamental properties of BMF, including output force, contractions at different frequencies, initial loading rate, and displacement-rate are verified. The tri-pedal soft bot CAD model is designed inspired by spider’s legged structure and its locomotion is assessed by simulating strain and displacement using finite element analysis. A customized rotational multi-head 3D printing system assisted with multiple wavelength’s curing lasers is used for in situ fabrication of tri-pedal soft-bot using two flexible materials (epoxy and polyurethane) in three layered steps. The size of tri-pedal soft-bot is 80 mm in diameter and each pedal’s width and depth is 5 mm × 5 mm respectively. The maximum forward speed achieved is 2.7 mm s-1 @ 5 Hz with input voltage of 3 V and 250 mA on a smooth surface. The fabricated tri-pedal soft bot proved its power efficiency and controllable locomotion at three input signal frequencies (1, 2, 5 Hz).

Numerical simulation of multi-rifled tube drawing - finding proper feedstock dimensions and tool geometry

NASA Astrophysics Data System (ADS)

Bella, P.; Buček, P.; Ridzoň, M.; Mojžiš, M.; Parilák, L.'

2017-02-01

Production of multi-rifled seamless steel tubes is quite a new technology in Železiarne Podbrezová. Therefore, a lot of technological questions emerges (process technology, input feedstock dimensions, material flow during drawing, etc.) Pilot experiments to fine tune the process cost a lot of time and energy. For this, numerical simulation would be an alternative solution for achieving optimal parameters in production technology. This would reduce the number of experiments needed, lowering the overall costs of development. However, to claim the numerical results to be relevant it is necessary to verify them against the actual plant trials. Searching for optimal input feedstock dimension for drawing of multi-rifled tube with dimensions Ø28.6 mm × 6.3 mm is what makes the main topic of this paper. As a secondary task, effective position of the plug - die couple has been solved via numerical simulation. Comparing the calculated results with actual numbers from plant trials a good agreement was observed.

On codes with multi-level error-correction capabilities

NASA Technical Reports Server (NTRS)

Lin, Shu

1987-01-01

In conventional coding for error control, all the information symbols of a message are regarded equally significant, and hence codes are devised to provide equal protection for each information symbol against channel errors. However, in some occasions, some information symbols in a message are more significant than the other symbols. As a result, it is desired to devise codes with multilevel error-correcting capabilities. Another situation where codes with multi-level error-correcting capabilities are desired is in broadcast communication systems. An m-user broadcast channel has one input and m outputs. The single input and each output form a component channel. The component channels may have different noise levels, and hence the messages transmitted over the component channels require different levels of protection against errors. Block codes with multi-level error-correcting capabilities are also known as unequal error protection (UEP) codes. Structural properties of these codes are derived. Based on these structural properties, two classes of UEP codes are constructed.

A Novel Degradation Identification Method for Wind Turbine Pitch System

NASA Astrophysics Data System (ADS)

Guo, Hui-Dong

2018-04-01

It’s difficult for traditional threshold value method to identify degradation of operating equipment accurately. An novel degradation evaluation method suitable for wind turbine condition maintenance strategy implementation was proposed in this paper. Based on the analysis of typical variable-speed pitch-to-feather control principle and monitoring parameters for pitch system, a multi input multi output (MIMO) regression model was applied to pitch system, where wind speed, power generation regarding as input parameters, wheel rotation speed, pitch angle and motor driving currency for three blades as output parameters. Then, the difference between the on-line measurement and the calculated value from the MIMO regression model applying least square support vector machines (LSSVM) method was defined as the Observed Vector of the system. The Gaussian mixture model (GMM) was applied to fitting the distribution of the multi dimension Observed Vectors. Applying the model established, the Degradation Index was calculated using the SCADA data of a wind turbine damaged its pitch bearing retainer and rolling body, which illustrated the feasibility of the provided method.

Bridgeless SEPIC PFC Converter for Multistring LED Driver

NASA Astrophysics Data System (ADS)

Jha, Aman; Singh, Bhim

2018-05-01

This paper deals with Power Factor Correction (PFC) in Low Voltage High Current (LVHC) multi-string light emitting diode (LED) using a bridgeless (BL) single ended primary inductance converter (SEPIC). This application is designed for large area LED lighting with illumination control. A multi-mode LED dimming technique is used for the lighting control. The BL-SEPIC PFC converter is used as a load emulator for high power factor. The regulated low voltage from flyback converter is a source power to the synchronous buck converters for multi-string LED driver and forced cooling system for LED junction. The BL-SEPIC PFC converter inductor design is based on Discontinuous Inductor Current Modes (DICM) which provides good PFC at low cost. Test results are found quite satisfactory for universal input AC (90-265 V). There is significant improvement in the power factor and input current Total Harmonic Distortion (THD) with good margin of harmonic limits for lighting IEC 61000-3-2 Class C.

Efficient design of gain-flattened multi-pump Raman fiber amplifiers using least squares support vector regression

NASA Astrophysics Data System (ADS)

Chen, Jing; Qiu, Xiaojie; Yin, Cunyi; Jiang, Hao

2018-02-01

An efficient method to design the broadband gain-flattened Raman fiber amplifier with multiple pumps is proposed based on least squares support vector regression (LS-SVR). A multi-input multi-output LS-SVR model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. The proposed approach contains two stages: offline training stage and online optimization stage. During the offline stage, the LS-SVR model is trained. Owing to the good generalization capability of LS-SVR, the net gain spectrum can be directly and accurately obtained when inputting any combination of the pump wavelength and power to the well-trained model. During the online stage, we incorporate the LS-SVR model into the particle swarm optimization algorithm to find the optimal pump configuration. The design results demonstrate that the proposed method greatly shortens the computation time and enhances the efficiency of the pump parameter optimization for Raman fiber amplifier design.

Multi-muscle FES force control of the human arm for arbitrary goals.

PubMed

Schearer, Eric M; Liao, Yu-Wei; Perreault, Eric J; Tresch, Matthew C; Memberg, William D; Kirsch, Robert F; Lynch, Kevin M

2014-05-01

We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subject's hand was identified. We inverted the model of this redundant and coupled multiple-input multiple-output system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total root mean square error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trial-to-trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals.

Building accurate historic and future climate MEPDG input files for Louisiana DOTD.

DOT National Transportation Integrated Search

2017-02-01

The pavement design process (originally MEPDG, then DARWin-ME, and now Pavement ME Design) requires a multi-year set of hourly : climate input data that influence pavement material properties. In Louisiana, the software provides nine locations with c...

An integrated analog O/E/O link for multi-channel laser neurons

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

Nahmias, Mitchell A., E-mail: mnahmias@princeton.edu; Tait, Alexander N.; Tolias, Leonidas

2016-04-11

We demonstrate an analog O/E/O electronic link to allow integrated laser neurons to accept many distinguishable, high bandwidth input signals simultaneously. This device utilizes wavelength division multiplexing to achieve multi-channel fan-in, a photodetector to sum signals together, and a laser cavity to perform a nonlinear operation. Its speed outpaces accelerated-time neuromorphic electronics, and it represents a viable direction towards scalable networking approaches.

The automated multi-stage substructuring system for NASTRAN

NASA Technical Reports Server (NTRS)

Field, E. I.; Herting, D. N.; Herendeen, D. L.; Hoesly, R. L.

1975-01-01

The substructuring capability developed for eventual installation in Level 16 is now operational in a test version of NASTRAN. Its features are summarized. These include the user-oriented, Case Control type control language, the automated multi-stage matrix processing, the independent direct access data storage facilities, and the static and normal modes solution capabilities. A complete problem analysis sequence is presented with card-by-card description of the user input.

Research in Applied Mathematics Related to Mathematical System Theory.

DTIC Science & Technology

1977-06-01

This report deals with research results obtained in the field of mathematical system theory . Special emphasis was given to the following areas: (1...Linear system theory over a field: parametrization of multi-input, multi-output systems and the geometric structure of classes of systems of...constant dimension. (2) Linear systems over a ring: development of the theory for very general classes of rings. (3) Nonlinear system theory : basic

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2012-09-30

Estimation Methods for Underwater OFDM 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. 6) Asynchronous Multiuser...multi-input multi-output ( MIMO ) OFDM is also pursued, where it is shown that the proposed hybrid initialization enables drastically improved receiver...are investigated. 5) Two Iterative Receivers for Distributed MIMO - OFDM with Large Doppler Deviations. This work studies a distributed system with

Vibration suppression of a piezo-equipped cylindrical shell in a broad-band frequency domain

NASA Astrophysics Data System (ADS)

Loghmani, Ali; Danesh, Mohammad; Kwak, Moon K.; Keshmiri, Mehdi

2017-12-01

This paper focuses on the dynamic modeling of a cylindrical shell equipped with piezoceramic sensors and actuators, as well as the design of a broad band multi-input and multi-output linear quadratic Gaussian controller for the suppression of vibrations. The optimal locations of actuators are derived by Genetic Algorithm (GA) to effectively control the specific structural modes of the cylinder. The dynamic model is derived based on the Sanders shell theory and the energy approach for both the cylinder and the piezoelectric transducers, all of which reflect the piezoelectric effect. The natural vibration characteristics of the cylindrical shell are investigated both theoretically and experimentally. The theoretical predictions are in good agreement with the experimental results. Then, the broad band multi-input and multi-output linear quadratic Gaussian controller was designed and applied to the test article. An active vibration control experiment is carried out on the cylindrical shell and the digital control system is used to implement the proposed control algorithm. The experimental results show that vibrations of the cylindrical shell can be suppressed by the piezoceramic sensors and actuators along with the proposed controller. The optimal location of the actuators makes the proposed control system more efficient than other configurations.

A comparison of two multi-variable integrator windup protection schemes

NASA Technical Reports Server (NTRS)

Mattern, Duane

1993-01-01

Two methods are examined for limit and integrator wind-up protection for multi-input, multi-output linear controllers subject to actuator constraints. The methods begin with an existing linear controller that satisfies the specifications for the nominal, small perturbation, linear model of the plant. The controllers are formulated to include an additional contribution to the state derivative calculations. The first method to be examined is the multi-variable version of the single-input, single-output, high gain, Conventional Anti-Windup (CAW) scheme. Except for the actuator limits, the CAW scheme is linear. The second scheme to be examined, denoted the Modified Anti-Windup (MAW) scheme, uses a scalar to modify the magnitude of the controller output vector while maintaining the vector direction. The calculation of the scalar modifier is a nonlinear function of the controller outputs and the actuator limits. In both cases the constrained actuator is tracked. These two integrator windup protection methods are demonstrated on a turbofan engine control system with five measurements, four control variables, and four actuators. The closed-loop responses of the two schemes are compared and contrasted during limit operation. The issue of maintaining the direction of the controller output vector using the Modified Anti-Windup scheme is discussed and the advantages and disadvantages of both of the IWP methods are presented.

Generalized model of electromigration with 1:1 (analyte:selector) complexation stoichiometry: part I. Theory.

PubMed

Dubský, Pavel; Müllerová, Ludmila; Dvořák, Martin; Gaš, Bohuslav

2015-03-06

The model of electromigration of a multivalent weak acidic/basic/amphoteric analyte that undergoes complexation with a mixture of selectors is introduced. The model provides an extension of the series of models starting with the single-selector model without dissociation by Wren and Rowe in 1992, continuing with the monovalent weak analyte/single-selector model by Rawjee, Williams and Vigh in 1993 and that by Lelièvre in 1994, and ending with the multi-selector overall model without dissociation developed by our group in 2008. The new multivalent analyte multi-selector model shows that the effective mobility of the analyte obeys the original Wren and Row's formula. The overall complexation constant, mobility of the free analyte and mobility of complex can be measured and used in a standard way. The mathematical expressions for the overall parameters are provided. We further demonstrate mathematically that the pH dependent parameters for weak analytes can be simply used as an input into the multi-selector overall model and, in reverse, the multi-selector overall parameters can serve as an input into the pH-dependent models for the weak analytes. These findings can greatly simplify the rationale method development in analytical electrophoresis, specifically enantioseparations. Copyright © 2015 Elsevier B.V. All rights reserved.

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2016-10-14

High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

Mechanical System Analysis/Design Tool (MSAT) Quick Guide

NASA Technical Reports Server (NTRS)

Lee, HauHua; Kolb, Mark; Madelone, Jack

1998-01-01

MSAT is a unique multi-component multi-disciplinary tool that organizes design analysis tasks around object-oriented representations of configuration components, analysis programs and modules, and data transfer links between them. This creative modular architecture enables rapid generation of input stream for trade-off studies of various engine configurations. The data transfer links automatically transport output from one application as relevant input to the next application once the sequence is set up by the user. The computations are managed via constraint propagation - the constraints supplied by the user as part of any optimization module. The software can be used in the preliminary design stage as well as during the detail design of product development process.

Validation of a new modal performance measure for flexible controllers design

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

Simo, J.B.; Tahan, S.A.; Kamwa, I.

1996-05-01

A new modal performance measure for power system stabilizer (PSS) optimization is proposed in this paper. The new method is based on modifying the square envelopes of oscillating modes, in order to take into account their damping ratios while minimizing the performance index. This criteria is applied to flexible controllers optimal design, on a multi-input-multi-output (MIMO) reduced-order model of a prototype power system. The multivariable model includes four generators, each having one input and one output. Linear time-response simulation and transient stability analysis with a nonlinear package confirm the superiority of the proposed criteria and illustrate its effectiveness in decentralizedmore » control.« less

Multi-channel detector readout method and integrated circuit

DOEpatents

Moses, William W.; Beuville, Eric; Pedrali-Noy, Marzio

2006-12-12

An integrated circuit which provides multi-channel detector readout from a detector array. The circuit receives multiple signals from the elements of a detector array and compares the sampled amplitudes of these signals against a noise-floor threshold and against one another. A digital signal is generated which corresponds to the location of the highest of these signal amplitudes which exceeds the noise floor threshold. The digital signal is received by a multiplexing circuit which outputs an analog signal corresponding the highest of the input signal amplitudes. In addition a digital control section provides for programmatic control of the multiplexer circuit, amplifier gain, amplifier reset, masking selection, and test circuit functionality on each input thereof.

Multi-channel detector readout method and integrated circuit

DOEpatents

Moses, William W.; Beuville, Eric; Pedrali-Noy, Marzio

2004-05-18

An integrated circuit which provides multi-channel detector readout from a detector array. The circuit receives multiple signals from the elements of a detector array and compares the sampled amplitudes of these signals against a noise-floor threshold and against one another. A digital signal is generated which corresponds to the location of the highest of these signal amplitudes which exceeds the noise floor threshold. The digital signal is received by a multiplexing circuit which outputs an analog signal corresponding the highest of the input signal amplitudes. In addition a digital control section provides for programmatic control of the multiplexer circuit, amplifier gain, amplifier reset, masking selection, and test circuit functionality on each input thereof.

PS2-06: Best Practices for Advancing Multi-site Chart Abstraction Research

PubMed Central

Blick, Noelle; Cole, Deanna; King, Colleen; Riordan, Rick; Von Worley, Ann; Yarbro, Patty

2012-01-01

Background/Aims Multi-site chart abstraction studies are becoming increasingly common within the HMORN. Differences in systems among HMORN sites can pose significant obstacles to the success of these studies. It is therefore crucial to standardize abstraction activities by following best practices for multi-site chart abstraction, as consistency of processes across sites will increase efficiencies and enhance data quality. Methods Over the past few months the authors have been meeting to identify obstacles to multi-site chart abstraction and to address ways in which multi-site chart abstraction processes can be systemized and standardized. The aim of this workgroup is to create a best practice guide for multi-site chart abstraction studies. Focus areas include: abstractor training, format for chart abstraction (database, paper, etc), data quality, redaction, mechanism for transferring data, site specific access to medical records, IRB/HIPAA concerns, and budgetary issues. Results The results of the workgroup’s efforts (the best practice guide) will be presented by a panel of experts at the 2012 HMORN conference. The presentation format will also focus on discussion among attendees to elicit further input and to identify areas that need to be further addressed. Subsequently, the best practice guide will be posted on the HMORN website. Discussion The best practice guide for multi-site chart abstraction studies will establish sound guidelines and serve as an aid to researchers embarking on multi-site chart abstraction studies. Efficiencies and data quality will be further enhanced with standardized multi-site chart abstraction practices.

Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection

NASA Astrophysics Data System (ADS)

Hashimoto, Ryoji; Matsumura, Tomoya; Nozato, Yoshihiro; Watanabe, Kenji; Onoye, Takao

A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640×512 pixel input images can be processed in real-time with three agents at a rate of 9fps in 48MHz operation.

Multi-dimensional simulations of core-collapse supernova explosions with CHIMERA

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

Messer, Bronson; Harris, James Austin; Hix, William Raphael

Unraveling the core-collapse supernova (CCSN) mechanism is a problem that remains essentially unsolved despite more than four decades of effort. Spherically symmetric models with otherwise high physical fidelity generally fail to produce explosions, and it is widely accepted that CCSNe are inherently multi-dimensional. Progress in realistic modeling has occurred recently through the availability of petascale platforms and the increasing sophistication of supernova codes. We will discuss our most recent work on understanding neutrino-driven CCSN explosions employing multi-dimensional neutrino-radiation hydrodynamics simulations with the Chimera code. We discuss the inputs and resulting outputs from these simulations, the role of neutrino radiation transport,more » and the importance of multi-dimensional fluid flows in shaping the explosions. We also highlight the production of 48Ca in long-running Chimera simulations.« less

Performance analysis of MISO multi-hop FSO links over log-normal channels with fog and beam divergence attenuations

NASA Astrophysics Data System (ADS)

Abaza, Mohamed; Mesleh, Raed; Mansour, Ali; Aggoune, el-Hadi

2015-01-01

The performance analysis of a multi-hop decode and forward relaying free-space optical (FSO) communication system is presented in this paper. The considered FSO system uses intensity modulation and direct detection as means of transmission and reception. Atmospheric turbulence impacts are modeled as a log-normal channel, and different weather attenuation effects and geometric losses are taken into account. It is shown that multi-hop is an efficient technique to mitigate such effects in FSO communication systems. A comparison with direct link and multiple-input single-output (MISO) systems considering correlation effects at the transmitter is provided. Results show that MISO multi-hop FSO systems are superior than their counterparts over links exhibiting high attenuation. Monte Carlo simulation results are provided to validate the bit error rate (BER) analyses and conclusions.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development and testing of methodology for evaluating the performance of multi-input/multi-output digital control systems

NASA Technical Reports Server (NTRS)

Polotzky, Anthony S.; Wieseman, Carol; Hoadley, Sherwood Tiffany; Mukhopadhyay, Vivek

1990-01-01

The development of a controller performance evaluation (CPE) methodology for multiinput/multioutput digital control systems is described. The equations used to obtain the open-loop plant, controller transfer matrices, and return-difference matrices are given. Results of applying the CPE methodology to evaluate MIMO digital flutter suppression systems being tested on an active flexible wing wind-tunnel model are presented to demonstrate the CPE capability.

A single-layer platform for Boolean logic and arithmetic through DNA excision in mammalian cells

PubMed Central

Weinberg, Benjamin H.; Hang Pham, N. T.; Caraballo, Leidy D.; Lozanoski, Thomas; Engel, Adrien; Bhatia, Swapnil; Wong, Wilson W.

2017-01-01

Genetic circuits engineered for mammalian cells often require extensive fine-tuning to perform their intended functions. To overcome this problem, we present a generalizable biocomputing platform that can engineer genetic circuits which function in human cells with minimal optimization. We used our Boolean Logic and Arithmetic through DNA Excision (BLADE) platform to build more than 100 multi-input-multi-output circuits. We devised a quantitative metric to evaluate the performance of the circuits in human embryonic kidney and Jurkat T cells. Of 113 circuits analysed, 109 functioned (96.5%) with the correct specified behavior without any optimization. We used our platform to build a three-input, two-output Full Adder and six-input, one-output Boolean Logic Look Up Table. We also used BLADE to design circuits with temporal small molecule-mediated inducible control and circuits that incorporate CRISPR/Cas9 to regulate endogenous mammalian genes. PMID:28346402

Coherence properties of spontaneous parametric down-conversion pumped by a multi-mode cw diode laser.

PubMed

Kwon, Osung; Ra, Young-Sik; Kim, Yoon-Ho

2009-07-20

Coherence properties of the photon pair generated via spontaneous parametric down-conversion pumped by a multi-mode cw diode laser are studied with a Mach-Zehnder interferometer. Each photon of the pair enters a different input port of the interferometer and the biphoton coherence properties are studied with a two-photon detector placed at one output port. When the photon pair simultaneously enters the interferometer, periodic recurrence of the biphoton de Broglie wave packet is observed, closely resembling the coherence properties of the pump diode laser. With non-zero delays between the photons at the input ports, biphoton interference exhibits the same periodic recurrence but the wave packet shapes are shown to be dependent on both the input delay as well as the interferometer delay. These properties could be useful for building engineered entangled photon sources based on diode laser-pumped spontaneous parametric down-conversion.

A Design of Experiments Approach Defining the Relationships Between Processing and Microstructure for Ti-6Al-4V

NASA Technical Reports Server (NTRS)

Wallace, Terryl A.; Bey, Kim S.; Taminger, Karen M. B.; Hafley, Robert A.

2004-01-01

A study was conducted to evaluate the relative significance of input parameters on Ti- 6Al-4V deposits produced by an electron beam free form fabrication process under development at the NASA Langley Research Center. Five input parameters where chosen (beam voltage, beam current, translation speed, wire feed rate, and beam focus), and a design of experiments (DOE) approach was used to develop a set of 16 experiments to evaluate the relative importance of these parameters on the resulting deposits. Both single-bead and multi-bead stacks were fabricated using 16 combinations, and the resulting heights and widths of the stack deposits were measured. The resulting microstructures were also characterized to determine the impact of these parameters on the size of the melt pool and heat affected zone. The relative importance of each input parameter on the height and width of the multi-bead stacks will be discussed. .

Control design methods for floating wind turbines for optimal disturbance rejection

NASA Astrophysics Data System (ADS)

Lemmer, Frank; Schlipf, David; Cheng, Po Wen

2016-09-01

An analysis of the floating wind turbine as a multi-input-multi-output system investigating the effect of the control inputs on the system outputs is shown. These effects are compared to the ones of the disturbances from wind and waves in order to give insights for the selection of the control layout. The frequencies with the largest impact on the outputs due to limited effect of the controlled variables are identified. Finally, an optimal controller is designed as a benchmark and compared to a conventional PI-controller using only the rotor speed as input. Here, the previously found system properties, especially the difficulties to damp responses to wave excitation, are confirmed and verified through a spectral analysis with realistic environmental conditions. This comparison also assesses the quality of the employed simplified linear simulation model compared to the nonlinear model and shows that such an efficient frequency-domain evaluation for control design is feasible.

Feedback linearizing control of a MIMO power system

NASA Astrophysics Data System (ADS)

Ilyes, Laszlo

Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.

Decentralised fixed modes of networked MIMO systems

NASA Astrophysics Data System (ADS)

Hao, Yuqing; Duan, Zhisheng; Chen, Guanrong

2018-04-01

In this paper, decentralised fixed modes (DFMs) of a networked system are studied. The network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant (LTI) dynamical systems. The effects of the network topology, the node-system dynamics, the external control inputs, and the inner interactions on the existence of DFMs for the whole networked system are investigated. A necessary and sufficient condition for networked multi-input/multi-output (MIMO) systems in a general topology to possess no DFMs is derived. For networked single-input/single-output (SISO) LTI systems in general as well as some typical topologies, some specific conditions for having no DFMs are established. It is shown that the existence of DFMs is an integrated result of the aforementioned relevant factors which cannot be decoupled into individual DFMs of the node-systems and the properties solely determined by the network topology.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Analytical and experimental comparisons of electromechanical vibration response of a piezoelectric bimorph beam for power harvesting

NASA Astrophysics Data System (ADS)

Lumentut, M. F.; Howard, I. M.

2013-03-01

Power harvesters that extract energy from vibrating systems via piezoelectric transduction show strong potential for powering smart wireless sensor devices in applications of health condition monitoring of rotating machinery and structures. This paper presents an analytical method for modelling an electromechanical piezoelectric bimorph beam with tip mass under two input base transverse and longitudinal excitations. The Euler-Bernoulli beam equations were used to model the piezoelectric bimorph beam. The polarity-electric field of the piezoelectric element is excited by the strain field caused by base input excitation, resulting in electrical charge. The governing electromechanical dynamic equations were derived analytically using the weak form of the Hamiltonian principle to obtain the constitutive equations. Three constitutive electromechanical dynamic equations based on independent coefficients of virtual displacement vectors were formulated and then further modelled using the normalised Ritz eigenfunction series. The electromechanical formulations include both the series and parallel connections of the piezoelectric bimorph. The multi-mode frequency response functions (FRFs) under varying electrical load resistance were formulated using Laplace transformation for the multi-input mechanical vibrations to provide the multi-output dynamic displacement, velocity, voltage, current and power. The experimental and theoretical validations reduced for the single mode system were shown to provide reasonable predictions. The model results from polar base excitation for off-axis input motions were validated with experimental results showing the change to the electrical power frequency response amplitude as a function of excitation angle, with relevance for practical implementation.

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

MultiElec: A MATLAB Based Application for MEA Data Analysis.

PubMed

Georgiadis, Vassilis; Stephanou, Anastasis; Townsend, Paul A; Jackson, Thomas R

2015-01-01

We present MultiElec, an open source MATLAB based application for data analysis of microelectrode array (MEA) recordings. MultiElec displays an extremely user-friendly graphic user interface (GUI) that allows the simultaneous display and analysis of voltage traces for 60 electrodes and includes functions for activation-time determination, the production of activation-time heat maps with activation time and isoline display. Furthermore, local conduction velocities are semi-automatically calculated along with their corresponding vector plots. MultiElec allows ad hoc signal suppression, enabling the user to easily and efficiently handle signal artefacts and for incomplete data sets to be analysed. Voltage traces and heat maps can be simply exported for figure production and presentation. In addition, our platform is able to produce 3D videos of signal progression over all 60 electrodes. Functions are controlled entirely by a single GUI with no need for command line input or any understanding of MATLAB code. MultiElec is open source under the terms of the GNU General Public License as published by the Free Software Foundation, version 3. Both the program and source code are available to download from http://www.cancer.manchester.ac.uk/MultiElec/.

A grid generation system for multi-disciplinary design optimization

NASA Technical Reports Server (NTRS)

Jones, William T.; Samareh-Abolhassani, Jamshid

1995-01-01

A general multi-block three-dimensional volume grid generator is presented which is suitable for Multi-Disciplinary Design Optimization. The code is timely, robust, highly automated, and written in ANSI 'C' for platform independence. Algebraic techniques are used to generate and/or modify block face and volume grids to reflect geometric changes resulting from design optimization. Volume grids are generated/modified in a batch environment and controlled via an ASCII user input deck. This allows the code to be incorporated directly into the design loop. Generated volume grids are presented for a High Speed Civil Transport (HSCT) Wing/Body geometry as well a complex HSCT configuration including horizontal and vertical tails, engine nacelles and pylons, and canard surfaces.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

A joint precoding scheme for indoor downlink multi-user MIMO VLC systems

NASA Astrophysics Data System (ADS)

Zhao, Qiong; Fan, Yangyu; Kang, Bochao

2017-11-01

In this study, we aim to improve the system performance and reduce the implementation complexity of precoding scheme for visible light communication (VLC) systems. By incorporating the power-method algorithm and the block diagonalization (BD) algorithm, we propose a joint precoding scheme for indoor downlink multi-user multi-input-multi-output (MU-MIMO) VLC systems. In this scheme, we apply the BD algorithm to eliminate the co-channel interference (CCI) among users firstly. Secondly, the power-method algorithm is used to search the precoding weight for each user based on the optimal criterion of signal to interference plus noise ratio (SINR) maximization. Finally, the optical power restrictions of VLC systems are taken into account to constrain the precoding weight matrix. Comprehensive computer simulations in two scenarios indicate that the proposed scheme always has better bit error rate (BER) performance and lower computation complexity than that of the traditional scheme.

Estimating multi-period global cost efficiency and productivity change of systems with network structures

NASA Astrophysics Data System (ADS)

Tohidnia, S.; Tohidi, G.

2018-02-01

The current paper develops three different ways to measure the multi-period global cost efficiency for homogeneous networks of processes when the prices of exogenous inputs are known at all time periods. A multi-period network data envelopment analysis model is presented to measure the minimum cost of the network system based on the global production possibility set. We show that there is a relationship between the multi-period global cost efficiency of network system and its subsystems, and also its processes. The proposed model is applied to compute the global cost Malmquist productivity index for measuring the productivity changes of network system and each of its process between two time periods. This index is circular. Furthermore, we show that the productivity changes of network system can be defined as a weighted average of the process productivity changes. Finally, a numerical example will be presented to illustrate the proposed approach.

Tunable single-photon multi-channel quantum router based on an optomechanical system

NASA Astrophysics Data System (ADS)

Ma, Peng-Cheng; Yan, Lei-Lei; Zhang, Jian; Chen, Gui-Bin; Li, Xiao-Wei; Zhan, You-Bang

2018-01-01

Routing of photons plays a key role in optical communication networks and quantum networks. Although the quantum routing of signals has been investigated for various systems, both in theory and experiment, the general form of a quantum router with multi-output terminals still needs to be explored. Here, we propose an experimentally accessible tunable single-photon multi-channel routing scheme using an optomechanics cavity which is Coulomb coupled to a nanomechanical resonator. The router can extract single photons from the coherent input signal and directly modulate them into three different output channels. More importantly, the two output signal frequencies can be selected by adjusting the Coulomb coupling strength. For application purposes, we justify that there is insignificant influence from the vacuum and thermal noises on the performance of the router under cryogenic conditions. Our proposal may pave a new avenue towards multi-channel routers and quantum networks.

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

NASA Technical Reports Server (NTRS)

Reith, Ernest; Roberts, Dar A.; Prentiss, Dylan

2001-01-01

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Multi-input and -output logic circuits based on bioelectrocatalysis with horseradish peroxidase and glucose oxidase immobilized in multi-responsive copolymer films on electrodes.

PubMed

Yu, Xue; Lian, Wenjing; Zhang, Jiannan; Liu, Hongyun

2016-06-15

Herein, poly(N-isopropylacrylamide-co-N,N'-dimethylaminoethylmethacrylate) copolymer films were polymerized on electrode surface with a simple one-step method, and the enzyme horseradish peroxidase (HRP) was embedded in the films simultaneously, which were designated as P(NiPAAm-co-DMEM)-HRP. The films exhibited a reversible structure change with the external stimuli, such as pH, CO2, temperature and SO4(2-), causing the cyclic voltammetric (CV) response of electroactive K3Fe(CN)6 at the film electrodes to display the corresponding multi-stimuli sensitive ON-OFF behavior. Based on the switchable CV property of the system and the electrochemical reduction of H2O2 catalyzed by HRP in the films and mediated by Fe(CN)6(3-) in solution, a 5-input/3-output logic gate was established. To further increase the complexity of the logic system, another enzyme glucose oxidase (GOD) was added into the films, designated as P(NiPAAm-co-DMEM)-HRP-GOD. In the presence of oxygen, the oxidation of glucose in the solution was catalyzed by GOD in the films, and the produced H2O2 in situ was recognized and electrocatalytically reduced by HRP and mediated by Fe(CN)6(3-). Based on the bienzyme films, a cascaded or concatenated 4-input/3-output logic gate system was proposed. The present work combined the multi-responsive interface with bioelectrocatalysis to construct cascaded logic circuits, which might open a new avenue to develop biocomputing elements with more sophisticated functions and design novel glucose biosensors. Copyright © 2016 Elsevier B.V. All rights reserved.

An enhanced multi-channel bacterial foraging optimization algorithm for MIMO communication system

NASA Astrophysics Data System (ADS)

Palanimuthu, Senthilkumar Jayalakshmi; Muthial, Chandrasekaran

2017-04-01

Channel estimation and optimisation are the main challenging tasks in Multi Input Multi Output (MIMO) wireless communication systems. In this work, a Multi-Channel Bacterial Foraging Optimization Algorithm approach is proposed for the selection of antenna in a transmission area. The main advantage of this method is, it reduces the loss of bandwidth during data transmission effectively. Here, we considered the channel estimation and optimisation for improving the transmission speed and reducing the unused bandwidth. Initially, the message is given to the input of the communication system. Then, the symbol mapping process is performed for converting the message into signals. It will be encoded based on the space-time encoding technique. Here, the single signal is divided into multiple signals and it will be given to the input of space-time precoder. Hence, the multiplexing is applied to transmission channel estimation. In this paper, the Rayleigh channel is selected based on the bandwidth range. This is the Gaussian distribution type channel. Then, the demultiplexing is applied on the obtained signal that is the reverse function of multiplexing, which splits the combined signal arriving from a medium into the original information signal. Furthermore, the long-term evolution technique is used for scheduling the time to channels during transmission. Here, the hidden Markov model technique is employed to predict the status information of the channel. Finally, the signals are decoded and the reconstructed signal is obtained after performing the scheduling process. The experimental results evaluate the performance of the proposed MIMO communication system in terms of bit error rate, mean squared error, average throughput, outage capacity and signal to interference noise ratio.

77 FR 14031 - Public Listening Sessions To Obtain Input on the Multi-Stakeholder Group Tasked With the...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-08

... DEPARTMENT OF THE INTERIOR [Docket No. ONRR-2012-0002] Public Listening Sessions To Obtain Input... Transparency Initiative AGENCY: Office of the Secretary, Interior. ACTION: Notice. SUMMARY: The Department of the Interior (Department) announces four public listening sessions to receive comments regarding a...

Pure JavaScript Storyline Layout Algorithm

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

This is a JavaScript library for a storyline layout algorithm. Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The library in this disclosure takes as input a list of objects containing an id, time, and state. The output is a data structure that can be used to conveniently render a storyline visualization. Most importantly, the library computes the y-coordinate of the entities over time that decreases layout artifacts including crossings, wiggles, and whitespace. This is accomplished through multi-objective, multi-stage optimizationmore » problem, where the output of one stage produces input and constraints for the next stage.« less

Consensus for multi-agent systems with time-varying input delays

NASA Astrophysics Data System (ADS)

Yuan, Chengzhi; Wu, Fen

2017-10-01

This paper addresses the consensus control problem for linear multi-agent systems subject to uniform time-varying input delays and external disturbance. A novel state-feedback consensus protocol is proposed under the integral quadratic constraint (IQC) framework, which utilises not only the relative state information from neighbouring agents but also the real-time information of delays by means of the dynamic IQC system states for feedback control. Based on this new consensus protocol, the associated IQC-based control synthesis conditions are established and fully characterised as linear matrix inequalities (LMIs), such that the consensus control solution with optimal ? disturbance attenuation performance can be synthesised efficiently via convex optimisation. A numerical example is used to demonstrate the proposed approach.

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion

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

He, Xu; Tuo, Rui; Jeff Wu, C. F.

Computer experiments based on mathematical models are powerful tools for understanding physical processes. This article addresses the problem of kriging-based optimization for deterministic computer experiments with tunable accuracy. Our approach is to use multi- delity computer experiments with increasing accuracy levels and a nonstationary Gaussian process model. We propose an optimization scheme that sequentially adds new computer runs by following two criteria. The first criterion, called EQI, scores candidate inputs with given level of accuracy, and the second criterion, called EQIE, scores candidate combinations of inputs and accuracy. Here, from simulation results and a real example using finite element analysis,more » our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.« less

Device for modular input high-speed multi-channel digitizing of electrical data

DOEpatents

VanDeusen, Alan L.; Crist, Charles E.

1995-09-26

A multi-channel high-speed digitizer module converts a plurality of analog signals to digital signals (digitizing) and stores the signals in a memory device. The analog input channels are digitized simultaneously at high speed with a relatively large number of on-board memory data points per channel. The module provides an automated calibration based upon a single voltage reference source. Low signal noise at such a high density and sample rate is accomplished by ensuring the A/D converters are clocked at the same point in the noise cycle each time so that synchronous noise sampling occurs. This sampling process, in conjunction with an automated calibration, yields signal noise levels well below the noise level present on the analog reference voltages.

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion

DOE PAGES

He, Xu; Tuo, Rui; Jeff Wu, C. F.

2017-01-31

Computer experiments based on mathematical models are powerful tools for understanding physical processes. This article addresses the problem of kriging-based optimization for deterministic computer experiments with tunable accuracy. Our approach is to use multi- delity computer experiments with increasing accuracy levels and a nonstationary Gaussian process model. We propose an optimization scheme that sequentially adds new computer runs by following two criteria. The first criterion, called EQI, scores candidate inputs with given level of accuracy, and the second criterion, called EQIE, scores candidate combinations of inputs and accuracy. Here, from simulation results and a real example using finite element analysis,more » our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.« less

Adaptive nonlinear control for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Black, William S.

We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.

A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms.

PubMed

Alsmadi, Othman M K; Abo-Hammour, Zaer S

2015-01-01

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements of B, C, and D matrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

Multi-diversity combining and selection for relay-assisted mixed RF/FSO system

NASA Astrophysics Data System (ADS)

Chen, Li; Wang, Weidong

2017-12-01

We propose and analyze multi-diversity combining and selection to enhance the performance of relay-assisted mixed radio frequency/free-space optics (RF/FSO) system. We focus on a practical scenario for cellular network where a single-antenna source is communicating to a multi-apertures destination through a relay equipped with multiple receive antennas and multiple transmit apertures. The RF single input multiple output (SIMO) links employ either maximal-ratio combining (MRC) or receive antenna selection (RAS), and the FSO multiple input multiple output (MIMO) links adopt either repetition coding (RC) or transmit laser selection (TLS). The performance is evaluated via an outage probability analysis over Rayleigh fading RF links and Gamma-Gamma atmospheric turbulence FSO links with pointing errors where channel state information (CSI) assisted amplify-and-forward (AF) scheme is considered. Asymptotic closed-form expressions at high signal-to-noise ratio (SNR) are also derived. Coding gain and diversity order for different combining and selection schemes are further discussed. Numerical results are provided to verify and illustrate the analytical results.

Uncertainty Quantification in Multi-Scale Coronary Simulations Using Multi-resolution Expansion

NASA Astrophysics Data System (ADS)

Tran, Justin; Schiavazzi, Daniele; Ramachandra, Abhay; Kahn, Andrew; Marsden, Alison

2016-11-01

Computational simulations of coronary flow can provide non-invasive information on hemodynamics that can aid in surgical planning and research on disease propagation. In this study, patient-specific geometries of the aorta and coronary arteries are constructed from CT imaging data and finite element flow simulations are carried out using the open source software SimVascular. Lumped parameter networks (LPN), consisting of circuit representations of vascular hemodynamics and coronary physiology, are used as coupled boundary conditions for the solver. The outputs of these simulations depend on a set of clinically-derived input parameters that define the geometry and boundary conditions, however their values are subjected to uncertainty. We quantify the effects of uncertainty from two sources: uncertainty in the material properties of the vessel wall and uncertainty in the lumped parameter models whose values are estimated by assimilating patient-specific clinical and literature data. We use a generalized multi-resolution chaos approach to propagate the uncertainty. The advantages of this approach lies in its ability to support inputs sampled from arbitrary distributions and its built-in adaptivity that efficiently approximates stochastic responses characterized by steep gradients.

Computational simulation of weld microstructure and distortion by considering process mechanics

NASA Astrophysics Data System (ADS)

Mochizuki, M.; Mikami, Y.; Okano, S.; Itoh, S.

2009-05-01

Highly precise fabrication of welded materials is in great demand, and so microstructure and distortion controls are essential. Furthermore, consideration of process mechanics is important for intelligent fabrication. In this study, the microstructure and hardness distribution in multi-pass weld metal are evaluated by computational simulations under the conditions of multiple heat cycles and phase transformation. Because conventional CCT diagrams of weld metal are not available even for single-pass weld metal, new diagrams for multi-pass weld metals are created. The weld microstructure and hardness distribution are precisely predicted when using the created CCT diagram for multi-pass weld metal and calculating the weld thermal cycle. Weld distortion is also investigated by using numerical simulation with a thermal elastic-plastic analysis. In conventional evaluations of weld distortion, the average heat input has been used as the dominant parameter; however, it is difficult to consider the effect of molten pool configurations on weld distortion based only on the heat input. Thus, the effect of welding process conditions on weld distortion is studied by considering molten pool configurations, determined by temperature distribution and history.

Impact of Acoustic Standing Waves on Structural Responses: Reverberant Acoustic Testing (RAT) vs. Direct Field Acoustic Testing (DFAT)

NASA Technical Reports Server (NTRS)

Kolaini, Ali R.; Doty, Benjamin; Chang, Zensheu

2012-01-01

Loudspeakers have been used for acoustic qualification of spacecraft, reflectors, solar panels, and other acoustically responsive structures for more than a decade. Limited measurements from some of the recent speaker tests used to qualify flight hardware have indicated significant spatial variation of the acoustic field within the test volume. Also structural responses have been reported to differ when similar tests were performed using reverberant chambers. To address the impact of non-uniform acoustic field on structural responses, a series of acoustic tests were performed using a flat panel and a 3-ft cylinder exposed to the field controlled by speakers and repeated in a reverberant chamber. The speaker testing was performed using multi-input-single-output (MISO) and multi-input-multi-output (MIMO) control schemes with and without the test articles. In this paper the spatial variation of the acoustic field due to acoustic standing waves and their impacts on the structural responses in RAT and DFAT (both using MISO and MIMO controls for DFAT) are discussed in some detail.

Investigation into the Use of Texturing for Real-Time Computer Animation.

DTIC Science & Technology

1987-12-01

produce a rough polygon surface [7]. Research in the area of real time texturing has also been conducted. Using a specially designed multi-processor system ...Oka, Tsutsui, Ohba, Kurauchi and Tago have introduced real-time manipulation of texture mapped surfaces [8]. Using multi- processors, systems will...a call to the system function defpattern(n,size,mask) short n,size; short *mask, which takes as input an index to a system table of patterns, a

Heterogeneous Multi-Metric Learning for Multi-Sensor Fusion

DTIC Science & Technology

2011-07-01

distance”. One of the most widely used methods is the k-nearest neighbor ( KNN ) method [4], which labels an input data sample to be the class with majority...despite of its simplicity, it can be an effective candidate and can be easily extended to handle multiple sensors. Distance based method such as KNN relies...Neighbor (LMNN) method [21] which will be briefly reviewed in the sequel. LMNN method tries to learn an optimal metric specifically for KNN classifier. The

X-band T/R switch with body-floating multi-gate PDSOI NMOS transistors

NASA Astrophysics Data System (ADS)

Park, Mingyo; Min, Byung-Wook

2018-03-01

This paper presents an X-band transmit/receive switch using multi-gate NMOS transistors in a silicon-on-insulator CMOS process. For low loss and high power handling capability, floating body multi-gate NMOS transistors are adopted instead of conventional stacked NMOS transistors, resulting in 53% reduction of transistor area. Comparing to the stacked NMOS transistors, the multi gate transistor shares the source and drain region between stacked transistors, resulting in reduced chip area and parasitics. The impedance between bodies of gates in multi-gate NMOS transistors is assumed to be very large during design and confirmed after measurement. The measured input 1 dB compression point is 34 dBm. The measured insertion losses of TX and RX modes are respectively 1.7 dB and 2.0 dB at 11 GHz, and the measured isolations of TX and RX modes are >27 dB and >20 dB in X-band, respectively. The chip size is 0.086 mm2 without pads, which is 25% smaller than the T/R switch with stacked transistors.

Wide bandgap matrix switcher, amplifier and oscillator

DOEpatents

Sampayan, Stephen

2016-08-16

An electronic device comprising an optical gate, an electrical input an electrical output and a wide bandgap material positioned between the electrical input and the electrical output to control an amount of current flowing between the electrical input and the electrical output in response to a stimulus received at the optical gate can be used in wideband telecommunication applications in transmission of multi-channel signals.

PyXRD v0.6.7: a free and open-source program to quantify disordered phyllosilicates using multi-specimen X-ray diffraction profile fitting

NASA Astrophysics Data System (ADS)

Dumon, M.; Van Ranst, E.

2016-01-01

This paper presents a free and open-source program called PyXRD (short for Python X-ray diffraction) to improve the quantification of complex, poly-phasic mixed-layer phyllosilicate assemblages. The validity of the program was checked by comparing its output with Sybilla v2.2.2, which shares the same mathematical formalism. The novelty of this program is the ab initio incorporation of the multi-specimen method, making it possible to share phases and (a selection of) their parameters across multiple specimens. PyXRD thus allows for modelling multiple specimens side by side, and this approach speeds up the manual refinement process significantly. To check the hypothesis that this multi-specimen set-up - as it effectively reduces the number of parameters and increases the number of observations - can also improve automatic parameter refinements, we calculated X-ray diffraction patterns for four theoretical mineral assemblages. These patterns were then used as input for one refinement employing the multi-specimen set-up and one employing the single-pattern set-ups. For all of the assemblages, PyXRD was able to reproduce or approximate the input parameters with the multi-specimen approach. Diverging solutions only occurred in single-pattern set-ups, which do not contain enough information to discern all minerals present (e.g. patterns of heated samples). Assuming a correct qualitative interpretation was made and a single pattern exists in which all phases are sufficiently discernible, the obtained results indicate a good quantification can often be obtained with just that pattern. However, these results from theoretical experiments cannot automatically be extrapolated to all real-life experiments. In any case, PyXRD has proven to be useful when X-ray diffraction patterns are modelled for complex mineral assemblages containing mixed-layer phyllosilicates with a multi-specimen approach.

Identifying multiple timescale rainfall controls on Mojave Desert ecohydrology using an integrated data and modeling approach for Larrea tridentata

USGS Publications Warehouse

Ng, Gene-Hua Crystal; Bedford, David R.; Miller, David M.

2015-01-01

The perennial shrub Larrea tridentata is widely successful in North American warm deserts but is also susceptible to climatic perturbations. Understanding its response to rainfall variability requires consideration of multiple timescales. We examine intra-annual to multi-year relationships using model simulations of soil moisture and vegetation growth over 50 years in the Mojave National Preserve in southeastern California (USA). Ecohydrological model parameters are conditioned on field and remote sensing data using an ensemble Kalman filter. Although no specific periodicities were detected in the rainfall record, simulated leaf-area-index exhibits multi-year dynamics that are driven by multi-year (∼3-years) rains, but with up to a 1-year delay in peak response. Within a multi-year period, Larrea tridentata is more sensitive to winter rains than summer. In the most active part of the root zone (above ∼80 cm), >1-year average soil moisture drives vegetation growth, but monthly average soil moisture is controlled by root uptake. Moisture inputs reach the lower part of the root zone (below ∼80 cm) infrequently, but once there they can persist over a year to help sustain plant growth. Parameter estimates highlight efficient plant physiological properties facilitating persistent growth and high soil hydraulic conductivity allowing deep soil moisture stores. We show that soil moisture as an ecological indicator is complicated by bidirectional interactions with vegetation that depend on timescale and depth. Under changing climate, Larrea tridentata will likely be relatively resilient to shorter-term moisture variability but will exhibit higher sensitivity to shifts in seasonal to multi-year moisture inputs.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

PubMed Central

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-01-01

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

Optimized System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Longman, Richard W.

1999-01-01

In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.

Uncertainty in modeled upper ocean heat content change

NASA Astrophysics Data System (ADS)

Tokmakian, Robin; Challenor, Peter

2014-02-01

This paper examines the uncertainty in the change in the heat content in the ocean component of a general circulation model. We describe the design and implementation of our statistical methodology. Using an ensemble of model runs and an emulator, we produce an estimate of the full probability distribution function (PDF) for the change in upper ocean heat in an Atmosphere/Ocean General Circulation Model, the Community Climate System Model v. 3, across a multi-dimensional input space. We show how the emulator of the GCM's heat content change and hence, the PDF, can be validated and how implausible outcomes from the emulator can be identified when compared to observational estimates of the metric. In addition, the paper describes how the emulator outcomes and related uncertainty information might inform estimates of the same metric from a multi-model Coupled Model Intercomparison Project phase 3 ensemble. We illustrate how to (1) construct an ensemble based on experiment design methods, (2) construct and evaluate an emulator for a particular metric of a complex model, (3) validate the emulator using observational estimates and explore the input space with respect to implausible outcomes and (4) contribute to the understanding of uncertainties within a multi-model ensemble. Finally, we estimate the most likely value for heat content change and its uncertainty for the model, with respect to both observations and the uncertainty in the value for the input parameters.

General Strategy for Broadband Coherent Perfect Absorption and Multi-wavelength All-optical Switching Based on Epsilon-Near-Zero Multilayer Films

PubMed Central

Kim, Tae Young; Badsha, Md. Alamgir; Yoon, Junho; Lee, Seon Young; Jun, Young Chul; Hwangbo, Chang Kwon

2016-01-01

We propose a general, easy-to-implement scheme for broadband coherent perfect absorption (CPA) using epsilon-near-zero (ENZ) multilayer films. Specifically, we employ indium tin oxide (ITO) as a tunable ENZ material, and theoretically investigate CPA in the near-infrared region. We first derive general CPA conditions using the scattering matrix and the admittance matching methods. Then, by combining these two methods, we extract analytic expressions for all relevant parameters for CPA. Based on this theoretical framework, we proceed to study ENZ CPA in a single layer ITO film and apply it to all-optical switching. Finally, using an ITO multilayer of different ENZ wavelengths, we implement broadband ENZ CPA structures and investigate multi-wavelength all-optical switching in the technologically important telecommunication window. In our design, the admittance matching diagram was employed to graphically extract not only the structural parameters (the film thicknesses and incident angles), but also the input beam parameters (the irradiance ratio and phase difference between two input beams). We find that the multi-wavelength all-optical switching in our broadband ENZ CPA system can be fully controlled by the phase difference between two input beams. The simple but general design principles and analyses in this work can be widely used in various thin-film devices. PMID:26965195

Early Examples from the Integrated Multi-Satellite Retrievals for GPM (IMERG)

NASA Astrophysics Data System (ADS)

Huffman, George; Bolvin, David; Braithwaite, Daniel; Hsu, Kuolin; Joyce, Robert; Kidd, Christopher; Sorooshian, Soroosh; Xie, Pingping

2014-05-01

The U.S. GPM Science Team's Day-1 algorithm for computing combined precipitation estimates as part of GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). The goal is to compute the best time series of (nearly) global precipitation from "all" precipitation-relevant satellites and global surface precipitation gauge analyses. IMERG is being developed as a unified U.S. algorithm drawing on strengths in the three contributing groups, whose previous work includes: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA); 2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH); and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS). We review the IMERG design and development, plans for testing, and current status. Some of the lessons learned in running and reprocessing the previous data sets include the importance of quality-controlling input data sets, strategies for coping with transitions in the various input data sets, and practical approaches to retrospective analysis of multiple output products (namely the real- and post-real-time data streams). IMERG output will be illustrated using early test data, including the variety of supporting fields, such as the merged-microwave and infrared estimates, and the precipitation type. We end by considering recent changes in input data specifications, the transition from TRMM-based calibration to GPM-based, and further "Day 2" development.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

PubMed

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers

PubMed Central

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Abstract Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlOx), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers. PMID:28634499

Three-input gate logic circuits on chemically assembled single-electron transistors with organic and inorganic hybrid passivation layers.

PubMed

Majima, Yutaka; Hackenberger, Guillaume; Azuma, Yasuo; Kano, Shinya; Matsuzaki, Kosuke; Susaki, Tomofumi; Sakamoto, Masanori; Teranishi, Toshiharu

2017-01-01

Single-electron transistors (SETs) are sub-10-nm scale electronic devices based on conductive Coulomb islands sandwiched between double-barrier tunneling barriers. Chemically assembled SETs with alkanethiol-protected Au nanoparticles show highly stable Coulomb diamonds and two-input logic operations. The combination of bottom-up and top-down processes used to form the passivation layer is vital for realizing multi-gate chemically assembled SET circuits, as this combination enables us to connect conventional complementary metal oxide semiconductor (CMOS) technologies via planar processes. Here, three-input gate exclusive-OR (XOR) logic operations are demonstrated in passivated chemically assembled SETs. The passivation layer is a hybrid bilayer of self-assembled monolayers (SAMs) and pulsed laser deposited (PLD) aluminum oxide (AlO[Formula: see text]), and top-gate electrodes were prepared on the hybrid passivation layers. Top and two-side-gated SETs showed clear Coulomb oscillation and diamonds for each of the three available gates, and three-input gate XOR logic operation was clearly demonstrated. These results show the potential of chemically assembled SETs to work as logic devices with multi-gate inputs using organic and inorganic hybrid passivation layers.

Overview of multi-input frequency domain modal testing methods with an emphasis on sine testing

NASA Technical Reports Server (NTRS)

Rost, Robert W.; Brown, David L.

1988-01-01

An overview of the current state of the art multiple-input, multiple-output modal testing technology is discussed. A very brief review of the current time domain methods is given. A detailed review of frequency and spatial domain methods is presented with an emphasis on sine testing.

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Information rates of probabilistically shaped coded modulation for a multi-span fiber-optic communication system with 64QAM

NASA Astrophysics Data System (ADS)

Fehenberger, Tobias

2018-02-01

This paper studies probabilistic shaping in a multi-span wavelength-division multiplexing optical fiber system with 64-ary quadrature amplitude modulation (QAM) input. In split-step fiber simulations and via an enhanced Gaussian noise model, three figures of merit are investigated, which are signal-to-noise ratio (SNR), achievable information rate (AIR) for capacity-achieving forward error correction (FEC) with bit-metric decoding, and the information rate achieved with low-density parity-check (LDPC) FEC. For the considered system parameters and different shaped input distributions, shaping is found to decrease the SNR by 0.3 dB yet simultaneously increases the AIR by up to 0.4 bit per 4D-symbol. The information rates of LDPC-coded modulation with shaped 64QAM input are improved by up to 0.74 bit per 4D-symbol, which is larger than the shaping gain when considering AIRs. This increase is attributed to the reduced coding gap of the higher-rate code that is used for decoding the nonuniform QAM input.

Input Shaping enhanced Active Disturbance Rejection Control for a twin rotor multi-input multi-output system (TRMS).

PubMed

Yang, Xiaoyan; Cui, Jianwei; Lao, Dazhong; Li, Donghai; Chen, Junhui

2016-05-01

In this paper, a composite control based on Active Disturbance Rejection Control (ADRC) and Input Shaping is presented for TRMS with two degrees of freedom (DOF). The control tasks consist of accurately tracking desired trajectories and obtaining disturbance rejection in both horizontal and vertical planes. Due to un-measurable states as well as uncertainties stemming from modeling uncertainty and unknown disturbance torques, ADRC is employed, and feed-forward Input Shaping is used to improve the dynamical response. In the proposed approach, because the coupling effects are maintained in controller derivation, there is no requirement to decouple the TRMS into horizontal and vertical subsystems, which is usually performed in the literature. Finally, the proposed method is implemented on the TRMS platform, and the results are compared with those of PID and ADRC in a similar structure. The experimental results demonstrate the effectiveness of the proposed method. The operation of the controller allows for an excellent set-point tracking behavior and disturbance rejection with system nonlinearity and complex coupling conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-Input Multi-Output Flight Control System Design for the YF-16 Using Nonlinear QFT and Pilot Compensation

DTIC Science & Technology

1990-12-01

methods are implemented in MATRIXx with the programs SISOTF and MIMOTF respectively. Following the mathe - matical development, the application of these...intent is not to teach any of the methods , it has been written in a manner to significantly assist an individual attempting follow on work. I would...equivalent plant models. A detailed mathematical development of the method used to develop these equivalent LTI plant models is provided. After this inner

Multi-Rate Digital Control Systems with Simulation Applications. Volume I. Technical Report

DTIC Science & Technology

1980-09-01

108 45. A Pseudo Differentiation Configuration ........................ 110 46. Bode Plot, Pseudo Differentiation ...symbolically in Fig. 7a and for 11 x 2 in Fig. 7b. (* notation on x2is used here to indicate an "unconven- tional" sampling operation.) 115 TXi ,A! T...the general multi-rate/multiple-order open-loop system of Fig. 21 have a sine wave input. In Fig 2L, = (GIRj) (114) CT/N = [GGRt]T/N ( 115 ) where a, B

Hybrid Solution of Stochastic Optimal Control Problems Using Gauss Pseudospectral Method and Generalized Polynomial Chaos Algorithms

DTIC Science & Technology

2012-03-01

0-486-41183-4. 15. Brown , Robert G. and Patrick Y. C. Hwang . Introduction to Random Signals and Applied Kalman Filtering. Wiley, New York, 1996. ISBN...stability and perfor- mance criteria. In the 1960’s, Kalman introduced the Linear Quadratic Regulator (LQR) method using an integral performance index...feedback of the state variables and was able to apply this method to time-varying and Multi-Input Multi-Output (MIMO) systems. Kalman further showed

Rapid rotational/translational maneuvering experiments of a flexible steel beam

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Yang, Li-Farn; Huanag, Jen-Kuang; Macauley, Richard

1989-01-01

Future space manipulators may need translational base motion to expand the access region of a manipulator. An experiment was conducted to demonstrate slewing of flexible structures with coupled rotational and translational axes while simultaneously suppressing vibrational motion during the maneuver. In the experiment, a flexible steel beam carried by a translational cart was maneuvered by an active controller to perform position-control tasks. Experimental results are presented to show how the flexibility of the steel beam influences the multi-input multi-output feedback controller.

Networked Guidance and Control for Mobile Multi-Agent Systems: A Multi-terminal (Network) Information Theoretic Approach

DTIC Science & Technology

2012-01-19

time , i.e., the state of the system is the input delayed by one time unit. In contrast with classical approaches, here the control action must be a...Transactions on Automatic Control , Vol. 56, No. 9, September 2011, Pages 2013-2025 Consider a first order linear time -invariant discrete time system driven by...1, January 2010, Pages 175-179 Consider a discrete- time networked control system , in which the controller has direct access to noisy

Mathematical modelling of the electric sense of fish: the role of multi-frequency measurements and movement.

PubMed

Ammari, Habib; Boulier, Thomas; Garnier, Josselin; Wang, Han

2017-01-31

Understanding active electrolocation in weakly electric fish remains a challenging issue. In this article we propose a mathematical formulation of this problem, in terms of partial differential equations. This allows us to detail two algorithms: one for localizing a target using the multi-frequency aspect of the signal, and another one for identifying the shape of this target. Shape recognition is designed in a machine learning point of view, and takes advantage of both the multi-frequency setup and the movement of the fish around its prey. Numerical simulations are shown for the computation of the electric field emitted and sensed by the fish; they are then used as an input for the two algorithms.

Multiple-function multi-input/multi-output digital control and on-line analysis

NASA Technical Reports Server (NTRS)

Hoadley, Sherwood T.; Wieseman, Carol D.; Mcgraw, Sandra M.

1992-01-01

The design and capabilities of two digital controller systems for aeroelastic wind-tunnel models are described. The first allowed control of flutter while performing roll maneuvers with wing load control as well as coordinating the acquisition, storage, and transfer of data for on-line analysis. This system, which employs several digital signal multi-processor (DSP) boards programmed in high-level software languages, is housed in a SUN Workstation environment. A second DCS provides a measure of wind-tunnel safety by functioning as a trip system during testing in the case of high model dynamic response or in case the first DCS fails. The second DCS uses National Instruments LabVIEW Software and Hardware within a Macintosh environment.

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Device for modular input high-speed multi-channel digitizing of electrical data

DOEpatents

VanDeusen, A.L.; Crist, C.E.

1995-09-26

A multi-channel high-speed digitizer module converts a plurality of analog signals to digital signals (digitizing) and stores the signals in a memory device. The analog input channels are digitized simultaneously at high speed with a relatively large number of on-board memory data points per channel. The module provides an automated calibration based upon a single voltage reference source. Low signal noise at such a high density and sample rate is accomplished by ensuring the A/D converters are clocked at the same point in the noise cycle each time so that synchronous noise sampling occurs. This sampling process, in conjunction with an automated calibration, yields signal noise levels well below the noise level present on the analog reference voltages. 1 fig.

GPC-Based Stable Reconfigurable Control

NASA Technical Reports Server (NTRS)

Soloway, Don; Shi, Jian-Jun; Kelkar, Atul

2004-01-01

This paper presents development of multi-input multi-output (MIMO) Generalized Pre-dictive Control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. A Controlled Auto-Regressive Integrating Moving Average (CARIMA) model is used to describe the plant dynamics. The control law is derived using input-output description of the system and is also related to the state-space form of the model. The stability of the GPC control law without reconfiguration is first established using Riccati-based approach and state-space formulation. A novel reconfiguration strategy is developed for the systems which have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable control design is presented with stability proof. Several numerical examples are presented to demonstrate the application of various results.

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Texas two-step: a framework for optimal multi-input single-output deconvolution.

PubMed

Neelamani, Ramesh; Deffenbaugh, Max; Baraniuk, Richard G

2007-11-01

Multi-input single-output deconvolution (MISO-D) aims to extract a deblurred estimate of a target signal from several blurred and noisy observations. This paper develops a new two step framework--Texas Two-Step--to solve MISO-D problems with known blurs. Texas Two-Step first reduces the MISO-D problem to a related single-input single-output deconvolution (SISO-D) problem by invoking the concept of sufficient statistics (SSs) and then solves the simpler SISO-D problem using an appropriate technique. The two-step framework enables new MISO-D techniques (both optimal and suboptimal) based on the rich suite of existing SISO-D techniques. In fact, the properties of SSs imply that a MISO-D algorithm is mean-squared-error optimal if and only if it can be rearranged to conform to the Texas Two-Step framework. Using this insight, we construct new wavelet- and curvelet-based MISO-D algorithms with asymptotically optimal performance. Simulated and real data experiments verify that the framework is indeed effective.

Multi input single output model predictive control of non-linear bio-polymerization process

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

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less

Nicotine replacement therapy decision based on fuzzy multi-criteria analysis

NASA Astrophysics Data System (ADS)

Tarmudi, Zamali; Matmali, Norfazillah; Abdullah, Mohd Lazim

2017-08-01

It has been observed that Nicotine Replacement Therapy (NRT) is one of the alternatives to control and reduce smoking addiction among smokers. Since the decision to choose the best NRT alternative involves uncertainty, ambiguity factors and diverse input datasets, thus, this paper proposes a fuzzy multi-criteria analysis (FMA) to overcome these issues. It focuses on how the fuzzy approach can unify the diversity of datasets based on NRT's decision-making problem. The analysis done employed the advantage of the cost-benefit criterion to unify the mixture of dataset input. The performance matrix was utilised to derive the performance scores. An empirical example regarding the NRT's decision-making problem was employed to illustrate the proposed approach. Based on the calculations, this analytical approach was found to be highly beneficial in terms of usability. It was also very applicable and efficient in dealing with the mixture of input datasets. Hence, the decision-making process can easily be used by experts and patients who are interested to join the therapy/cessation program.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Robust predictive control with optimal load tracking for critical applications. Final report

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

Tse, J.; Bentsman, J.; Miller, N.

1994-09-01

This report derives a multi-input multi-output (MIMO) version of a two-degree-of-freedom receding-horizon control law based on mixed H{sub 2}/H{infinity} minimization. First, the integrand in the frequency domain representation of the MIMO performance criterion is decomposed into disturbance and reference spectra. Then the controller is derived which minimizes the peak of the disturbance spectrum and the integral of the reference spectrum on the unit circle. The resulting two-degree-of-freedom MIMO control strategy, referred to as the minimax predictive multivariable control (MPC), is shown to have worst-case-disturbance-rejection and robust-stability properties superior to those of purely H{sub 2}-optimal controllers, such as Generalized Predictive Controlmore » (GPC), for identical horizons. An attractive feature of the receding horizon structure of MPC is that it can, in ways similar to GPC, directly incorporate input constraints and pre-programmed reference inputs, which are nontrivial tasks in the standard H{infinity} design.« less

Globally linearized control on diabatic continuous stirred tank reactor: a case study.

PubMed

Jana, Amiya Kumar; Samanta, Amar Nath; Ganguly, Saibal

2005-07-01

This paper focuses on the promise of globally linearized control (GLC) structure in the realm of strongly nonlinear reactor system control. The proposed nonlinear control strategy is comprised of: (i) an input-output linearizing state feedback law (transformer), (ii) a state observer, and (iii) an external linear controller. The synthesis of discrete-time GLC controller for single-input single-output diabatic continuous stirred tank reactor (DCSTR) has been studied first, followed by the synthesis of feedforward/feedback controller for the same reactor having dead time in process as well as in disturbance. Subsequently, the multivariable GLC structure has been designed and then applied on multi-input multi-output DCSTR system. The simulation study shows high quality performance of the derived nonlinear controllers. The better-performed GLC in conjunction with reduced-order observer has been compared with the conventional proportional integral controller on the example reactor and superior performance has been achieved by the proposed GLC control scheme.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

PubMed

Bastian, Nathaniel D; Ekin, Tahir; Kang, Hyojung; Griffin, Paul M; Fulton, Lawrence V; Grannan, Benjamin C

2017-06-01

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Multi-level tree analysis of pulmonary artery/vein trees in non-contrast CT images

NASA Astrophysics Data System (ADS)

Gao, Zhiyun; Grout, Randall W.; Hoffman, Eric A.; Saha, Punam K.

2012-02-01

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

Revised Multi-Node Well (MNW2) Package for MODFLOW Ground-Water Flow Model

USGS Publications Warehouse

Konikow, Leonard F.; Hornberger, George Z.; Halford, Keith J.; Hanson, Randall T.; Harbaugh, Arlen W.

2009-01-01

Wells that are open to multiple aquifers can provide preferential pathways to flow and solute transport that short-circuit normal fluid flowlines. Representing these features in a regional flow model can produce a more realistic and reliable simulation model. This report describes modifications to the Multi-Node Well (MNW) Package of the U.S. Geological Survey (USGS) three-dimensional ground-water flow model (MODFLOW). The modifications build on a previous version and add several new features, processes, and input and output options. The input structure of the revised MNW (MNW2) is more well-centered than the original verion of MNW (MNW1) and allows the user to easily define hydraulic characteristics of each multi-node well. MNW2 also allows calculations of additional head changes due to partial penetration effects, flow into a borehole through a seepage face, changes in well discharge related to changes in lift for a given pump, and intraborehole flows with a pump intake located at any specified depth within the well. MNW2 also offers an improved capability to simulate nonvertical wells. A new output option allows selected multi-node wells to be designated as 'observation wells' for which changes in selected variables with time will be written to separate output files to facilitate postprocessing. MNW2 is compatible with the MODFLOW-2000 and MODFLOW-2005 versions of MODFLOW and with the version of MODFLOW that includes the Ground-Water Transport process (MODFLOW-GWT).

Novel application of simultaneous multi-image display during complex robotic abdominal procedures

PubMed Central

2014-01-01

Background The surgical robot offers the potential to integrate multiple views into the surgical console screen, and for the assistant’s monitors to provide real-time views of both fields of operation. This function has the potential to increase patient safety and surgical efficiency during an operation. Herein, we present a novel application of the multi-image display system for simultaneous visualization of endoscopic views during various complex robotic gastrointestinal operations. All operations were performed using the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) with the assistance of Tilepro, multi-input display software, during employment of the intraoperative scopes. Three robotic operations, left hepatectomy with intraoperative common bile duct exploration, low anterior resection, and radical distal subtotal gastrectomy with intracorporeal gastrojejunostomy, were performed by three different surgeons at a tertiary academic medical center. Results The three complex robotic abdominal operations were successfully completed without difficulty or intraoperative complications. The use of the Tilepro to simultaneously visualize the images from the colonoscope, gastroscope, and choledochoscope made it possible to perform additional intraoperative endoscopic procedures without extra monitors or interference with the operations. Conclusion We present a novel use of the multi-input display program on the da Vinci Surgical System to facilitate the performance of intraoperative endoscopies during complex robotic operations. Our study offers another potentially beneficial application of the robotic surgery platform toward integration and simplification of combining additional procedures with complex minimally invasive operations. PMID:24628761

Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.

PubMed

Shen, Ying; Colloc, Joël; Jacquet-Andrieu, Armelle; Lei, Kai

2015-08-01

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts. Copyright © 2015 Elsevier Inc. All rights reserved.

The effect of uncertainties in distance-based ranking methods for multi-criteria decision making

NASA Astrophysics Data System (ADS)

Jaini, Nor I.; Utyuzhnikov, Sergei V.

2017-08-01

Data in the multi-criteria decision making are often imprecise and changeable. Therefore, it is important to carry out sensitivity analysis test for the multi-criteria decision making problem. The paper aims to present a sensitivity analysis for some ranking techniques based on the distance measures in multi-criteria decision making. Two types of uncertainties are considered for the sensitivity analysis test. The first uncertainty is related to the input data, while the second uncertainty is towards the Decision Maker preferences (weights). The ranking techniques considered in this study are TOPSIS, the relative distance and trade-off ranking methods. TOPSIS and the relative distance method measure a distance from an alternative to the ideal and antiideal solutions. In turn, the trade-off ranking calculates a distance of an alternative to the extreme solutions and other alternatives. Several test cases are considered to study the performance of each ranking technique in both types of uncertainties.

MURI: Impact of Oceanographic Variability on Acoustic Communications

DTIC Science & Technology

2011-09-01

multiplexing ( OFDM ), multiple- input/multiple-output ( MIMO ) transmissions, and multi-user single-input/multiple-output (SIMO) communications. Lastly... MIMO - OFDM communications: Receiver design for Doppler distorted underwater acoustic channels,” Proc. Asilomar Conf. on Signals, Systems, and... MIMO ) will be of particular interest. Validating experimental data will be obtained during the ONR acoustic communications experiment in summer 2008

Simulation requirements for the Large Deployable Reflector (LDR)

NASA Technical Reports Server (NTRS)

Soosaar, K.

1984-01-01

Simulation tools for the large deployable reflector (LDR) are discussed. These tools are often the transfer function variety equations. However, transfer functions are inadequate to represent time-varying systems for multiple control systems with overlapping bandwidths characterized by multi-input, multi-output features. Frequency domain approaches are the useful design tools, but a full-up simulation is needed. Because of the need for a dedicated computer for high frequency multi degree of freedom components encountered, non-real time smulation is preferred. Large numerical analysis software programs are useful only to receive inputs and provide output to the next block, and should be kept out of the direct loop of simulation. The following blocks make up the simulation. The thermal model block is a classical heat transfer program. It is a non-steady state program. The quasistatic block deals with problems associated with rigid body control of reflector segments. The steady state block assembles data into equations of motion and dynamics. A differential raytrace is obtained to establish a change in wave aberrations. The observation scene is described. The focal plane module converts the photon intensity impinging on it into electron streams or into permanent film records.

Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Luy, N. T.

2018-04-01

The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

A Multi-Finger Interface with MR Actuators for Haptic Applications.

PubMed

Qin, Huanhuan; Song, Aiguo; Gao, Zhan; Liu, Yuqing; Jiang, Guohua

2018-01-01

Haptic devices with multi-finger input are highly desirable in providing realistic and natural feelings when interacting with the remote or virtual environment. Compared with the conventional actuators, MR (Magneto-rheological) actuators are preferable options in haptics because of larger passive torque and torque-volume ratios. Among the existing haptic MR actuators, most of them are still bulky and heavy. If they were smaller and lighter, they would become more suitable for haptics. In this paper, a small-scale yet powerful MR actuator was designed to build a multi-finger interface for the 6 DOF haptic device. The compact structure was achieved by adopting the multi-disc configuration. Based on this configuration, the MR actuator can generate the maximum torque of 480 N.mm with dimensions of only 36 mm diameter and 18 mm height. Performance evaluation showed that it can exhibit a relatively high dynamic range and good response characteristics when compared with some other haptic MR actuators. The multi-finger interface is equipped with three MR actuators and can provide up to 8 N passive force to the thumb, index and middle fingers, respectively. An application example was used to demonstrate the effectiveness and potential of this new MR actuator based interface.

Distributed event-triggered consensus strategy for multi-agent systems under limited resources

NASA Astrophysics Data System (ADS)

Noorbakhsh, S. Mohammad; Ghaisari, Jafar

2016-01-01

The paper proposes a distributed structure to address an event-triggered consensus problem for multi-agent systems which aims at concurrent reduction in inter-agent communication, control input actuation and energy consumption. Following the proposed approach, asymptotic convergence of all agents to consensus requires that each agent broadcasts its sampled-state to the neighbours and updates its control input only at its own triggering instants, unlike the existing related works. Obviously, it decreases the network bandwidth usage, sensor energy consumption, computation resources usage and actuator wears. As a result, it facilitates the implementation of the proposed consensus protocol in the real-world applications with limited resources. The stability of the closed-loop system under an event-based protocol is proved analytically. Some numerical results are presented which confirm the analytical discussion on the effectiveness of the proposed design.

Energetics of the multi-phase fluid flow in a narrow kerf in laser cutting conditions

NASA Astrophysics Data System (ADS)

Golyshev, A. A.; Orishich, A. M.; Shulyatyev, V. B.

2016-10-01

The energy balance of the multi-phase medium flow is studied experimentally under the laser cutting. Experimental data are generalized due to the condition of minimal roughness of the created surface used as a quality criterion of the melt flow, and also due to the application of dimensionless parameters: Peclet number and dimensionless absorbed laser power. For the first time ever it is found that, regardless the assistant gas (oxygen or nitrogen), laser type (the fiber one with the wavelength of 1.07 µm or CO2-laser with the wavelength of 10.6 µm), the minimal roughness is provided at a certain energy input in a melt unit, about 26 J/mm3. With oxygen, 50% of this input is provided by the radiation, the other 50% - by the exothermic reaction of iron oxidation.

Optimal modified tracking performance for MIMO networked control systems with communication constraints.

PubMed

Wu, Jie; Zhou, Zhu-Jun; Zhan, Xi-Sheng; Yan, Huai-Cheng; Ge, Ming-Feng

2017-05-01

This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

NASA Astrophysics Data System (ADS)

Ahmadian, Narjes; Khosravi, Alireza; Sarhadi, Pouria

2017-08-01

In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials.

PubMed

Hristozov, Danail; Zabeo, Alex; Alstrup Jensen, Keld; Gottardo, Stefania; Isigonis, Panagiotis; Maccalman, Laura; Critto, Andrea; Marcomini, Antonio

2016-11-01

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Multi-ray medical ultrasound simulation without explicit speckle modelling.

PubMed

Tuzer, Mert; Yazıcı, Abdulkadir; Türkay, Rüştü; Boyman, Michael; Acar, Burak

2018-05-04

To develop a medical ultrasound (US) simulation method using T1-weighted magnetic resonance images (MRI) as the input that offers a compromise between low-cost ray-based and high-cost realistic wave-based simulations. The proposed method uses a novel multi-ray image formation approach with a virtual phased array transducer probe. A domain model is built from input MR images. Multiple virtual acoustic rays are emerged from each element of the linear transducer array. Reflected and transmitted acoustic energy at discrete points along each ray is computed independently. Simulated US images are computed by fusion of the reflected energy along multiple rays from multiple transducers, while phase delays due to differences in distances to transducers are taken into account. A preliminary implementation using GPUs is presented. Preliminary results show that the multi-ray approach is capable of generating view point-dependent realistic US images with an inherent Rician distributed speckle pattern automatically. The proposed simulator can reproduce the shadowing artefacts and demonstrates frequency dependence apt for practical training purposes. We also have presented preliminary results towards the utilization of the method for real-time simulations. The proposed method offers a low-cost near-real-time wave-like simulation of realistic US images from input MR data. It can further be improved to cover the pathological findings using an improved domain model, without any algorithmic updates. Such a domain model would require lesion segmentation or manual embedding of virtual pathologies for training purposes.

HCP: A Flexible CNN Framework for Multi-label Image Classification.

PubMed

Wei, Yunchao; Xia, Wei; Lin, Min; Huang, Junshi; Ni, Bingbing; Dong, Jian; Zhao, Yao; Yan, Shuicheng

2015-10-26

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [44] based on hand-crafted features on the VOC 2012 dataset.

Design and multi-physics optimization of rotary MRF brakes

NASA Astrophysics Data System (ADS)

Topcu, Okan; Taşcıoğlu, Yiğit; Konukseven, Erhan İlhan

2018-03-01

Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors' knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs.

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

A boundary PDE feedback control approach for the stabilization of mortgage price dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Sarno, D.

2017-11-01

Several transactions taking place in financial markets are dependent on the pricing of mortgages (loans for the purchase of residences, land or farms). In this article, a method for stabilization of mortgage price dynamics is developed. It is considered that mortgage prices follow a PDE model which is equivalent to a multi-asset Black-Scholes PDE. Actually it is a diffusion process evolving in a 2D assets space, where the first asset is the house price and the second asset is the interest rate. By applying semi-discretization and a finite differences scheme this multi-asset PDE is transformed into a state-space model consisting of ordinary nonlinear differential equations. For the local subsystems, into which the mortgage PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the mortgage price PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem's dynamics and can eliminate the subsystem's tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the multi-factor mortgage price PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the mortgage price PDE system so as to assure that all its state variables will converge to the desirable setpoints. By showing the feasibility of such a control method it is also proven that through selected modification of the PDE boundary conditions the price of the mortgage can be made to converge and stabilize at specific reference values.

A biomimetic colorimetric logic gate system based on multi-functional peptide-mediated gold nanoparticle assembly

NASA Astrophysics Data System (ADS)

Li, Yong; Li, Wang; He, Kai-Yu; Li, Pei; Huang, Yan; Nie, Zhou; Yao, Shou-Zhuo

2016-04-01

In natural biological systems, proteins exploit various functional peptide motifs to exert target response and activity switch, providing a functional and logic basis for complex cellular activities. Building biomimetic peptide-based bio-logic systems is highly intriguing but remains relatively unexplored due to limited logic recognition elements and complex signal outputs. In this proof-of-principle work, we attempted to address these problems by utilizing multi-functional peptide probes and the peptide-mediated nanoparticle assembly system. Here, the rationally designed peptide probes function as the dual-target responsive element specifically responsive to metal ions and enzymes as well as the mediator regulating the assembly of gold nanoparticles (AuNPs). Taking advantage of Zn2+ ions and chymotrypsin as the model inputs of metal ions and enzymes, respectively, we constructed the peptide logic system computed by the multi-functional peptide probes and outputted by the readable colour change of AuNPs. In this way, the representative binary basic logic gates (AND, OR, INHIBIT, NAND, IMPLICATION) have been achieved by delicately coding the peptide sequence, demonstrating the versatility of our logic system. Additionally, we demonstrated that the three-input combinational logic gate (INHIBIT-OR) could also be successfully integrated and applied as a multi-tasking biosensor for colorimetric detection of dual targets. This nanoparticle-based peptide logic system presents a valid strategy to illustrate peptide information processing and provides a practical platform for executing peptide computing or peptide-related multiplexing sensing, implying that the controllable nanomaterial assembly is a promising and potent methodology for the advancement of biomimetic bio-logic computation.In natural biological systems, proteins exploit various functional peptide motifs to exert target response and activity switch, providing a functional and logic basis for complex cellular activities. Building biomimetic peptide-based bio-logic systems is highly intriguing but remains relatively unexplored due to limited logic recognition elements and complex signal outputs. In this proof-of-principle work, we attempted to address these problems by utilizing multi-functional peptide probes and the peptide-mediated nanoparticle assembly system. Here, the rationally designed peptide probes function as the dual-target responsive element specifically responsive to metal ions and enzymes as well as the mediator regulating the assembly of gold nanoparticles (AuNPs). Taking advantage of Zn2+ ions and chymotrypsin as the model inputs of metal ions and enzymes, respectively, we constructed the peptide logic system computed by the multi-functional peptide probes and outputted by the readable colour change of AuNPs. In this way, the representative binary basic logic gates (AND, OR, INHIBIT, NAND, IMPLICATION) have been achieved by delicately coding the peptide sequence, demonstrating the versatility of our logic system. Additionally, we demonstrated that the three-input combinational logic gate (INHIBIT-OR) could also be successfully integrated and applied as a multi-tasking biosensor for colorimetric detection of dual targets. This nanoparticle-based peptide logic system presents a valid strategy to illustrate peptide information processing and provides a practical platform for executing peptide computing or peptide-related multiplexing sensing, implying that the controllable nanomaterial assembly is a promising and potent methodology for the advancement of biomimetic bio-logic computation. Electronic supplementary information (ESI) available: Additional figures (Tables S1-S3 and Fig. S1-S6). See DOI: 10.1039/c6nr01072e

Multi-mode horn

NASA Technical Reports Server (NTRS)

Neilson, Jeffrey M. (Inventor)

2002-01-01

A horn has an input aperture and an output aperture, and comprises a conductive inner surface formed by rotating a curve about a central axis. The curve comprises a first arc having an input aperture end and a transition end, and a second arc having a transition end and an output aperture end. When rotated about the central axis, the first arc input aperture end forms an input aperture, and the second arc output aperture end forms an output aperture. The curve is then optimized to provide a mode conversion which maximizes the power transfer of input energy to the Gaussian mode at the output aperture.

THE SPITZER SURVEY OF STELLAR STRUCTURE IN GALAXIES (S{sup 4}G): MULTI-COMPONENT DECOMPOSITION STRATEGIES AND DATA RELEASE

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

Salo, Heikki; Laurikainen, Eija; Laine, Jarkko

The Spitzer Survey of Stellar Structure in Galaxies (S{sup 4}G) is a deep 3.6 and 4.5 μm imaging survey of 2352 nearby (<40 Mpc) galaxies. We describe the S{sup 4}G data analysis pipeline 4, which is dedicated to two-dimensional structural surface brightness decompositions of 3.6 μm images, using GALFIT3.0. Besides automatic 1-component Sérsic fits, and 2-component Sérsic bulge + exponential disk fits, we present human-supervised multi-component decompositions, which include, when judged appropriate, a central point source, bulge, disk, and bar components. Comparison of the fitted parameters indicates that multi-component models are needed to obtain reliable estimates for the bulge Sérsicmore » index and bulge-to-total light ratio (B/T), confirming earlier results. Here, we describe the preparations of input data done for decompositions, give examples of our decomposition strategy, and describe the data products released via IRSA and via our web page (www.oulu.fi/astronomy/S4G-PIPELINE4/MAIN). These products include all the input data and decomposition files in electronic form, making it easy to extend the decompositions to suit specific science purposes. We also provide our IDL-based visualization tools (GALFIDL) developed for displaying/running GALFIT-decompositions, as well as our mask editing procedure (MASK-EDIT) used in data preparation. A detailed analysis of the bulge, disk, and bar parameters derived from multi-component decompositions will be published separately.« less

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

A biomimetic colorimetric logic gate system based on multi-functional peptide-mediated gold nanoparticle assembly.

PubMed

Li, Yong; Li, Wang; He, Kai-Yu; Li, Pei; Huang, Yan; Nie, Zhou; Yao, Shou-Zhuo

2016-04-28

In natural biological systems, proteins exploit various functional peptide motifs to exert target response and activity switch, providing a functional and logic basis for complex cellular activities. Building biomimetic peptide-based bio-logic systems is highly intriguing but remains relatively unexplored due to limited logic recognition elements and complex signal outputs. In this proof-of-principle work, we attempted to address these problems by utilizing multi-functional peptide probes and the peptide-mediated nanoparticle assembly system. Here, the rationally designed peptide probes function as the dual-target responsive element specifically responsive to metal ions and enzymes as well as the mediator regulating the assembly of gold nanoparticles (AuNPs). Taking advantage of Zn2+ ions and chymotrypsin as the model inputs of metal ions and enzymes, respectively, we constructed the peptide logic system computed by the multi-functional peptide probes and outputted by the readable colour change of AuNPs. In this way, the representative binary basic logic gates (AND, OR, INHIBIT, NAND, IMPLICATION) have been achieved by delicately coding the peptide sequence, demonstrating the versatility of our logic system. Additionally, we demonstrated that the three-input combinational logic gate (INHIBIT-OR) could also be successfully integrated and applied as a multi-tasking biosensor for colorimetric detection of dual targets. This nanoparticle-based peptide logic system presents a valid strategy to illustrate peptide information processing and provides a practical platform for executing peptide computing or peptide-related multiplexing sensing, implying that the controllable nanomaterial assembly is a promising and potent methodology for the advancement of biomimetic bio-logic computation.

Multi-element stochastic spectral projection for high quantile estimation

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

Ko, Jordan, E-mail: jordan.ko@mac.com; Garnier, Josselin

2013-06-15

We investigate quantile estimation by multi-element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model’s exact response. The gPC metamodel is constructed by the non-intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as essentially free. Thus, large number of Monte Carlo samples from the metamodel can be used to estimate α-quantile, for moderate values of α. As the gPC metamodel is an expansion about the means of the inputs, its accuracy may worsen away from these mean values where themore » extreme events may occur. By increasing the approximation accuracy of the metamodel, we may eventually improve accuracy of quantile estimation but it is very expensive. A multi-element approach is therefore proposed by combining a global metamodel in the standard normal space with supplementary local metamodels constructed in bounded domains about the design points corresponding to the extreme events. To improve the accuracy and to minimize the sampling cost, sparse-tensor and anisotropic-tensor quadratures are tested in addition to the full-tensor Gauss quadrature in the construction of local metamodels; different bounds of the gPC expansion are also examined. The global and local metamodels are combined in the multi-element gPC (MEgPC) approach and it is shown that MEgPC can be more accurate than Monte Carlo or importance sampling methods for high quantile estimations for input dimensions roughly below N=8, a limit that is very much case- and α-dependent.« less

Robust set-point regulation for ecological models with multiple management goals.

PubMed

Guiver, Chris; Mueller, Markus; Hodgson, Dave; Townley, Stuart

2016-05-01

Population managers will often have to deal with problems of meeting multiple goals, for example, keeping at specific levels both the total population and population abundances in given stage-classes of a stratified population. In control engineering, such set-point regulation problems are commonly tackled using multi-input, multi-output proportional and integral (PI) feedback controllers. Building on our recent results for population management with single goals, we develop a PI control approach in a context of multi-objective population management. We show that robust set-point regulation is achieved by using a modified PI controller with saturation and anti-windup elements, both described in the paper, and illustrate the theory with examples. Our results apply more generally to linear control systems with positive state variables, including a class of infinite-dimensional systems, and thus have broader appeal.

A multi-threshold sampling method for TOF-PET signal processing

NASA Astrophysics Data System (ADS)

Kim, H.; Kao, C. M.; Xie, Q.; Chen, C. T.; Zhou, L.; Tang, F.; Frisch, H.; Moses, W. W.; Choong, W. S.

2009-04-01

As an approach to realizing all-digital data acquisition for positron emission tomography (PET), we have previously proposed and studied a multi-threshold sampling method to generate samples of a PET event waveform with respect to a few user-defined amplitudes. In this sampling scheme, one can extract both the energy and timing information for an event. In this paper, we report our prototype implementation of this sampling method and the performance results obtained with this prototype. The prototype consists of two multi-threshold discriminator boards and a time-to-digital converter (TDC) board. Each of the multi-threshold discriminator boards takes one input and provides up to eight threshold levels, which can be defined by users, for sampling the input signal. The TDC board employs the CERN HPTDC chip that determines the digitized times of the leading and falling edges of the discriminator output pulses. We connect our prototype electronics to the outputs of two Hamamatsu R9800 photomultiplier tubes (PMTs) that are individually coupled to a 6.25×6.25×25 mm3 LSO crystal. By analyzing waveform samples generated by using four thresholds, we obtain a coincidence timing resolution of about 340 ps and an ˜18% energy resolution at 511 keV. We are also able to estimate the decay-time constant from the resulting samples and obtain a mean value of 44 ns with an ˜9 ns FWHM. In comparison, using digitized waveforms obtained at a 20 GSps sampling rate for the same LSO/PMT modules we obtain ˜300 ps coincidence timing resolution, ˜14% energy resolution at 511 keV, and ˜5 ns FWHM for the estimated decay-time constant. Details of the results on the timing and energy resolutions by using the multi-threshold method indicate that it is a promising approach for implementing digital PET data acquisition.

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

PubMed

van der Lee, J H; Svrcek, W Y; Young, B R

2008-01-01

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.

Using Arden Syntax for the creation of a multi-patient surveillance dashboard.

PubMed

Kraus, Stefan; Drescher, Caroline; Sedlmayr, Martin; Castellanos, Ixchel; Prokosch, Hans-Ulrich; Toddenroth, Dennis

2015-10-09

Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not natively support patient overviews from customizable CDS routines, but local physicians indicated a demand for multi-patient tabular overviews of important clinical parameters such as key laboratory measurements. As our PDMS installation provides Arden Syntax support, we set out to explore the capability of Arden Syntax for multi-patient CDS by implementing a prototypical dashboard for visualizing laboratory findings from patient sets. Our implementation leveraged the object data type, supported by later versions of Arden, which turned out to be serviceable for representing complex input data from several patients. For our prototype, we designed a modularized architecture that separates the definition of technical operations, in particular the control of the patient context, from the actual clinical knowledge. Individual Medical Logic Modules (MLMs) for processing single patient attributes could then be developed according to well-tried Arden Syntax conventions. We successfully implemented a working dashboard prototype entirely in Arden Syntax. The architecture consists of a controller MLM to handle the patient context, a presenter MLM to generate a dashboard view, and a set of traditional MLMs containing the clinical decision logic. Our prototype could be integrated into the graphical user interface of the local PDMS. We observed that with realistic input data the average execution time of about 200ms for generating dashboard views attained applicable performance. Our study demonstrated the general feasibility of creating multi-patient CDS routines in Arden Syntax. We believe that our prototypical dashboard also suggests that such implementations can be relatively easy, and may simultaneously hold promise for sharing dashboards between institutions and reusing elementary components for additional dashboards. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement

PubMed Central

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-01-01

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro–meso–scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy–enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy–enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates. PMID:28869520

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement.

PubMed

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-09-03

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro-meso-scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy-enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy-enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates.

Diabetes Bingo: Research Prioritization with the Filipino Community

PubMed Central

Oculto, Tessie; Ramones, Emilyn; Caagbay, Cedric R

2010-01-01

This community-based participatory research, conducted in partnership between a European-American academic researcher and a professional group of Filipino nurses, aimed to determine the diabetes research priority for the Filipino community on the island of O‘ahu in Hawai‘i, and to evaluate the multi-voting technique to seek input from the community. The study design was a qualitative, cross-sectional interactive process consisting of an educational presentation followed by data collection from the audience. Ten community presentations about the impact of diabetes on the Filipino community were conducted by a Filipino nurse with participants (N = 265). Following the educational session, the participants selected priorities for research using a multi-vote technique developed as a Diabetes Bingo card. Community voting results identified prevention and a focus on adults as important priorities for research. Based on the results of the multi-voting, the research partners were able to come to consensus on a research priority area of prevention of type 2 diabetes in adults. Multi-voting using a Diabetes Bingo card, preceded by an educational presentation by a Filipino nurse, was a culturally competent community-based participatory research method that gave voice to the participants and direction to the research partners for future projects. The multi-voting technique was readily accepted and enjoyed by participants. PMID:21229487

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Digital multi-channel stabilization of four-mode phase-sensitive parametric multicasting.

PubMed

Liu, Lan; Tong, Zhi; Wiberg, Andreas O J; Kuo, Bill P P; Myslivets, Evgeny; Alic, Nikola; Radic, Stojan

2014-07-28

Stable four-mode phase-sensitive (4MPS) process was investigated as a means to enhance two-pump driven parametric multicasting conversion efficiency (CE) and signal to noise ratio (SNR). Instability of multi-beam, phase sensitive (PS) device that inherently behaves as an interferometer, with output subject to ambient induced fluctuations, was addressed theoretically and experimentally. A new stabilization technique that controls phases of three input waves of the 4MPS multicaster and maximizes CE was developed and described. Stabilization relies on digital phase-locked loop (DPLL) specifically was developed to control pump phases to guarantee stable 4MPS operation that is independent of environmental fluctuations. The technique also controls a single (signal) input phase to optimize the PS-induced improvement of the CE and SNR. The new, continuous-operation DPLL has allowed for fully stabilized PS parametric broadband multicasting, demonstrating CE improvement over 20 signal copies in excess of 10 dB.

Improving orientation in head injured adults by repeated practice, multi-sensory input and peer participation.

PubMed

Zencius, A H; Wesolowski, M D; Rodriguez, I M

1998-01-01

The efficacy of using antecedent control procedures (practice, multi-sensory input and peer participation) in facilitating orientation to person, place and time with two survivors of traumatic brain injuries were tested in two studies. In the first investigation, a 23-year-old male was treated by presenting the orientation questions orally while being shown questions on written flashcards. Results suggest that correct responses to orientation questions only occurred when flashcards were coupled with oral questioning. The participant responded correctly to nearly 100% of all orientation questions within 2 weeks of initiating flashcards. In the second study, a 19-year-old male was asked to respond in writing to 20 orientation questions in a small group. The group had a leader and 4 TBI patients. Following this, group members who correctly answered the orientation questions, took turns reading orientation questions and providing the correct responses.

Advanced information processing system: Local system services

NASA Technical Reports Server (NTRS)

Burkhardt, Laura; Alger, Linda; Whittredge, Roy; Stasiowski, Peter

1989-01-01

The Advanced Information Processing System (AIPS) is a multi-computer architecture composed of hardware and software building blocks that can be configured to meet a broad range of application requirements. The hardware building blocks are fault-tolerant, general-purpose computers, fault-and damage-tolerant networks (both computer and input/output), and interfaces between the networks and the computers. The software building blocks are the major software functions: local system services, input/output, system services, inter-computer system services, and the system manager. The foundation of the local system services is an operating system with the functions required for a traditional real-time multi-tasking computer, such as task scheduling, inter-task communication, memory management, interrupt handling, and time maintenance. Resting on this foundation are the redundancy management functions necessary in a redundant computer and the status reporting functions required for an operator interface. The functional requirements, functional design and detailed specifications for all the local system services are documented.

Tracking control of WMRs on loose soil based on mixed H2/H∞ control with longitudinal slip ratio estimation

NASA Astrophysics Data System (ADS)

Gao, Haibo; Chen, Chao; Ding, Liang; Li, Weihua; Yu, Haitao; Xia, Kerui; Liu, Zhen

2017-11-01

Wheeled mobile robots (WMRs) often suffer from the longitudinal slipping when moving on the loose soil of the surface of the moon during exploration. Longitudinal slip is the main cause of WMRs' delay in trajectory tracking. In this paper, a nonlinear extended state observer (NESO) is introduced to estimate the longitudinal velocity in order to estimate the slip ratio and the derivative of the loss of velocity which are used in modelled disturbance compensation. Owing to the uncertainty and disturbance caused by estimation errors, a multi-objective controller using the mixed H2/H∞ method is employed to ensure the robust stability and performance of the WMR system. The final inputs of the trajectory tracking consist of the feedforward compensation, compensation for the modelled disturbances and designed multi-objective control inputs. Finally, the simulation results demonstrate the effectiveness of the controller, which exhibits a satisfactory tracking performance.

Identification and control of a multizone crystal growth furnace

NASA Technical Reports Server (NTRS)

Batur, C.; Sharpless, R. B.; Duval, W. M. B.; Rosenthal, B. N.; Singh, N. B.

1992-01-01

This paper presents an intelligent adaptive control system for the control of a solid-liquid interface of a crystal while it is growing via directional solidification inside a multizone transparent furnace. The task of the process controller is to establish a user-specified axial temperature profile and to maintain a desirable interface shape. Both single-input-single-output and multi-input-multi-output adaptive pole placement algorithms have been used to control the temperature. Also described is an intelligent measurement system to assess the shape of the crystal while it is growing. A color video imaging system observes the crystal in real time and determines the position and the shape of the interface. This information is used to evaluate the crystal growth rate, and to analyze the effects of translational velocity and temperature profiles on the shape of the interface. Creation of this knowledge base is the first step to incorporate image processing into furnace control.

Model reduction of nonsquare linear MIMO systems using multipoint matrix continued-fraction expansions

NASA Technical Reports Server (NTRS)

Guo, Tong-Yi; Hwang, Chyi; Shieh, Leang-San

1994-01-01

This paper deals with the multipoint Cauer matrix continued-fraction expansion (MCFE) for model reduction of linear multi-input multi-output (MIMO) systems with various numbers of inputs and outputs. A salient feature of the proposed MCFE approach to model reduction of MIMO systems with square transfer matrices is its equivalence to the matrix Pade approximation approach. The Cauer second form of the ordinary MCFE for a square transfer function matrix is generalized in this paper to a multipoint and nonsquare-matrix version. An interesting connection of the multipoint Cauer MCFE method to the multipoint matrix Pade approximation method is established. Also, algorithms for obtaining the reduced-degree matrix-fraction descriptions and reduced-dimensional state-space models from a transfer function matrix via the multipoint Cauer MCFE algorithm are presented. Practical advantages of using the multipoint Cauer MCFE are discussed and a numerical example is provided to illustrate the algorithms.

Removing flicker based on sparse color correspondences in old film restoration

NASA Astrophysics Data System (ADS)

Huang, Xi; Ding, Youdong; Yu, Bing; Xia, Tianran

2018-04-01

In the long history of human civilization, archived film is an indispensable part of it, and using digital method to repair damaged film is also a mainstream trend nowadays. In this paper, we propose a sparse color correspondences based technique to remove fading flicker for old films. Our model, combined with multi frame images to establish a simple correction model, includes three key steps. Firstly, we recover sparse color correspondences in the input frames to build a matrix with many missing entries. Secondly, we present a low-rank matrix factorization approach to estimate the unknown parameters of this model. Finally, we adopt a two-step strategy that divide the estimated parameters into reference frame parameters for color recovery correction and other frame parameters for color consistency correction to remove flicker. Our method combined multi-frames takes continuity of the input sequence into account, and the experimental results show the method can remove fading flicker efficiently.

Logarithmic profile mapping multi-scale Retinex for restoration of low illumination images

NASA Astrophysics Data System (ADS)

Shi, Haiyan; Kwok, Ngaiming; Wu, Hongkun; Li, Ruowei; Liu, Shilong; Lin, Ching-Feng; Wong, Chin Yeow

2018-04-01

Images are valuable information sources for many scientific and engineering applications. However, images captured in poor illumination conditions would have a large portion of dark regions that could heavily degrade the image quality. In order to improve the quality of such images, a restoration algorithm is developed here that transforms the low input brightness to a higher value using a modified Multi-Scale Retinex approach. The algorithm is further improved by a entropy based weighting with the input and the processed results to refine the necessary amplification at regions of low brightness. Moreover, fine details in the image are preserved by applying the Retinex principles to extract and then re-insert object edges to obtain an enhanced image. Results from experiments using low and normal illumination images have shown satisfactory performances with regard to the improvement in information contents and the mitigation of viewing artifacts.

Multi-function all optical packet switch by periodic wavelength arrangement in an arrayed waveguide grating and wideband optical filters.

PubMed

Feng, Kai-Ming; Wu, Chung-Yu; Wen, Yu-Hsiang

2012-01-16

By utilizing the cyclic filtering function of an NxN arrayed waveguide grating (AWG), we propose and experimentally demonstrate a novel multi-function all optical packet switching (OPS) architecture by applying a periodical wavelength arrangement between the AWG in the optical routing/buffering unit and a set of wideband optical filters in the switched output ports to achieve the desired routing and buffering functions. The proposed OPS employs only one tunable wavelength converter at the input port to convert the input wavelength to a designated wavelength which reduces the number of active optical components and thus the complexity of the traffic control is simplified in the OPS. With the proposed OPS architecture, multiple optical packet switching functions, including arbitrary packet switching and buffering, first-in-first-out (FIFO) packet multiplexing, packet demultiplexing and packet add/drop multiplexing, have been successfully demonstrated.

Effective techniques for the identification and accommodation of disturbances

NASA Technical Reports Server (NTRS)

Johnson, C. D.

1989-01-01

The successful control of dynamic systems such as space stations, or launch vehicles, requires a controller design methodology that acknowledges and addresses the disruptive effects caused by external and internal disturbances that inevitably act on such systems. These disturbances, technically defined as uncontrollable inputs, typically vary with time in an uncertain manner and usually cannot be directly measured in real time. A relatively new non-statistical technique for modeling, and (on-line) identification, of those complex uncertain disturbances that are not as erratic and capricious as random noise is described. This technique applies to multi-input cases and to many of the practical disturbances associated with the control of space stations, or launch vehicles. Then, a collection of smart controller design techniques that allow controlled dynamic systems, with possible multi-input controls, to accommodate (cope with) such disturbances with extraordinary effectiveness are associated. These new smart controllers are designed by non-statistical techniques and typically turn out to be unconventional forms of dynamic linear controllers (compensators) with constant coefficients. The simplicity and reliability of linear, constant coefficient controllers is well-known in the aerospace field.

Multi-output differential technologies

NASA Astrophysics Data System (ADS)

Bidare, Srinivas R.

1997-01-01

A differential is a very old and proven mechanical device that allows a single input to be split into two outputs having equal torque irrespective of the output speeds. A standard differential is capable of providing only two outputs from a single input. A recently patented multi-output differential technology known as `Plural-Output Differential' allows a single input to be split into many outputs. This new technology is the outcome of a systematic study of complex gear trains (Bidare 1992). The unique feature of a differential (equal torque at different speeds) can be applied to simplify the construction and operation of many complex mechanical devices that require equal torque's or forces at multiple outputs. It is now possible to design a mechanical hand with three or more fingers with equal torque. Since these finger are powered via a differential they are `mechanically intelligent'. A prototype device is operational and has been used to demonstrate the utility and flexibility of the design. In this paper we shall review two devices that utilize the new technology resulting in increased performance, robustness with reduced complexity and cost.

Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems

PubMed Central

Indiveri, Giacomo

2008-01-01

Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to pay attention to subsets of sensory inputs suppressing non salient ones. Selective attention strategies are extremely effective in both natural and artificial systems which have to cope with large amounts of input data and have limited computational resources. One of the main computational primitives used to perform these selection operations is the Winner-Take-All (WTA) network. These types of networks are formed by arrays of coupled computational nodes that selectively amplify the strongest input signals, and suppress the weaker ones. Neuromorphic circuits are an optimal medium for constructing WTA networks and for implementing efficient hardware models of selective attention systems. In this paper we present an overview of selective attention systems based on neuromorphic WTA circuits ranging from single-chip vision sensors for selecting and tracking the position of salient features, to multi-chip systems implement saliency-map based models of selective attention. PMID:27873818

Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems.

PubMed

Indiveri, Giacomo

2008-09-03

Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to pay attention to subsets of sensory inputs suppressing non salient ones. Selective attention strategies are extremely effective in both natural and artificial systems which have to cope with large amounts of input data and have limited computational resources. One of the main computational primitives used to perform these selection operations is the Winner-Take-All (WTA) network. These types of networks are formed by arrays of coupled computational nodes that selectively amplify the strongest input signals, and suppress the weaker ones. Neuromorphic circuits are an optimal medium for constructing WTA networks and for implementing efficient hardware models of selective attention systems. In this paper we present an overview of selective attention systems based on neuromorphic WTA circuits ranging from single-chip vision sensors for selecting and tracking the position of salient features, to multi-chip systems implement saliency-map based models of selective attention.

Multi-response optimization of process parameters for GTAW process in dissimilar welding of Incoloy 800HT and P91 steel by using grey relational analysis

NASA Astrophysics Data System (ADS)

vellaichamy, Lakshmanan; Paulraj, Sathiya

2018-02-01

The dissimilar welding of Incoloy 800HT and P91 steel using Gas Tungsten arc welding process (GTAW) This material is being used in the Nuclear Power Plant and Aerospace Industry based application because Incoloy 800HT possess good corrosion and oxidation resistance and P91 possess high temperature strength and creep resistance. This work discusses on multi-objective optimization using gray relational analysis (GRA) using 9CrMoV-N filler materials. The experiment conducted L9 orthogonal array. The input parameter are current, voltage, speed. The output response are Tensile strength, Hardness and Toughness. To optimize the input parameter and multiple output variable by using GRA. The optimal parameter is combination was determined as A2B1C1 so given input parameter welding current at 120 A, voltage at 16 V and welding speed at 0.94 mm/s. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics.

Characterising switching behaviour in perceptual multi-stability.

PubMed

Denham, Susan; Bendixen, Alexandra; Mill, Robert; Tóth, Dénes; Wennekers, Thomas; Coath, Martin; Bőhm, Tamás; Szalardy, Orsolya; Winkler, István

2012-09-15

When people experience an unchanging sensory input for a long period of time, their perception tends to switch stochastically and unavoidably between alternative interpretations of the sensation; a phenomenon known as perceptual bi-stability or multi-stability. The huge variability in the experimental data obtained in such paradigms makes it difficult to distinguish typical patterns of behaviour, or to identify differences between switching patterns. Here we propose a new approach to characterising switching behaviour based upon the extraction of transition matrices from the data, which provide a compact representation that is well-understood mathematically. On the basis of this representation we can characterise patterns of perceptual switching, visualise and simulate typical switching patterns, and calculate the likelihood of observing a particular switching pattern. The proposed method can support comparisons between different observers, experimental conditions and even experiments. We demonstrate the insights offered by this approach using examples from our experiments investigating multi-stability in auditory streaming. However, the methodology is generic and thus widely applicable in studies of multi-stability in any domain. Copyright © 2012 Elsevier B.V. All rights reserved.

An overlapped grid method for multigrid, finite volume/difference flow solvers: MaGGiE

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Lessard, Victor R.

1990-01-01

The objective is to develop a domain decomposition method via overlapping/embedding the component grids, which is to be used by upwind, multi-grid, finite volume solution algorithms. A computer code, given the name MaGGiE (Multi-Geometry Grid Embedder) is developed to meet this objective. MaGGiE takes independently generated component grids as input, and automatically constructs the composite mesh and interpolation data, which can be used by the finite volume solution methods with or without multigrid convergence acceleration. Six demonstrative examples showing various aspects of the overlap technique are presented and discussed. These cases are used for developing the procedure for overlapping grids of different topologies, and to evaluate the grid connection and interpolation data for finite volume calculations on a composite mesh. Time fluxes are transferred between mesh interfaces using a trilinear interpolation procedure. Conservation losses are minimal at the interfaces using this method. The multi-grid solution algorithm, using the coaser grid connections, improves the convergence time history as compared to the solution on composite mesh without multi-gridding.

Simulating multiprimary LCDs on standard tri-stimulus LC displays

NASA Astrophysics Data System (ADS)

Lebowsky, Fritz; Vonneilich, Katrin; Bonse, Thomas

2008-01-01

Large-scale, direct view TV screens, in particular those based on liquid crystal technology, are beginning to use subpixel structures with more than three subpixels to implement a multi-primary display with up to six primaries. Since their input color space is likely to remain tri-stimulus RGB we first focus on some fundamental constraints. Among them, we elaborate simplified gamut mapping architectures as well as color filter geometry, transparency, and chromaticity coordinates in color space. Based on a 'display centric' RGB color space tetrahedrization combined with linear interpolation we describe a simulation framework which enables optimization for up to 7 primaries. We evaluated the performance through mapping the multi-primary design back onto a RGB LC display gamut without building a prototype multi-primary display. As long as we kept the RGB equivalent output signal within the display gamut we could analyze all desirable multi-primary configurations with regard to colorimetric variance and visually perceived quality. Not only does our simulation tool enable us to verify a novel concept it also demonstrates how carefully one needs to design a multiprimary display for LCD TV applications.

Multi-scale symbolic transfer entropy analysis of EEG

NASA Astrophysics Data System (ADS)

Yao, Wenpo; Wang, Jun

2017-10-01

From both global and local perspectives, we symbolize two kinds of EEG and analyze their dynamic and asymmetrical information using multi-scale transfer entropy. Multi-scale process with scale factor from 1 to 199 and step size of 2 is applied to EEG of healthy people and epileptic patients, and then the permutation with embedding dimension of 3 and global approach are used to symbolize the sequences. The forward and reverse symbol sequences are taken as the inputs of transfer entropy. Scale factor intervals of permutation and global way are (37, 57) and (65, 85) where the two kinds of EEG have satisfied entropy distinctions. When scale factor is 67, transfer entropy of the healthy and epileptic subjects of permutation, 0.1137 and 0.1028, have biggest difference. And the corresponding values of the global symbolization is 0.0641 and 0.0601 which lies in the scale factor of 165. Research results show that permutation which takes contribution of local information has better distinction and is more effectively applied to our multi-scale transfer entropy analysis of EEG.

Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

2014-08-01

Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

Software Defined Radio with Parallelized Software Architecture

NASA Technical Reports Server (NTRS)

Heckler, Greg

2013-01-01

This software implements software-defined radio procession over multi-core, multi-CPU systems in a way that maximizes the use of CPU resources in the system. The software treats each processing step in either a communications or navigation modulator or demodulator system as an independent, threaded block. Each threaded block is defined with a programmable number of input or output buffers; these buffers are implemented using POSIX pipes. In addition, each threaded block is assigned a unique thread upon block installation. A modulator or demodulator system is built by assembly of the threaded blocks into a flow graph, which assembles the processing blocks to accomplish the desired signal processing. This software architecture allows the software to scale effortlessly between single CPU/single-core computers or multi-CPU/multi-core computers without recompilation. NASA spaceflight and ground communications systems currently rely exclusively on ASICs or FPGAs. This software allows low- and medium-bandwidth (100 bps to .50 Mbps) software defined radios to be designed and implemented solely in C/C++ software, while lowering development costs and facilitating reuse and extensibility.

Health condition identification of multi-stage planetary gearboxes using a mRVM-based method

NASA Astrophysics Data System (ADS)

Lei, Yaguo; Liu, Zongyao; Wu, Xionghui; Li, Naipeng; Chen, Wu; Lin, Jing

2015-08-01

Multi-stage planetary gearboxes are widely applied in aerospace, automotive and heavy industries. Their key components, such as gears and bearings, can easily suffer from damage due to tough working environment. Health condition identification of planetary gearboxes aims to prevent accidents and save costs. This paper proposes a method based on multiclass relevance vector machine (mRVM) to identify health condition of multi-stage planetary gearboxes. In this method, a mRVM algorithm is adopted as a classifier, and two features, i.e. accumulative amplitudes of carrier orders (AACO) and energy ratio based on difference spectra (ERDS), are used as the input of the classifier to classify different health conditions of multi-stage planetary gearboxes. To test the proposed method, seven health conditions of a two-stage planetary gearbox are considered and vibration data is acquired from the planetary gearbox under different motor speeds and loading conditions. The results of three tests based on different data show that the proposed method obtains an improved identification performance and robustness compared with the existing method.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

Enabling multi-faceted measures of success for protected area management in Trinidad and Tobago.

PubMed

Granderson, Ainka A

2011-08-01

A key challenge has been to define and measure "success" in managing protected areas. A case study was conducted of efforts to evaluate the new protected area management system in Trinidad and Tobago using a participatory approach. The aim of the case study was to (1) examine whether stakeholder involvement better captures the multi-faceted nature of success and (2) identify the role and influence of various stakeholder groups in this process. An holistic and systematic framework was developed with stakeholder input that facilitated the integration of expert and lay knowledge, a broad emphasis on ecological, socio-economic, and institutional aspects, and the use of both quantitative and qualitative data allowing the evaluation to capture the multi-faceted nature and impacts of protected area management. Input from primary stakeholders, such as local communities, was critical as they have a high stake in protected area outcomes. Secondary and external stakeholders, including government agencies, non-governmental organizations, academia and the private sector, were also important in providing valuable technical assistance and serving as mediators. However, a lack of consensus over priorities, politics, and limited stakeholder capacity and data access pose significant barriers to engaging stakeholders to effectively measure the management success of protected areas. Copyright © 2011 Elsevier Ltd. All rights reserved.

Robust Stability Analysis of the Space Launch System Control Design: A Singular Value Approach

NASA Technical Reports Server (NTRS)

Pei, Jing; Newsome, Jerry R.

2015-01-01

Classical stability analysis consists of breaking the feedback loops one at a time and determining separately how much gain or phase variations would destabilize the stable nominal feedback system. For typical launch vehicle control design, classical control techniques are generally employed. In addition to stability margins, frequency domain Monte Carlo methods are used to evaluate the robustness of the design. However, such techniques were developed for Single-Input-Single-Output (SISO) systems and do not take into consideration the off-diagonal terms in the transfer function matrix of Multi-Input-Multi-Output (MIMO) systems. Robust stability analysis techniques such as H(sub infinity) and mu are applicable to MIMO systems but have not been adopted as standard practices within the launch vehicle controls community. This paper took advantage of a simple singular-value-based MIMO stability margin evaluation method based on work done by Mukhopadhyay and Newsom and applied it to the SLS high-fidelity dynamics model. The method computes a simultaneous multi-loop gain and phase margin that could be related back to classical margins. The results presented in this paper suggest that for the SLS system, traditional SISO stability margins are similar to the MIMO margins. This additional level of verification provides confidence in the robustness of the control design.

BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data.

PubMed

Chae, Heejoon; Lee, Sangseon; Seo, Seokjun; Jung, Daekyoung; Chang, Hyeonsook; Nephew, Kenneth P; Kim, Sun

2016-12-01

Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much difficulty in investigating relationships among gene expression, DNA sequence variation, and DNA methylation using multi-omics data. In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.kr/software/biovlab_mcpg_snp_express/. Copyright © 2016 Elsevier Inc. All rights reserved.

R-parametrization and its role in classification of linear multivariable feedback systems

NASA Technical Reports Server (NTRS)

Chen, Robert T. N.

1988-01-01

A classification of all the compensators that stabilize a given general plant in a linear, time-invariant multi-input, multi-output feedback system is developed. This classification, along with the associated necessary and sufficient conditions for stability of the feedback system, is achieved through the introduction of a new parameterization, referred to as R-Parameterization, which is a dual of the familiar Q-Parameterization. The classification is made to the stability conditions of the compensators and the plant by themselves; and necessary and sufficient conditions are based on the stability of Q and R themselves.

Measurements of acoustic impedance at the input to the occluded ear canal.

PubMed

Larson, V D; Nelson, J A; Cooper, W A; Egolf, D P

1993-01-01

Multi-frequency (multi-component) acoustic impedance measurements may evolve into a sensitive technique for the remote detection of aural pathologies. Such data are also relevant to models used in hearing aid design and could be an asset to the hearing aid prescription and fitting process. This report describes the development and use of a broad-band procedure which acquires impedance data in 20 Hz intervals and describes a comparison of data collected at two sites by different investigators. Mean data were in excellent agreement, and an explanation for a single case of extreme normal variability is presented.

Observer-Based Discrete-Time Nonnegative Edge Synchronization of Networked Systems.

PubMed

Su, Housheng; Wu, Han; Chen, Xia

2017-10-01

This paper studies the multi-input and multi-output discrete-time nonnegative edge synchronization of networked systems based on neighbors' output information. The communication relationship among the edges of networked systems is modeled by well-known line graph. Two observer-based edge synchronization algorithms are designed, for which some necessary and sufficient synchronization conditions are derived. Moreover, some computable sufficient synchronization conditions are obtained, in which the feedback matrix and the observer matrix are computed by solving the linear programming problems. We finally design several simulation examples to demonstrate the validity of the given nonnegative edge synchronization algorithms.

Concurrent design of an RTP chamber and advanced control system

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

Spence, P.; Schaper, C.; Kermani, A.

1995-12-31

A concurrent-engineering approach is applied to the development of an axisymmetric rapid-thermal-processing (RTP) reactor and its associated temperature controller. Using a detailed finite-element thermal model as a surrogate for actual hardware, the authors have developed and tested a multi-input multi-output (MIMO) controller. Closed-loop simulations are performed by linking the control algorithm with the finite-element code. Simulations show that good temperature uniformity is maintained on the wafer during both steady and transient conditions. A numerical study shows the effect of ramp rate, feedback gain, sensor placement, and wafer-emissivity patterns on system performance.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine

NASA Astrophysics Data System (ADS)

Farda, N. M.

2017-12-01

Coastal wetlands provide ecosystem services essential to people and the environment. Changes in coastal wetlands, especially on land use, are important to monitor by utilizing multi-temporal imagery. The Google Earth Engine (GEE) provides many machine learning algorithms (10 algorithms) that are very useful for extracting land use from imagery. The research objective is to explore machine learning in Google Earth Engine and its accuracy for multi-temporal land use mapping of coastal wetland area. Landsat 3 MSS (1978), Landsat 5 TM (1991), Landsat 7 ETM+ (2001), and Landsat 8 OLI (2014) images located in Segara Anakan lagoon are selected to represent multi temporal images. The input for machine learning are visible and near infrared bands, PCA band, invers PCA bands, bare soil index, vegetation index, wetness index, elevation from ASTER GDEM, and GLCM (Harralick) texture, and also polygon samples in 140 locations. There are 10 machine learning algorithms applied to extract coastal wetlands land use from Landsat imagery. The algorithms are Fast Naive Bayes, CART (Classification and Regression Tree), Random Forests, GMO Max Entropy, Perceptron (Multi Class Perceptron), Winnow, Voting SVM, Margin SVM, Pegasos (Primal Estimated sub-GrAdient SOlver for Svm), IKPamir (Intersection Kernel Passive Aggressive Method for Information Retrieval, SVM). Machine learning in Google Earth Engine are very helpful in multi-temporal land use mapping, the highest accuracy for land use mapping of coastal wetland is CART with 96.98 % Overall Accuracy using K-Fold Cross Validation (K = 10). GEE is particularly useful for multi-temporal land use mapping with ready used image and classification algorithms, and also very challenging for other applications.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

NASA Astrophysics Data System (ADS)

Jothiprakash, V.; Magar, R. B.

2012-07-01

SummaryIn this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

Fuzzy Neuron: Method and Hardware Realization

NASA Technical Reports Server (NTRS)

Krasowski, Michael J.; Prokop, Norman F.

2014-01-01

This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.

The Search Engine for Multi-Proteoform Complexes: An Online Tool for the Identification and Stoichiometry Determination of Protein Complexes.

PubMed

Skinner, Owen S; Schachner, Luis F; Kelleher, Neil L

2016-12-08

Recent advances in top-down mass spectrometry using native electrospray now enable the analysis of intact protein complexes with relatively small sample amounts in an untargeted mode. Here, we describe how to characterize both homo- and heteropolymeric complexes with high molecular specificity using input data produced by tandem mass spectrometry of whole protein assemblies. The tool described is a "search engine for multi-proteoform complexes," (SEMPC) and is available for free online. The output is a list of candidate multi-proteoform complexes and scoring metrics, which are used to define a distinct set of one or more unique protein subunits, their overall stoichiometry in the intact complex, and their pre- and post-translational modifications. Thus, we present an approach for the identification and characterization of intact protein complexes from native mass spectrometry data. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Characterizing multi-photon quantum interference with practical light sources and threshold single-photon detectors

NASA Astrophysics Data System (ADS)

Navarrete, Álvaro; Wang, Wenyuan; Xu, Feihu; Curty, Marcos

2018-04-01

The experimental characterization of multi-photon quantum interference effects in optical networks is essential in many applications of photonic quantum technologies, which include quantum computing and quantum communication as two prominent examples. However, such characterization often requires technologies which are beyond our current experimental capabilities, and today's methods suffer from errors due to the use of imperfect sources and photodetectors. In this paper, we introduce a simple experimental technique to characterize multi-photon quantum interference by means of practical laser sources and threshold single-photon detectors. Our technique is based on well-known methods in quantum cryptography which use decoy settings to tightly estimate the statistics provided by perfect devices. As an illustration of its practicality, we use this technique to obtain a tight estimation of both the generalized Hong‑Ou‑Mandel dip in a beamsplitter with six input photons and the three-photon coincidence probability at the output of a tritter.

Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang; Huang, K. S.; Diep, J.

1993-01-01

Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

Multi-look fusion identification: a paradigm shift from quality to quantity in data samples

NASA Astrophysics Data System (ADS)

Wong, S.

2009-05-01

A multi-look identification method known as score-level fusion is found to be capable of achieving very high identification accuracy, even when low quality target signatures are used. Analysis using measured ground vehicle radar signatures has shown that a 97% correct identification rate can be achieved using this multi-look fusion method; in contrast, only a 37% accuracy rate is obtained when single target signature input is used. The results suggest that quantity can be used to replace quality of the target data in improving identification accuracy. With the advent of sensor technology, a large amount of target signatures of marginal quality can be captured routinely. This quantity over quality approach allows maximum exploitation of the available data to improve the target identification performance and this could have the potential of being developed into a disruptive technology.

Economic analysis of centralized vs. decentralized electronic data capture in multi-center clinical studies.

PubMed

Walden, Anita; Nahm, Meredith; Barnett, M Edwina; Conde, Jose G; Dent, Andrew; Fadiel, Ahmed; Perry, Theresa; Tolk, Chris; Tcheng, James E; Eisenstein, Eric L

2011-01-01

New data management models are emerging in multi-center clinical studies. We evaluated the incremental costs associated with decentralized vs. centralized models. We developed clinical research network economic models to evaluate three data management models: centralized, decentralized with local software, and decentralized with shared database. Descriptive information from three clinical research studies served as inputs for these models. The primary outcome was total data management costs. Secondary outcomes included: data management costs for sites, local data centers, and central coordinating centers. Both decentralized models were more costly than the centralized model for each clinical research study: the decentralized with local software model was the most expensive. Decreasing the number of local data centers and case book pages reduced cost differentials between models. Decentralized vs. centralized data management in multi-center clinical research studies is associated with increases in data management costs.

Economic Analysis of Centralized vs. Decentralized Electronic Data Capture in Multi-Center Clinical Studies

PubMed Central

Walden, Anita; Nahm, Meredith; Barnett, M. Edwina; Conde, Jose G.; Dent, Andrew; Fadiel, Ahmed; Perry, Theresa; Tolk, Chris; Tcheng, James E.; Eisenstein, Eric L.

2012-01-01

Background New data management models are emerging in multi-center clinical studies. We evaluated the incremental costs associated with decentralized vs. centralized models. Methods We developed clinical research network economic models to evaluate three data management models: centralized, decentralized with local software, and decentralized with shared database. Descriptive information from three clinical research studies served as inputs for these models. Main Outcome Measures The primary outcome was total data management costs. Secondary outcomes included: data management costs for sites, local data centers, and central coordinating centers. Results Both decentralized models were more costly than the centralized model for each clinical research study: the decentralized with local software model was the most expensive. Decreasing the number of local data centers and case book pages reduced cost differentials between models. Conclusion Decentralized vs. centralized data management in multi-center clinical research studies is associated with increases in data management costs. PMID:21335692

Estimating the concentration of gold nanoparticles incorporated on natural rubber membranes using multi-level starlet optimal segmentation

NASA Astrophysics Data System (ADS)

de Siqueira, A. F.; Cabrera, F. C.; Pagamisse, A.; Job, A. E.

2014-12-01

This study consolidates multi-level starlet segmentation (MLSS) and multi-level starlet optimal segmentation (MLSOS) techniques for photomicrograph segmentation, based on starlet wavelet detail levels to separate areas of interest in an input image. Several segmentation levels can be obtained using MLSS; after that, Matthews correlation coefficient is used to choose an optimal segmentation level, giving rise to MLSOS. In this paper, MLSOS is employed to estimate the concentration of gold nanoparticles with diameter around 47 nm, reduced on natural rubber membranes. These samples were used for the construction of SERS/SERRS substrates and in the study of the influence of natural rubber membranes with incorporated gold nanoparticles on the physiology of Leishmania braziliensis. Precision, recall, and accuracy are used to evaluate the segmentation performance, and MLSOS presents an accuracy greater than 88 % for this application.

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

Initial insights on the performances and management of dairy cattle herds combining two breeds with contrasting features.

PubMed

Magne, M A; Thénard, V; Mihout, S

2016-05-01

Finding ways of increasing animal production with low external inputs and without compromising reproductive performances is a key issue of livestock systems sustainability. One way is to take advantage of the diversity and interactions among components within livestock systems. Among studies that investigate the influence of differences in animals' individual abilities in a herd, few focus on combinations of cow breeds with contrasting features in dairy cattle herds. This study aimed to analyse the performances and management of such multi-breed dairy cattle herds. These herds were composed of two types of dairy breeds: 'specialist' (Holstein) and 'generalist' (e.g. Montbeliarde, Simmental, etc.). Based on recorded milk data in southern French region, we performed (i) to compare the performances of dairy herds according to breed-type composition: multi-breed, single specialist breed or single generalist breed and (ii) to test the difference of milk performances of specialist and generalist breed cows (n = 10 682) per multi-breed dairy herd within a sample of 22 farms. The sampled farmers were also interviewed to characterise herd management through multivariate analysis. Multi-breed dairy herds had a better trade-off among milk yield, milk fat and protein contents, herd reproduction and concentrate-conversion efficiency than single-breed herds. Conversely, they did not offer advantages in terms of milk prices and udder health. Compared to specialist dairy herds, they produce less milk with the same concentrate-conversion efficiency but have better reproductive performances. Compared to generalist dairy herds, they produce more milk with better concentrate-conversion efficiency but have worse reproductive performances. Within herds, specialist and generalist breed cows significantly differed in milk performances, showing their complementarity. The former produced more milk for a longer lactation length while the latter produced milk with higher protein and fat contents and had a slightly longer lactation rank. Our results also focus on the farmers' management of multi-breed dairy herds underlying herd performances. Three strategies of management were identified and structured along two main axes. The first differentiates farmers according to their animal-selection practices in relation with their objectives of production: adapting animal to produce milk with low-feeding inputs v. focussing on milk yield trait to intensify milk production. The second refers to the purpose farmers give to multi-breed dairy herds: milk v. milk/meat production. These initial insights on the performances and management of multi-breed dairy herds contribute to better understanding the functioning of ruminant livestock systems based on individual variability.

A multi-purpose open-source triggering platform for magnetic resonance

NASA Astrophysics Data System (ADS)

Ruytenberg, T.; Webb, A. G.; Beenakker, J. W. M.

2014-10-01

Many MR scans need to be synchronised with external events such as the cardiac or respiratory cycles. For common physiological functions commercial trigger equipment exists, but for more experimental inputs these are not available. This paper describes the design of a multi-purpose open-source trigger platform for MR systems. The heart of the system is an open-source Arduino Due microcontroller. This microcontroller samples an analogue input and digitally processes these data to determine the trigger. The output of the microcontroller is programmed to mimic a physiological signal which is fed into the electrocardiogram (ECG) or pulse oximeter port of MR scanner. The microcontroller is connected to a Bluetooth dongle that allows wireless monitoring and control outside the scanner room. This device can be programmed to generate a trigger based on various types of input. As one example, this paper describes how it can be used as an acoustic cardiac triggering unit. For this, a plastic stethoscope is connected to a microphone which is used as an input for the system. This test setup was used to acquire retrospectively-triggered cardiac scans in ten volunteers. Analysis showed that this platform produces a reliable trigger (>99% triggers are correct) with a small average 8 ms variation between the exact trigger points.

A multi-purpose open-source triggering platform for magnetic resonance.

PubMed

Ruytenberg, T; Webb, A G; Beenakker, J W M

2014-10-01

Many MR scans need to be synchronised with external events such as the cardiac or respiratory cycles. For common physiological functions commercial trigger equipment exists, but for more experimental inputs these are not available. This paper describes the design of a multi-purpose open-source trigger platform for MR systems. The heart of the system is an open-source Arduino Due microcontroller. This microcontroller samples an analogue input and digitally processes these data to determine the trigger. The output of the microcontroller is programmed to mimic a physiological signal which is fed into the electrocardiogram (ECG) or pulse oximeter port of MR scanner. The microcontroller is connected to a Bluetooth dongle that allows wireless monitoring and control outside the scanner room. This device can be programmed to generate a trigger based on various types of input. As one example, this paper describes how it can be used as an acoustic cardiac triggering unit. For this, a plastic stethoscope is connected to a microphone which is used as an input for the system. This test setup was used to acquire retrospectively-triggered cardiac scans in ten volunteers. Analysis showed that this platform produces a reliable trigger (>99% triggers are correct) with a small average 8 ms variation between the exact trigger points. Copyright © 2014 Elsevier Inc. All rights reserved.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

NASA Technical Reports Server (NTRS)

Meyn, Larry A.

2018-01-01

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

A Computational Methodology for Simulating Thermal Loss Testing of the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Reid, Terry V.; Wilson, Scott D.; Schifer, Nicholas A.; Briggs, Maxwell H.

2012-01-01

The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two highefficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. In an effort to improve net heat input predictions, numerous tasks have been performed which provided a more accurate value for net heat input into the ASCs, including the use of multidimensional numerical models. Validation test hardware has also been used to provide a direct comparison of numerical results and validate the multi-dimensional numerical models used to predict convertor net heat input and efficiency. These validation tests were designed to simulate the temperature profile of an operating Stirling convertor and resulted in a measured net heat input of 244.4 W. The methodology was applied to the multi-dimensional numerical model which resulted in a net heat input of 240.3 W. The computational methodology resulted in a value of net heat input that was 1.7 percent less than that measured during laboratory testing. The resulting computational methodology and results are discussed.

Progressive multi-atlas label fusion by dictionary evolution.

PubMed

Song, Yantao; Wu, Guorong; Bahrami, Khosro; Sun, Quansen; Shen, Dinggang

2017-02-01

Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of the Geophysical Scale Multi-Block Transport Modeling System to Hydrodynamic Forcing of Dredged Material Placement Sediment Transport within the James River Estuary

NASA Astrophysics Data System (ADS)

Kim, S. C.; Hayter, E. J.; Pruhs, R.; Luong, P.; Lackey, T. C.

2016-12-01

The geophysical scale circulation of the Mid Atlantic Bight and hydrologic inputs from adjacent Chesapeake Bay watersheds and tributaries influences the hydrodynamics and transport of the James River estuary. Both barotropic and baroclinic transport govern the hydrodynamics of this partially stratified estuary. Modeling the placement of dredged sediment requires accommodating this wide spectrum of atmospheric and hydrodynamic scales. The Geophysical Scale Multi-Block (GSMB) Transport Modeling System is a collection of multiple well established and USACE approved process models. Taking advantage of the parallel computing capability of multi-block modeling, we performed one year three-dimensional modeling of hydrodynamics in supporting simulation of dredged sediment placements transport and morphology changes. Model forcing includes spatially and temporally varying meteorological conditions and hydrological inputs from the watershed. Surface heat flux estimates were derived from the National Solar Radiation Database (NSRDB). The open water boundary condition for water level was obtained from an ADCIRC model application of the U. S. East Coast. Temperature-salinity boundary conditions were obtained from the Environmental Protection Agency (EPA) Chesapeake Bay Program (CBP) long-term monitoring stations database. Simulated water levels were calibrated and verified by comparison with National Oceanic and Atmospheric Administration (NOAA) tide gage locations. A harmonic analysis of the modeled tides was performed and compared with NOAA tide prediction data. In addition, project specific circulation was verified using US Army Corps of Engineers (USACE) drogue data. Salinity and temperature transport was verified at seven CBP long term monitoring stations along the navigation channel. Simulation and analysis of model results suggest that GSMB is capable of resolving the long duration, multi-scale processes inherent to practical engineering problems such as dredged material placement stability.

Report on stakeholder input on transformational goals, performance measures and user needs for integrated dynamic transit operations.

DOT National Transportation Integrated Search

2012-03-01

In support of USDOTs Intelligent Transportation Systems (ITS) Mobility Program, the Dynamic Mobility Applications (DMA) program seeks to create applications that fully leverage frequently collected and rapidly disseminated multi-source data gat...

LIMEPY: Lowered Isothermal Model Explorer in PYthon

NASA Astrophysics Data System (ADS)

Gieles, Mark; Zocchi, Alice

2017-10-01

LIMEPY solves distribution function (DF) based lowered isothermal models. It solves Poisson's equation used on input parameters and offers fast solutions for isotropic/anisotropic, single/multi-mass models, normalized DF values, density and velocity moments, projected properties, and generates discrete samples.

3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

PubMed

Li, Xiaomeng; Dou, Qi; Chen, Hao; Fu, Chi-Wing; Qi, Xiaojuan; Belavý, Daniel L; Armbrecht, Gabriele; Felsenberg, Dieter; Zheng, Guoyan; Heng, Pheng-Ann

2018-04-01

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on extended data collected from two different time points demonstrate the efficacy of our method on tracking the morphological changes in a longitudinal study. Copyright © 2018 Elsevier B.V. All rights reserved.

Predicting the tensile strength of compacted multi-component mixtures of pharmaceutical powders.

PubMed

Wu, Chuan-Yu; Best, Serena M; Bentham, A Craig; Hancock, Bruno C; Bonfield, William

2006-08-01

Pharmaceutical tablets are generally produced by compacting a mixture of several ingredients, including active drugs and excipients. It is of practical importance if the properties of such tablets can be predicted on the basis of the ones for constituent components. The purpose of this work is to develop a theoretical model which can predict the tensile strength of compacted multi-component pharmaceutical mixtures. The model was derived on the basis of the Ryshkewitch-Duckworth equation that was originally proposed for porous materials. The required input parameters for the model are the relative density or solid fraction (ratio of the volume of solid materials to the total volume of the tablets) of the multi-component tablets and parameters associated with the constituent single-component powders, which are readily accessible. The tensile strength of tablets made of various powder blends at different relative density was also measured using diametrical compression. It has been shown that the tensile strength of the multi-component powder compacts is primarily a function of the solid fraction. Excellent agreement between prediction and experimental data for tablets of binary, ternary and four-component blends of some widely used pharmaceutical excipients was obtained. It has been demonstrated that the proposed model can well predict the tensile strength of multi-component pharmaceutical tablets. Thus, the model will be a useful design tool for formulation engineers in the pharmaceutical industry.

Multi-disciplinary team meetings in stroke rehabilitation: an observation study and conceptual framework.

PubMed

Tyson, S F; Burton, L; McGovern, A

2014-12-01

To explore how multi-disciplinary team meetings operate in stroke rehabilitation. Non-participant observation of multi-disciplinary team meetings and semi-structured interviews with attending staff. Twelve meetings were observed (at least one at each site) and 18 staff (one psychologist, one social worker; four nurses; four physiotherapists four occupational therapists, two speech and language therapists, one stroke co-ordinator and one stroke ward manager) were interviewed in eight in-patient stroke rehabilitation units. Multi-disciplinary team meetings in stroke rehabilitation were complex, demanding and highly varied. A model emerged which identified the main inputs to influence conduct of the meetings were personal contributions of the members and structure and format of the meetings. These were mediated by the team climate and leadership skills of the chair. The desired outputs; clinical decisions and the attributes of apparently effective meetings were identified by the staff. A notable difference between the meetings that staff considered effective and those that were not, was their structure and format. Successful meetings tended to feature a set agenda, structured documentation; formal use of measurement tools; pre-meeting preparation and skilled chairing. These features were often absent in meetings perceived to be ineffective. The main features of operation of multi-disciplinary team meetings have been identified which will enable assessment tools and interventions to improve effectiveness to be developed. © The Author(s) 2014.

Quadratic spatial soliton interactions

NASA Astrophysics Data System (ADS)

Jankovic, Ladislav

Quadratic spatial soliton interactions were investigated in this Dissertation. The first part deals with characterizing the principal features of multi-soliton generation and soliton self-reflection. The second deals with two beam processes leading to soliton interactions and collisions. These subjects were investigated both theoretically and experimentally. The experiments were performed by using potassium niobate (KNBO 3) and periodically poled potassium titanyl phosphate (KTP) crystals. These particular crystals were desirable for these experiments because of their large nonlinear coefficients and, more importantly, because the experiments could be performed under non-critical-phase-matching (NCPM) conditions. The single soliton generation measurements, performed on KNBO3 by launching the fundamental component only, showed a broad angular acceptance bandwidth which was important for the soliton collisions performed later. Furthermore, at high input intensities multi-soliton generation was observed for the first time. The influence on the multi-soliton patterns generated of the input intensity and beam symmetry was investigated. The combined experimental and theoretical efforts indicated that spatial and temporal noise on the input laser beam induced multi-soliton patterns. Another research direction pursued was intensity dependent soliton routing by using of a specially engineered quadratically nonlinear interface within a periodically poled KTP sample. This was the first time demonstration of the self-reflection phenomenon in a system with a quadratic nonlinearity. The feature investigated is believed to have a great potential for soliton routing and manipulation by engineered structures. A detailed investigation was conducted on two soliton interaction and collision processes. Birth of an additional soliton resulting from a two soliton collision was observed and characterized for the special case of a non-planar geometry. A small amount of spiraling, up to 30 degrees rotation, was measured in the experiments performed. The parameters relevant for characterizing soliton collision processes were also studied in detail. Measurements were performed for various collision angles (from 0.2 to 4 degrees), phase mismatch, relative phase between the solitons and the distance to the collision point within the sample (which affects soliton formation). Both the individual and combined effects of these collision variables were investigated. Based on the research conducted, several all-optical switching scenarios were proposed.

Assessing the impacts induced by global climate change through a multi-risk approach: lessons learned from the North Adriatic coast (Italy)

NASA Astrophysics Data System (ADS)

Gallina, Valentina; Torressan, Silvia; Zabeo, Alex; Critto, Andrea; Glade, Thomas; Marcomini, Antonio

2015-04-01

Climate change is expected to pose a wide range of impacts on natural and human systems worldwide, increasing risks from long-term climate trends and disasters triggered by weather extremes. Accordingly, in the future, one region could be potentially affected by interactions, synergies and trade-offs of multiple hazards and impacts. A multi-risk risk approach is needed to effectively address multiple threats posed by climate change across regions and targets supporting decision-makers toward a new paradigm of multi-hazard and risk management. Relevant initiatives have been already developed for the assessment of multiple hazards and risks affecting the same area in a defined timeframe by means of quantitative and semi-quantitative approaches. Most of them are addressing the relations of different natural hazards, however, the effect of future climate change is usually not considered. In order to fill this gap, an advanced multi-risk methodology was developed at the Euro-Mediterranean Centre on Climate Change (CMCC) for estimating cumulative impacts related to climate change at the regional (i.e. sub-national) scale. This methodology was implemented into an assessment tool which allows to scan and classify quickly natural systems and human assets at risk resulting from different interacting hazards. A multi-hazard index is proposed to evaluate the relationships of different climate-related hazards (e.g. sea-level rise, coastal erosion, storm surge) occurring in the same spatial and temporal area, by means of an influence matrix and the disjoint probability function. Future hazard scenarios provided by regional climate models are used as input for this step in order to consider possible effects of future climate change scenarios. Then, the multi-vulnerability of different exposed receptors (e.g. natural systems, beaches, agricultural and urban areas) is estimated through a variety of vulnerability indicators (e.g. vegetation cover, sediment budget, % of urbanization), tailored case by case to different sets of natural hazards and elements at risk. Finally, the multi-risk assessment integrates the multi-hazard with the multi-vulnerability index of exposed receptors, providing a relative ranking of areas and targets potentially affected by multiple risks in the considered region. The methodology was applied to the North Adriatic coast (Italy) producing a range of GIS-based multi-hazard, exposure, multi-vulnerability and multi-risk maps that can be used by policy-makers to define risk management and adaptation strategies. Results show that areas affected by higher multi-hazard scores are located close to the coastline where all the investigated hazards are present. Multi-vulnerability assumes relatively high scores in the whole case study, showing that beaches, wetlands, protected areas and river mouths are the more sensible targets. The final estimate of multi-risk for coastal municipalities provides useful information for local public authorities to set future priorities for adaptation and define future plans for shoreline and coastal management in view of climate change.

Multi-scale gyrokinetic simulations of an Alcator C-Mod, ELM-y H-mode plasma

NASA Astrophysics Data System (ADS)

Howard, N. T.; Holland, C.; White, A. E.; Greenwald, M.; Rodriguez-Fernandez, P.; Candy, J.; Creely, A. J.

2018-01-01

High fidelity, multi-scale gyrokinetic simulations capable of capturing both ion ({k}θ {ρ }s∼ { O }(1.0)) and electron-scale ({k}θ {ρ }e∼ { O }(1.0)) turbulence were performed in the core of an Alcator C-Mod ELM-y H-mode discharge which exhibits reactor-relevant characteristics. These simulations, performed with all experimental inputs and realistic ion to electron mass ratio ({({m}i/{m}e)}1/2=60.0) provide insight into the physics fidelity that may be needed for accurate simulation of the core of fusion reactor discharges. Three multi-scale simulations and series of separate ion and electron-scale simulations performed using the GYRO code (Candy and Waltz 2003 J. Comput. Phys. 186 545) are presented. As with earlier multi-scale results in L-mode conditions (Howard et al 2016 Nucl. Fusion 56 014004), both ion and multi-scale simulations results are compared with experimentally inferred ion and electron heat fluxes, as well as the measured values of electron incremental thermal diffusivities—indicative of the experimental electron temperature profile stiffness. Consistent with the L-mode results, cross-scale coupling is found to play an important role in the simulation of these H-mode conditions. Extremely stiff ion-scale transport is observed in these high-performance conditions which is shown to likely play and important role in the reproduction of measurements of perturbative transport. These results provide important insight into the role of multi-scale plasma turbulence in the core of reactor-relevant plasmas and establish important constraints on the the fidelity of models needed for predictive simulations.

Impact of Intercollegiate Athletic Participation on Leadership Development

ERIC Educational Resources Information Center

Huntrods, Clint S.; An, Brian P.; Pascarella, Ernest T.

2017-01-01

This study examined the effects of participation in intercollegiate athletics on leadership development using a multi-institutional, longitudinal sample of students at four-year institutions. Using Astin's (1993) Input-Environment-Outcome model, we examined whether athletic participation influenced leadership development using the Socially…

A CONCEPTUAL MODEL FOR MULTI-SCALAR ASSESSMENTS OF ESTUARINE ECOLOGICAL INTEGRITY

EPA Science Inventory

A conceptual model was developed that relates an estuarine system's anthropogenic inputs to it's ecological integrity. Ecological integrity is operationally defined as an emergent property of an ecosystem that exists when the structural components are complete and the functional ...

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

NASA Astrophysics Data System (ADS)

Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin

2017-09-01

This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

Boiler-turbine control system design using a genetic algorithm

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

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Neural dynamic optimization for control systems. I. Background.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Neural dynamic optimization for control systems.III. Applications.

PubMed

Seong, C Y; Widrow, B

2001-01-01

For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively.

Neural dynamic optimization for control systems.II. Theory.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Manifold traversing as a model for learning control of autonomous robots

NASA Technical Reports Server (NTRS)

Szakaly, Zoltan F.; Schenker, Paul S.

1992-01-01

This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.

An optimal control model approach to the design of compensators for simulator delay

NASA Technical Reports Server (NTRS)

Baron, S.; Lancraft, R.; Caglayan, A.

1982-01-01

The effects of display delay on pilot performance and workload and of the design of the filters to ameliorate these effects were investigated. The optimal control model for pilot/vehicle analysis was used both to determine the potential delay effects and to design the compensators. The model was applied to a simple roll tracking task and to a complex hover task. The results confirm that even small delays can degrade performance and impose a workload penalty. A time-domain compensator designed by using the optimal control model directly appears capable of providing extensive compensation for these effects even in multi-input, multi-output problems.

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

PubMed

Perdikaris, P; Raissi, M; Damianou, A; Lawrence, N D; Karniadakis, G E

2017-02-01

Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

A multi-period distribution network design model under demand uncertainty

NASA Astrophysics Data System (ADS)

Tabrizi, Babak H.; Razmi, Jafar

2013-05-01

Supply chain management is taken into account as an inseparable component in satisfying customers' requirements. This paper deals with the distribution network design (DND) problem which is a critical issue in achieving supply chain accomplishments. A capable DND can guarantee the success of the entire network performance. However, there are many factors that can cause fluctuations in input data determining market treatment, with respect to short-term planning, on the one hand. On the other hand, network performance may be threatened by the changes that take place within practicing periods, with respect to long-term planning. Thus, in order to bring both kinds of changes under control, we considered a new multi-period, multi-commodity, multi-source DND problem in circumstances where the network encounters uncertain demands. The fuzzy logic is applied here as an efficient tool for controlling the potential customers' demand risk. The defuzzifying framework leads the practitioners and decision-makers to interact with the solution procedure continuously. The fuzzy model is then validated by a sensitivity analysis test, and a typical problem is solved in order to illustrate the implementation steps. Finally, the formulation is tested by some different-sized problems to show its total performance.

Design and Construction Multi Output Power Transmition with Single Prime Mover on Agricultural Products Machine

NASA Astrophysics Data System (ADS)

Koten, V. K.; Tanamal, C. E.

2017-03-01

Manufacturing agricultural products by the farmers, people or person who involve in medium industry, small industry, and households industry still be done in separately. Although the power on primemover is enough, in operations, primemover was only to move one of several agricultural products machine. This study attempts to design and construct power transmition multi output with single primemover; a single construction that allows primemover move some agricultur products machine in the same or not. This study begins with the determination of production capacity and the power to destroy products, the determination of resources and rotation, normalization of resources and rotation, the determination of the type material used, the size determination of each machine elements, construction machine elements, and assemble machine elements into a construction multi output power transmition with single primemover on agricultural products machine. The results show that with a input normalization 4 PK (2984 Watt), rotation 2000 rpm, the strength of material 60 kg/mm2, and several operating consideration, thus obtained size of machine elements through calculation. Based on the size, the machine elements is made through the use of some machine tools and assembled to form a multi output power transmition with single primemover.

Multi-component ground motion response spectra for coupled horizontal, vertical, angular accelerations, and tilt

USGS Publications Warehouse

Kalkan, E.; Graizer, V.

2007-01-01

Rotational and vertical components of ground motion are almost always ignored in design or in the assessment of structures despite the fact that vertical motion can be twice as much as the horizontal motion and may exceed 2g level, and rotational excitation may reach few degrees in the proximity of fault rupture. Coupling of different components of ground excitation may significantly amplify the seismic demand by introducing additional lateral forces and enhanced P-?? effects. In this paper, a governing equation of motion is postulated to compute the response of a SDOF oscillator under a multi-component excitation. The expanded equation includes secondary P-?? components associated with the combined impacts of tilt and vertical excitations in addition to the inertial forcing terms due to the angular and translational accelerations. The elastic and inelastic spectral ordinates traditionally generated considering the uniaxial input motion are compared at the end with the multi-component response spectra of coupled horizontal, vertical and tilting motions. The proposed multi-component response spectrum reflects kinematic characteristics of the ground motion that are not identifiable by the conventional spectrum itself, at least for the near-fault region where high intensity vertical shaking and rotational excitation are likely to occur.

An ultra-small, multi-point, and multi-color photo-detection system with high sensitivity and high dynamic range.

PubMed

Anazawa, Takashi; Yamazaki, Motohiro

2017-12-05

Although multi-point, multi-color fluorescence-detection systems are widely used in various sciences, they would find wider applications if they are miniaturized. Accordingly, an ultra-small, four-emission-point and four-color fluorescence-detection system was developed. Its size (space between emission points and a detection plane) is 15 × 10 × 12 mm, which is three-orders-of-magnitude smaller than that of a conventional system. Fluorescence from four emission points with an interval of 1 mm on the same plane was respectively collimated by four lenses and split into four color fluxes by four dichroic mirrors. Then, a total of sixteen parallel color fluxes were directly input into an image sensor and simultaneously detected. The emission-point plane and the detection plane (the image-sensor surface) were parallel and separated by a distance of only 12 mm. The developed system was applied to four-capillary array electrophoresis and successfully achieved Sanger DNA sequencing. Moreover, compared with a conventional system, the developed system had equivalent high fluorescence-detection sensitivity (lower detection limit of 17 pM dROX) and 1.6-orders-of-magnitude higher dynamic range (4.3 orders of magnitude).

The transformation of multi-sensory experiences into memories during sleep.

PubMed

Rothschild, Gideon

2018-03-26

Our everyday lives present us with a continuous stream of multi-modal sensory inputs. While most of this information is soon forgotten, sensory information associated with salient experiences can leave long-lasting memories in our minds. Extensive human and animal research has established that the hippocampus is critically involved in this process of memory formation and consolidation. However, the underlying mechanistic details are still only partially understood. Specifically, the hippocampus has often been suggested to encode information during experience, temporarily store it, and gradually transfer this information to the cortex during sleep. In rodents, ample evidence has supported this notion in the context of spatial memory, yet whether this process adequately describes the consolidation of multi-sensory experiences into memories is unclear. Here, focusing on rodent studies, I examine how multi-sensory experiences are consolidated into long term memories by hippocampal and cortical circuits during sleep. I propose that in contrast to the classical model of memory consolidation, the cortex is a "fast learner" that has a rapid and instructive role in shaping hippocampal-dependent memory consolidation. The proposed model may offer mechanistic insight into memory biasing using sensory cues during sleep. Copyright © 2018 Elsevier Inc. All rights reserved.

FMS: A Format Manipulation System for Automatic Production of Natural Language Documents, Second Edition. Final Report.

ERIC Educational Resources Information Center

Silver, Steven S.

FMS/3 is a system for producing hard copy documentation at high speed from free format text and command input. The system was originally written in assembler language for a 12K IBM 360 model 20 using a high speed 1403 printer with the UCS-TN chain option (upper and lower case). Input was from an IBM 2560 Multi-function Card Machine. The model 20…

Measurement equivalence: A non-technical primer on categorical multi-group confirmatory factor analysis in school psychology.

PubMed

Pendergast, Laura L; von der Embse, Nathaniel; Kilgus, Stephen P; Eklund, Katie R

2017-02-01

Evidence-based interventions (EBIs) have become a central component of school psychology research and practice, but EBIs are dependent upon the availability and use of evidence-based assessments (EBAs) with diverse student populations. Multi-group confirmatory factor analysis (MG-CFA) is an analytical tool that can be used to examine the validity and measurement equivalence/invariance of scores across diverse groups. The objective of this article is to provide a conceptual and procedural overview of categorical MG-CFA, as well as an illustrated example based on data from the Social and Academic Behavior Risk Screener (SABRS) - a tool designed for use in school-based interventions. This article serves as a non-technical primer on the topic of MG-CFA with ordinal (rating scale) data and does so through the framework of examining equivalence of measures used for EBIs within multi-tiered models - an understudied topic. To go along with the illustrated example, we have provided supplementary files that include sample data, Mplus input code, and an annotated guide for understanding the input code (http://dx.doi.org/10.1016/j.jsp.2016.11.002). Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Multiple-input single-output closed-loop isometric force control using asynchronous intrafascicular multi-electrode stimulation.

PubMed

Frankel, Mitchell A; Dowden, Brett R; Mathews, V John; Normann, Richard A; Clark, Gregory A; Meek, Sanford G

2011-06-01

Although asynchronous intrafascicular multi-electrode stimulation (IFMS) can evoke fatigue-resistant muscle force, a priori determination of the necessary stimulation parameters for precise force production is not possible. This paper presents a proportionally-modulated, multiple-input single-output (MISO) controller that was designed and experimentally validated for real-time, closed-loop force-feedback control of asynchronous IFMS. Experiments were conducted on anesthetized felines with a Utah Slanted Electrode Array implanted in the sciatic nerve, either acutely or chronically ( n = 1 for each). Isometric forces were evoked in plantar-flexor muscles, and target forces consisted of up to 7 min of step, sinusoidal, and more complex time-varying trajectories. The controller was successful in evoking steps in force with time-to-peak of less than 0.45 s, steady-state ripple of less than 7% of the mean steady-state force, and near-zero steady-state error even in the presence of muscle fatigue, but with transient overshoot of near 20%. The controller was also successful in evoking target sinusoidal and complex time-varying force trajectories with amplitude error of less than 0.5 N and time delay of approximately 300 ms. This MISO control strategy can potentially be used to develop closed-loop asynchronous IFMS controllers for a wide variety of multi-electrode stimulation applications to restore lost motor function.

iTOUGH2 v7.1

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

FINSTERLE, STEFAN; JUNG, YOOJIN; KOWALSKY, MICHAEL

2016-09-15

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional, multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. iTOUGH2 performs sensitivity analyses, data-worth analyses, parameter estimation, and uncertainty propagation analyses in geosciences and reservoir engineering and other application areas. iTOUGH2 supports a number of different combinations of fluids and components (equation-of-state (EOS) modules). In addition, the optimization routines implemented in iTOUGH2 can also be used for sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files using the PEST protocol. iTOUGH2 solves the inverse problem bymore » minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative-free, gradient-based, and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlo simulations for uncertainty propagation analyses. A detailed residual and error analysis is provided. This upgrade includes (a) global sensitivity analysis methods, (b) dynamic memory allocation (c) additional input features and output analyses, (d) increased forward simulation capabilities, (e) parallel execution on multicore PCs and Linux clusters, and (f) bug fixes. More details can be found at http://esd.lbl.gov/iTOUGH2.« less

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

All-optical logic gates and wavelength conversion via the injection locking of a Fabry-Perot semiconductor laser

NASA Astrophysics Data System (ADS)

Harvey, E.; Pochet, M.; Schmidt, J.; Locke, T.; Naderi, N.; Usechak, N. G.

2013-03-01

This work investigates the implementation of all-optical logic gates based on optical injection locking (OIL). All-optical inverting, NOR, and NAND gates are experimentally demonstrated using two distributed feedback (DFB) lasers, a multi-mode Fabry-Perot laser diode, and an optical band-pass filter. The DFB lasers are externally modulated to represent logic inputs into the cavity of the multi-mode Fabry-Perot slave laser. The input DFB (master) lasers' wavelengths are aligned with the longitudinal modes of the Fabry-Perot slave laser and their optical power is used to modulate the injection conditions in the Fabry-Perot slave laser. The optical band-pass filter is used to select a Fabry- Perot mode that is either suppressed or transmitted given the logic state of the injecting master laser signals. When the input signal(s) is (are) in the on state, injection locking, and thus the suppression of the non-injected Fabry-Perot modes, is induced, yielding a dynamic system that can be used to implement photonic logic functions. Additionally, all-optical photonic processing is achieved using the cavity-mode shift produced in the injected slave laser under external optical injection. The inverting logic case can also be used as a wavelength converter — a key component in advanced wavelength-division multiplexing networks. As a result of this experimental investigation, a more comprehensive understanding of the locking parameters involved in injecting multiple lasers into a multi-mode cavity and the logic transition time is achieved. The performance of optical logic computations and wavelength conversion has the potential for ultrafast operation, limited primarily by the photon decay rate in the slave laser.

Nutrient Budgets and Management Actions in the Patuxent River Estuary, Maryland

EPA Science Inventory

Multi-year nitrogen (N) and phosphorus (P) budgets were developed for the Patuxent River estuary, a seasonally stratified and moderately eutrophic tributary of Chesapeake Bay. Major inputs (point, diffuse, septic and direct atmospheric) were measured for 13 years during which la...

Integrating Ecological and Water Footprint Accounting in a Multi-Regional Input-Output Framework

EPA Science Inventory

Carbon, ecological, and water footprints (CF, EF, and WF) are accounting tools that can be used to understand the connection between consumption activities and environmental pressures on the Earth?s atmosphere, bioproductive areas, and freshwater resources. These indicators have ...

Detecting carbon uptake by individual algae in multi-species assemblages

USDA-ARS?s Scientific Manuscript database

Knowing how different algal species utilize carbon (C) can help predict how assemblage changes will alter energy input and flow in ecosystems, and can help refine algal species selection for bioengineering applications. Fourier-transform infrared (FTIR) microspectroscopy was used to measure inorgani...

A method to stabilize linear systems using eigenvalue gradient information

NASA Technical Reports Server (NTRS)

Wieseman, C. D.

1985-01-01

Formal optimization methods and eigenvalue gradient information are used to develop a stabilizing control law for a closed loop linear system that is initially unstable. The method was originally formulated by using direct, constrained optimization methods with the constraints being the real parts of the eigenvalues. However, because of problems in trying to achieve stabilizing control laws, the problem was reformulated to be solved differently. The method described uses the Davidon-Fletcher-Powell minimization technique to solve an indirect, constrained minimization problem in which the performance index is the Kreisselmeier-Steinhauser function of the real parts of all the eigenvalues. The method is applied successfully to solve two different problems: the determination of a fourth-order control law stabilizes a single-input single-output active flutter suppression system and the determination of a second-order control law for a multi-input multi-output lateral-directional flight control system. Various sets of design variables and initial starting points were chosen to show the robustness of the method.

Graphene-ferroelectric metadevices for nonvolatile memory and reconfigurable logic-gate operations.

PubMed

Kim, Woo Young; Kim, Hyeon-Don; Kim, Teun-Teun; Park, Hyun-Sung; Lee, Kanghee; Choi, Hyun Joo; Lee, Seung Hoon; Son, Jaehyeon; Park, Namkyoo; Min, Bumki

2016-01-27

Memory metamaterials are artificial media that sustain transformed electromagnetic properties without persistent external stimuli. Previous memory metamaterials were realized with phase-change materials, such as vanadium dioxide or chalcogenide glasses, which exhibit memory behaviour with respect to electrically/optically induced thermal stimuli. However, they require a thermally isolated environment for longer retention or strong optical pump for phase-change. Here we demonstrate electrically programmable nonvolatile memory metadevices realised by the hybridization of graphene, a ferroelectric and meta-atoms/meta-molecules, and extend the concept further to establish reconfigurable logic-gate metadevices. For a memory metadevice having a single electrical input, amplitude, phase and even the polarization multi-states were clearly distinguishable with a retention time of over 10 years at room temperature. Furthermore, logic-gate functionalities were demonstrated with reconfigurable logic-gate metadevices having two electrical inputs, with each connected to separate ferroelectric layers that act as the multi-level controller for the doping level of the sandwiched graphene layer.

Quadratic stabilisability of multi-agent systems under switching topologies

NASA Astrophysics Data System (ADS)

Guan, Yongqiang; Ji, Zhijian; Zhang, Lin; Wang, Long

2014-12-01

This paper addresses the stabilisability of multi-agent systems (MASs) under switching topologies. Necessary and/or sufficient conditions are presented in terms of graph topology. These conditions explicitly reveal how the intrinsic dynamics of the agents, the communication topology and the external control input affect stabilisability jointly. With the appropriate selection of some agents to which the external inputs are applied and the suitable design of neighbour-interaction rules via a switching topology, an MAS is proved to be stabilisable even if so is not for each of uncertain subsystem. In addition, a method is proposed to constructively design a switching rule for MASs with norm-bounded time-varying uncertainties. The switching rules designed via this method do not rely on uncertainties, and the switched MAS is quadratically stabilisable via decentralised external self-feedback for all uncertainties. With respect to applications of the stabilisability results, the formation control and the cooperative tracking control are addressed. Numerical simulations are presented to demonstrate the effectiveness of the proposed results.

Monte Carlo-based interval transformation analysis for multi-criteria decision analysis of groundwater management strategies under uncertain naphthalene concentrations and health risks

NASA Astrophysics Data System (ADS)

Ren, Lixia; He, Li; Lu, Hongwei; Chen, Yizhong

2016-08-01

A new Monte Carlo-based interval transformation analysis (MCITA) is used in this study for multi-criteria decision analysis (MCDA) of naphthalene-contaminated groundwater management strategies. The analysis can be conducted when input data such as total cost, contaminant concentration and health risk are represented as intervals. Compared to traditional MCDA methods, MCITA-MCDA has the advantages of (1) dealing with inexactness of input data represented as intervals, (2) mitigating computational time due to the introduction of Monte Carlo sampling method, (3) identifying the most desirable management strategies under data uncertainty. A real-world case study is employed to demonstrate the performance of this method. A set of inexact management alternatives are considered in each duration on the basis of four criteria. Results indicated that the most desirable management strategy lied in action 15 for the 5-year, action 8 for the 10-year, action 12 for the 15-year, and action 2 for the 20-year management.

Estimation of the contribution of exports to the provincial economy: an analysis based on China's multi-regional input-output tables.

PubMed

Wu, Sanmang; Li, Shantong; Lei, Yalin

2016-01-01

This paper developed an estimation model for the contribution of exports to a country's regional economy based on the Chenery-Moses model and conducted an empirical analysis using China's multi-regional input-output tables for 1997, 2002, and 2007. The results indicated that China's national exports make significantly different contributions to the provincial economy in various regions, with the greatest contribution being observed in the eastern region and the smallest in the central region. The provinces are also subjected to significantly different export spillover effects. The boosting effect for the eastern provinces is primarily generated from local exports, whereas the western provinces primarily benefit from the export spillover effect from the eastern provinces. The eastern provinces, such as Guangdong, Zhejiang, Jiangsu, and Shanghai, are the primary sources of export spillover effects, and Guangdong is the largest source of export spillover effects for almost all of the provinces in China.

Remote Sensing based modelling of Annual Surface Mass Balances of Chhota Shigiri Glacier, Western Himalayas, India

NASA Astrophysics Data System (ADS)

Chandrasekharan, Anita; Ramsankaran, Raaj

2017-04-01

The current study aims at modelling glacier mass balances over Chhota Shigiri glacier (32.28o N; 77.58° E) in Himachal Pradesh, India using the Equilibrium Line Altitude (ELA) gradient approach proposed by Rabatel et al. (2005). The model requires yearly ELA, average mass balance and mass balance gradient to estimate annual mass balance of a glacier which can be obtained either through field measurements or remote sensing observations. However, in view of the general scenario of lack of field data for Himalayan glaciers, in this study the model has been applied only using the inputs derived through multi-temporal satellite remote sensing observations thus eliminating the need for any field measurements. Preliminary analysis show that the obtained results are comparable with the observed field mass balance. The results also demonstrate that this approach with remote sensing inputs has potential to be used for glacier mass balance estimations provided good quality multi-temporal remote sensing dataset are available.

Multi-flux-transformer MRI detection with an atomic magnetometer.

PubMed

Savukov, Igor; Karaulanov, Todor

2014-12-01

Recently, anatomical ultra-low field (ULF) MRI has been demonstrated with an atomic magnetometer (AM). A flux-transformer (FT) has been used for decoupling MRI fields and gradients to avoid their negative effects on AM performance. The field of view (FOV) was limited because of the need to compromise between the size of the FT input coil and MRI sensitivity per voxel. Multi-channel acquisition is a well-known solution to increase FOV without significantly reducing sensitivity. In this paper, we demonstrate twofold FOV increase with the use of three FT input coils. We also show that it is possible to use a single atomic magnetometer and single acquisition channel to acquire three independent MRI signals by applying a frequency-encoding gradient along the direction of the detection array span. The approach can be generalized to more channels and can be critical for imaging applications of non-cryogenic ULF MRI where FOV needs to be large, including head, hand, spine, and whole-body imaging. Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-flux-transformer MRI detection with an atomic magnetometer

PubMed Central

Savukov, Igor; Karaulanov, Todor

2014-01-01

Recently, anatomical ultra-low field (ULF) MRI has been demonstrated with an atomic magnetometer (AM). A flux-transformer (FT) has been used for decoupling MRI fields and gradients to avoid their negative effects on AM performance. The field of view (FOV) was limited because of the need to compromise between the size of the FT input coil and MRI sensitivity per voxel. Multi-channel acquisition is a well-known solution to increase FOV without significantly reducing sensitivity. In this paper, we demonstrate two-fold FOV increase with the use of three FT input coils. We also show that it is possible to use a single atomic magnetometer and single acquisition channel to acquire three independent MRI signals by applying a frequency-encoding gradient along the direction of the detection array span. The approach can be generalized to more channels and can be critical for imaging applications of non-cryogenic ULF MRI where FOV needs to be large, including head, hand, spine, and whole-body imaging. PMID:25462946

Design of feedback control systems for stable plants with saturating actuators

NASA Technical Reports Server (NTRS)

Kapasouris, Petros; Athans, Michael; Stein, Gunter

1988-01-01

A systematic control design methodology is introduced for multi-input/multi-output stable open loop plants with multiple saturations. This new methodology is a substantial improvement over previous heuristic single-input/single-output approaches. The idea is to introduce a supervisor loop so that when the references and/or disturbances are sufficiently small, the control system operates linearly as designed. For signals large enough to cause saturations, the control law is modified in such a way as to ensure stability and to preserve, to the extent possible, the behavior of the linear control design. Key benefits of the methodology are: the modified compensator never produces saturating control signals, integrators and/or slow dynamics in the compensator never windup, the directional properties of the controls are maintained, and the closed loop system has certain guaranteed stability properties. The advantages of the new design methodology are illustrated in the simulation of an academic example and the simulation of the multivariable longitudinal control of a modified model of the F-8 aircraft.

Modeling and control for vibration suppression of a flexible smart structure

NASA Technical Reports Server (NTRS)

Dosch, J.; Leo, D.; Inman, D.

1993-01-01

Theoretical and experimental results of the modeling and control of a flexible ribbed antenna are presented. The antenna consists of eight flexible ribs which constitutes a smart antenna in the sense that the actuator and sensors are an integral part of the structure. The antenna exhibits closely space and repeated modes, thus multi-input multi-output (MIMO) control is necessary for controllability and observability of the structure. The structure also exhibits mode localization phenomenon and contains post buckled members making an accurate finite element model of the structure difficult to obtain. An identified MIMO minimum order model of the antenna is synthesized from identified single-input single-output (SISO) transfer functions curve fit in the frequency domain. The identified model is used to design a positive position feedback (PPF) controller that increases damping in all of the modes in the targeted frequency range. Due to the accuracy of the open loop model of the antenna, the closed loop response predicted by the identified model correlates well wtih experimental results.

Lower-Order Compensation Chain Threshold-Reduction Technique for Multi-Stage Voltage Multipliers.

PubMed

Dell' Anna, Francesco; Dong, Tao; Li, Ping; Wen, Yumei; Azadmehr, Mehdi; Casu, Mario; Berg, Yngvar

2018-04-17

This paper presents a novel threshold-compensation technique for multi-stage voltage multipliers employed in low power applications such as passive and autonomous wireless sensing nodes (WSNs) powered by energy harvesters. The proposed threshold-reduction technique enables a topological design methodology which, through an optimum control of the trade-off among transistor conductivity and leakage losses, is aimed at maximizing the voltage conversion efficiency (VCE) for a given ac input signal and physical chip area occupation. The conducted simulations positively assert the validity of the proposed design methodology, emphasizing the exploitable design space yielded by the transistor connection scheme in the voltage multiplier chain. An experimental validation and comparison of threshold-compensation techniques was performed, adopting 2N5247 N-channel junction field effect transistors (JFETs) for the realization of the voltage multiplier prototypes. The attained measurements clearly support the effectiveness of the proposed threshold-reduction approach, which can significantly reduce the chip area occupation for a given target output performance and ac input signal.

Nonlinear aeroservoelastic analysis of a controlled multiple-actuated-wing model with free-play

NASA Astrophysics Data System (ADS)

Huang, Rui; Hu, Haiyan; Zhao, Yonghui

2013-10-01

In this paper, the effects of structural nonlinearity due to free-play in both leading-edge and trailing-edge outboard control surfaces on the linear flutter control system are analyzed for an aeroelastic model of three-dimensional multiple-actuated-wing. The free-play nonlinearities in the control surfaces are modeled theoretically by using the fictitious mass approach. The nonlinear aeroelastic equations of the presented model can be divided into nine sub-linear modal-based aeroelastic equations according to the different combinations of deflections of the leading-edge and trailing-edge outboard control surfaces. The nonlinear aeroelastic responses can be computed based on these sub-linear aeroelastic systems. To demonstrate the effects of nonlinearity on the linear flutter control system, a single-input and single-output controller and a multi-input and multi-output controller are designed based on the unconstrained optimization techniques. The numerical results indicate that the free-play nonlinearity can lead to either limit cycle oscillations or divergent motions when the linear control system is implemented.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

Demonstration of a robust magnonic spin wave interferometer.

PubMed

Kanazawa, Naoki; Goto, Taichi; Sekiguchi, Koji; Granovsky, Alexander B; Ross, Caroline A; Takagi, Hiroyuki; Nakamura, Yuichi; Inoue, Mitsuteru

2016-07-22

Magnonics is an emerging field dealing with ultralow power consumption logic circuits, in which the flow of spin waves, rather than electric charges, transmits and processes information. Waves, including spin waves, excel at encoding information via their phase using interference. This enables a number of inputs to be processed in one device, which offers the promise of multi-input multi-output logic gates. To realize such an integrated device, it is essential to demonstrate spin wave interferometers using spatially isotropic spin waves with high operational stability. However, spin wave reflection at the waveguide edge has previously limited the stability of interfering waves, precluding the use of isotropic spin waves, i.e., forward volume waves. Here, a spin wave absorber is demonstrated comprising a yttrium iron garnet waveguide partially covered by gold. This device is shown experimentally to be a robust spin wave interferometer using the forward volume mode, with a large ON/OFF isolation value of 13.7 dB even in magnetic fields over 30 Oe.

Demonstration of a robust magnonic spin wave interferometer

PubMed Central

Kanazawa, Naoki; Goto, Taichi; Sekiguchi, Koji; Granovsky, Alexander B.; Ross, Caroline A.; Takagi, Hiroyuki; Nakamura, Yuichi; Inoue, Mitsuteru

2016-01-01

Magnonics is an emerging field dealing with ultralow power consumption logic circuits, in which the flow of spin waves, rather than electric charges, transmits and processes information. Waves, including spin waves, excel at encoding information via their phase using interference. This enables a number of inputs to be processed in one device, which offers the promise of multi-input multi-output logic gates. To realize such an integrated device, it is essential to demonstrate spin wave interferometers using spatially isotropic spin waves with high operational stability. However, spin wave reflection at the waveguide edge has previously limited the stability of interfering waves, precluding the use of isotropic spin waves, i.e., forward volume waves. Here, a spin wave absorber is demonstrated comprising a yttrium iron garnet waveguide partially covered by gold. This device is shown experimentally to be a robust spin wave interferometer using the forward volume mode, with a large ON/OFF isolation value of 13.7 dB even in magnetic fields over 30 Oe. PMID:27443989

Graphene-ferroelectric metadevices for nonvolatile memory and reconfigurable logic-gate operations

NASA Astrophysics Data System (ADS)

Kim, Woo Young; Kim, Hyeon-Don; Kim, Teun-Teun; Park, Hyun-Sung; Lee, Kanghee; Choi, Hyun Joo; Lee, Seung Hoon; Son, Jaehyeon; Park, Namkyoo; Min, Bumki

2016-01-01

Memory metamaterials are artificial media that sustain transformed electromagnetic properties without persistent external stimuli. Previous memory metamaterials were realized with phase-change materials, such as vanadium dioxide or chalcogenide glasses, which exhibit memory behaviour with respect to electrically/optically induced thermal stimuli. However, they require a thermally isolated environment for longer retention or strong optical pump for phase-change. Here we demonstrate electrically programmable nonvolatile memory metadevices realised by the hybridization of graphene, a ferroelectric and meta-atoms/meta-molecules, and extend the concept further to establish reconfigurable logic-gate metadevices. For a memory metadevice having a single electrical input, amplitude, phase and even the polarization multi-states were clearly distinguishable with a retention time of over 10 years at room temperature. Furthermore, logic-gate functionalities were demonstrated with reconfigurable logic-gate metadevices having two electrical inputs, with each connected to separate ferroelectric layers that act as the multi-level controller for the doping level of the sandwiched graphene layer.

Multi-objective experimental design for (13)C-based metabolic flux analysis.

PubMed

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

BYMUR software: a free and open source tool for quantifying and visualizing multi-risk analyses

NASA Astrophysics Data System (ADS)

Tonini, Roberto; Selva, Jacopo

2013-04-01

The BYMUR software aims to provide an easy-to-use open source tool for both computing multi-risk and managing/visualizing/comparing all the inputs (e.g. hazard, fragilities and exposure) as well as the corresponding results (e.g. risk curves, risk indexes). For all inputs, a complete management of inter-model epistemic uncertainty is considered. The BYMUR software will be one of the final products provided by the homonymous ByMuR project (http://bymur.bo.ingv.it/) funded by Italian Ministry of Education, Universities and Research (MIUR), focused to (i) provide a quantitative and objective general method for a comprehensive long-term multi-risk analysis in a given area, accounting for inter-model epistemic uncertainty through Bayesian methodologies, and (ii) apply the methodology to seismic, volcanic and tsunami risks in Naples (Italy). More specifically, the BYMUR software will be able to separately account for the probabilistic hazard assessment of different kind of hazardous phenomena, the relative (time-dependent/independent) vulnerabilities and exposure data, and their possible (predefined) interactions: the software will analyze these inputs and will use them to estimate both single- and multi- risk associated to a specific target area. In addition, it will be possible to connect the software to further tools (e.g., a full hazard analysis), allowing a dynamic I/O of results. The use of Python programming language guarantees that the final software will be open source and platform independent. Moreover, thanks to the integration of some most popular and rich-featured Python scientific modules (Numpy, Matplotlib, Scipy) with the wxPython graphical user toolkit, the final tool will be equipped with a comprehensive Graphical User Interface (GUI) able to control and visualize (in the form of tables, maps and/or plots) any stage of the multi-risk analysis. The additional features of importing/exporting data in MySQL databases and/or standard XML formats (for instance, the global standards defined in the frame of GEM project for seismic hazard and risk) will grant the interoperability with other FOSS software and tools and, at the same time, to be on hand of the geo-scientific community. An already available example of connection is represented by the BET_VH(**) tool, which probabilistic volcanic hazard outputs will be used as input for BYMUR. Finally, the prototype version of BYMUR will be used for the case study of the municipality of Naples, by considering three different natural hazards (volcanic eruptions, earthquakes and tsunamis) and by assessing the consequent long-term risk evaluation. (**)BET_VH (Bayesian Event Tree for Volcanic Hazard) is probabilistic tool for long-term volcanic hazard assessment, recently re-designed and adjusted to be run on the Vhub cyber-infrastructure, a free web-based collaborative tool in volcanology research (see http://vhub.org/resources/betvh).

Modular Multi-Sensor Display System Design Study. Volume 2. Detail Design and Application Analysis

DTIC Science & Technology

1974-08-01

control grid . 2. Horizontal AFC/Deflection Module - Generates horizontal sweep signals from input syncs to provide 525 to 1023 line television raster...separation, and gener- ate composite blanking for the CRT control grid . Signal Number of Lines Signal Type Characteristics Input Interface Composite...SEPERATOR DC RESTORA- TION l_i BLANKING VERT DRIVE ■♦ Bl" CRT " CATHODE * _fc> BRIGHTNESS ^ (FRONT PANEL) .CRT GRID ■♦• COMP SYNC Figure

Evolution of Bow-Tie Architectures in Biology

PubMed Central

Friedlander, Tamar; Mayo, Avraham E.; Tlusty, Tsvi; Alon, Uri

2015-01-01

Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved. PMID:25798588

Demand-driven energy requirement of world economy 2007: A multi-region input-output network simulation

NASA Astrophysics Data System (ADS)

Chen, Zhan-Ming; Chen, G. Q.

2013-07-01

This study presents a network simulation of the global embodied energy flows in 2007 based on a multi-region input-output model. The world economy is portrayed as a 6384-node network and the energy interactions between any two nodes are calculated and analyzed. According to the results, about 70% of the world's direct energy input is invested in resource, heavy manufacture, and transportation sectors which provide only 30% of the embodied energy to satisfy final demand. By contrast, non-transportation services sectors contribute to 24% of the world's demand-driven energy requirement with only 6% of the direct energy input. Commodity trade is shown to be an important alternative to fuel trade in redistributing energy, as international commodity flows embody 1.74E + 20 J of energy in magnitude up to 89% of the traded fuels. China is the largest embodied energy exporter with a net export of 3.26E + 19 J, in contrast to the United States as the largest importer with a net import of 2.50E + 19 J. The recent economic fluctuations following the financial crisis accelerate the relative expansions of energy requirement by developing countries, as a consequence China will take over the place of the United States as the world's top demand-driven energy consumer in 2022 and India will become the third largest in 2015.

78 FR 40132 - Wave Energy Converter Prize Administration Webinar

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

... any potential FOA to seek input from the public regarding possible approaches to structuring a prize... encourages commenters to provide alternative strategies and approaches for the prize administration. A multi stage-gate challenge structure requires competitors to pass through a series of stage gates based on...

Bountiful health care and A Beautiful Mind.

PubMed

Flower, Joe

2002-01-01

The Nash Equilibrium (put forward by John Nash and celebrated in the film, A Beautiful Mind) mathematically describes multi-player, infinite games. It is a general description of large human transactions, such as health care. Learn how new inputs, new players or new resources can disrupt these transactions.

Scientific Visualization of Radio Astronomy Data using Gesture Interaction

NASA Astrophysics Data System (ADS)

Mulumba, P.; Gain, J.; Marais, P.; Woudt, P.

2015-09-01

MeerKAT in South Africa (Meer = More Karoo Array Telescope) will require software to help visualize, interpret and interact with multidimensional data. While visualization of multi-dimensional data is a well explored topic, little work has been published on the design of intuitive interfaces to such systems. More specifically, the use of non-traditional interfaces (such as motion tracking and multi-touch) has not been widely investigated within the context of visualizing astronomy data. We hypothesize that a natural user interface would allow for easier data exploration which would in turn lead to certain kinds of visualizations (volumetric, multidimensional). To this end, we have developed a multi-platform scientific visualization system for FITS spectral data cubes using VTK (Visualization Toolkit) and a natural user interface to explore the interaction between a gesture input device and multidimensional data space. Our system supports visual transformations (translation, rotation and scaling) as well as sub-volume extraction and arbitrary slicing of 3D volumetric data. These tasks were implemented across three prototypes aimed at exploring different interaction strategies: standard (mouse/keyboard) interaction, volumetric gesture tracking (Leap Motion controller) and multi-touch interaction (multi-touch monitor). A Heuristic Evaluation revealed that the volumetric gesture tracking prototype shows great promise for interfacing with the depth component (z-axis) of 3D volumetric space across multiple transformations. However, this is limited by users needing to remember the required gestures. In comparison, the touch-based gesture navigation is typically more familiar to users as these gestures were engineered from standard multi-touch actions. Future work will address a complete usability test to evaluate and compare the different interaction modalities against the different visualization tasks.

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

NASA Astrophysics Data System (ADS)

He, Xin; Frey, Eric C.

2007-03-01

Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

Joint explorative analysis of neuroreceptor subsystems in the human brain: application to receptor-transporter correlation using PET data.

PubMed

Cselényi, Zsolt; Lundberg, Johan; Halldin, Christer; Farde, Lars; Gulyás, Balázs

2004-10-01

Positron emission tomography (PET) has proved to be a highly successful technique in the qualitative and quantitative exploration of the human brain's neurotransmitter-receptor systems. In recent years, the number of PET radioligands, targeted to different neuroreceptor systems of the human brain, has increased considerably. This development paves the way for a simultaneous analysis of different receptor systems and subsystems in the same individual. The detailed exploration of the versatility of neuroreceptor systems requires novel technical approaches, capable of operating on huge parametric image datasets. An initial step of such explorative data processing and analysis should be the development of novel exploratory data-mining tools to gain insight into the "structure" of complex multi-individual, multi-receptor data sets. For practical reasons, a possible and feasible starting point of multi-receptor research can be the analysis of the pre- and post-synaptic binding sites of the same neurotransmitter. In the present study, we propose an unsupervised, unbiased data-mining tool for this task and demonstrate its usefulness by using quantitative receptor maps, obtained with positron emission tomography, from five healthy subjects on (pre-synaptic) serotonin transporters (5-HTT or SERT) and (post-synaptic) 5-HT(1A) receptors. Major components of the proposed technique include the projection of the input receptor maps to a feature space, the quasi-clustering and classification of projected data (neighbourhood formation), trans-individual analysis of neighbourhood properties (trajectory analysis), and the back-projection of the results of trajectory analysis to normal space (creation of multi-receptor maps). The resulting multi-receptor maps suggest that complex relationships and tendencies in the relationship between pre- and post-synaptic transporter-receptor systems can be revealed and classified by using this method. As an example, we demonstrate the regional correlation of the serotonin transporter-receptor systems. These parameter-specific multi-receptor maps can usefully guide the researchers in their endeavour to formulate models of multi-receptor interactions and changes in the human brain.

Long-term autonomous volcanic gas monitoring with Multi-GAS at Mount St. Helens, Washington, and Augustine Volcano, Alaska

NASA Astrophysics Data System (ADS)

Kelly, P. J.; Ketner, D. M.; Kern, C.; Lahusen, R. G.; Lockett, C.; Parker, T.; Paskievitch, J.; Pauk, B.; Rinehart, A.; Werner, C. A.

2015-12-01

In recent years, the USGS Volcano Hazards Program has worked to implement continuous real-time in situ volcanic gas monitoring at volcanoes in the Cascade Range and Alaska. The main goal of this ongoing effort is to better link the compositions of volcanic gases to other real-time monitoring data, such as seismicity and deformation, in order to improve baseline monitoring and early detection of volcanic unrest. Due to the remote and difficult-to-access nature of volcanic-gas monitoring sites in the Cascades and Alaska, we developed Multi-GAS instruments that can operate unattended for long periods of time with minimal direct maintenance from field personnel. Our Multi-GAS stations measure H2O, CO2, SO2, and H2S gas concentrations, are comprised entirely of commercial off-the-shelf components, and are powered by small solar energy systems. One notable feature of our Multi-GAS stations is that they include a unique capability to perform automated CO2, SO2, and H2S sensor verifications using portable gas standards while deployed in the field, thereby allowing for rigorous tracking of sensor performances. In addition, we have developed novel onboard data-processing routines that allow diagnostic and monitoring data - including gas ratios (e.g. CO2/SO2) - to be streamed in real time to internal observatory and public web pages without user input. Here we present over one year of continuous data from a permanent Multi-GAS station installed in August 2014 in the crater of Mount St. Helens, Washington, and several months of data from a station installed near the summit of Augustine Volcano, Alaska in June 2015. Data from the Mount St. Helens Multi-GAS station has been streaming to a public USGS site since early 2015, a first for a permanent Multi-GAS site. Neither station has detected significant changes in gas concentrations or compositions since they were installed, consistent with low levels of seismicity and deformation.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Multi-objective thermodynamic optimisation of supercritical CO2 Brayton cycles integrated with solar central receivers

NASA Astrophysics Data System (ADS)

Vasquez Padilla, Ricardo; Soo Too, Yen Chean; Benito, Regano; McNaughton, Robbie; Stein, Wes

2018-01-01

In this paper, optimisation of the supercritical CO? Brayton cycles integrated with a solar receiver, which provides heat input to the cycle, was performed. Four S-CO? Brayton cycle configurations were analysed and optimum operating conditions were obtained by using a multi-objective thermodynamic optimisation. Four different sets, each including two objective parameters, were considered individually. The individual multi-objective optimisation was performed by using Non-dominated Sorting Genetic Algorithm. The effect of reheating, solar receiver pressure drop and cycle parameters on the overall exergy and cycle thermal efficiency was analysed. The results showed that, for all configurations, the overall exergy efficiency of the solarised systems achieved at maximum value between 700°C and 750°C and the optimum value is adversely affected by the solar receiver pressure drop. In addition, the optimum cycle high pressure was in the range of 24.2-25.9 MPa, depending on the configurations and reheat condition.

A capacitive CMOS-MEMS sensor designed by multi-physics simulation for integrated CMOS-MEMS technology

NASA Astrophysics Data System (ADS)

Konishi, Toshifumi; Yamane, Daisuke; Matsushima, Takaaki; Masu, Kazuya; Machida, Katsuyuki; Toshiyoshi, Hiroshi

2014-01-01

This paper reports the design and evaluation results of a capacitive CMOS-MEMS sensor that consists of the proposed sensor circuit and a capacitive MEMS device implemented on the circuit. To design a capacitive CMOS-MEMS sensor, a multi-physics simulation of the electromechanical behavior of both the MEMS structure and the sensing LSI was carried out simultaneously. In order to verify the validity of the design, we applied the capacitive CMOS-MEMS sensor to a MEMS accelerometer implemented by the post-CMOS process onto a 0.35-µm CMOS circuit. The experimental results of the CMOS-MEMS accelerometer exhibited good agreement with the simulation results within the input acceleration range between 0.5 and 6 G (1 G = 9.8 m/s2), corresponding to the output voltages between 908.6 and 915.4 mV, respectively. Therefore, we have confirmed that our capacitive CMOS-MEMS sensor and the multi-physics simulation will be beneficial method to realize integrated CMOS-MEMS technology.

Dedicated multichannel readout ASIC coupled with single crystal diamond for dosimeter application

NASA Astrophysics Data System (ADS)

Fabbri, A.; Falco, M. D.; De Notaristefani, F.; Galasso, M.; Marinelli, M.; Orsolini Cencelli, V.; Tortora, L.; Verona, C.; Verona Rinati, G.

2013-02-01

This paper reports on the tests of a low-noise, multi-channel readout integrated circuit used as a readout electronic front-end for a diamond multi-pixel dosimeter. The system is developed for dose distribution measurement in radiotherapy applications. The first 10-channel prototype chip was designed and fabricated in a 0.18 um CMOS process. Every channel includes a charge integrator with a 10 pF capacitor and a double slope A/D converter. The diamond multi-pixel detector, based on CVD synthetic single crystal diamond Schottky diodes, is made by a 3 × 3 sensor matrix. The overall device has been tested under irradiation with 6 MeV radio therapeutic photon beams at the Policlinico ``Tor Vergata'' (PTV) hospital. Measurements show a 20 fA RMS leakage current from the front-end input stage and a negligible dark current from the diamond detector, a stable temporal response and a good linear behaviour as a function of both dose and dose rate. These characteristics were common to each tested channel.

Performance analysis of visible light communication using the STBC-OFDM technique for intelligent transportation systems

NASA Astrophysics Data System (ADS)

Li, Changping; Yi, Ying; Lee, Kyujin; Lee, Kyesan

2014-08-01

Visible light communication (VLC) applied in an intelligent transportation system (ITS) has attracted growing attentions, but it also faces challenges, for example deep path loss and optical multi-path dispersion. In this work, we modelled an actual outdoor optical channel as a Rician channel and further proposed space-time block coding (STBC) orthogonal frequency-division multiplexing (OFDM) technology to reduce the influence of severe optical multi-path dispersion associated with such a mock channel for achieving the effective BER of 10-6 even at a low signal-to-noise ratio (SNR). In this case, the optical signals transmission distance can be extended as long as possible. Through the simulation results of STBC-OFDM and single-input-single-output (SISO) counterparts in bit error rate (BER) performance comparison, we can distinctly observe that the VLC-ITS system using STBC-OFDM technique can obtain a strongly improved BER performance due to multi-path dispersion alleviation.

Neural networks within multi-core optic fibers

PubMed Central

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-01-01

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. PMID:27383911

Investigation of an X-band gigawatt long pulse multi-beam relativistic klystron amplifier

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

Liu, Zhenbang; Huang, Hua; Lei, Lurong

2015-09-15

To achieve a gigawatt-level long pulse radiation power in X-band, a multi-beam relativistic klystron amplifier is proposed and studied experimentally. By introducing 18 electron drift tubes and extended interaction cavities, the power capacity of the device is increased. A radiation power of 1.23 GW with efficiency of 41% and amplifier gain of 46 dB is obtained in the particle-in-cell simulation. Under conditions of a 10 Hz repeat frequency and an input RF power of 30 kW, a radiation power of 0.9 GW, frequency of 9.405 GHz, pulse duration of 105 ns, and efficiency of 30% is generated in the experiment, and the amplifier gain is aboutmore » 45 dB. Both the simulation and the experiment prove that the multi-beam relativistic klystron amplifier can generate a long pulse GW-level radiation power in X-band.« less

Neural networks within multi-core optic fibers.

PubMed

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Real-time digital signal recovery for a multi-pole low-pass transfer function system.

PubMed

Lee, Jhinhwan

2017-08-01

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with multi-pole linear low-pass transfer characteristics, a simple digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. A mathematical analysis on the convolution kernel representation of the single-pole low-pass transfer function shows that the original source waveform can be accurately recovered in real time using a particular moving average algorithm applied on the input stream of the distorted waveform, which can also significantly reduce the overall delay time constant. This method is generalized for multi-pole low-pass systems and has noise characteristics of the inverse of the low-pass filter characteristics. This method can be applied to most sensors and amplifiers operating close to their frequency response limits to improve the overall performance of data acquisition systems and digital feedback control systems.

Dual adaptive control: Design principles and applications

NASA Technical Reports Server (NTRS)

Mookerjee, Purusottam

1988-01-01

The design of an actively adaptive dual controller based on an approximation of the stochastic dynamic programming equation for a multi-step horizon is presented. A dual controller that can enhance identification of the system while controlling it at the same time is derived for multi-dimensional problems. This dual controller uses sensitivity functions of the expected future cost with respect to the parameter uncertainties. A passively adaptive cautious controller and the actively adaptive dual controller are examined. In many instances, the cautious controller is seen to turn off while the latter avoids the turn-off of the control and the slow convergence of the parameter estimates, characteristic of the cautious controller. The algorithms have been applied to a multi-variable static model which represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. Monte Carlo comparisons based on parametric and nonparametric statistical analysis indicate the superiority of the dual controller over the baseline controller.

A Model for Generating Multi-hazard Scenarios

NASA Astrophysics Data System (ADS)

Lo Jacomo, A.; Han, D.; Champneys, A.

2017-12-01

Communities in mountain areas are often subject to risk from multiple hazards, such as earthquakes, landslides, and floods. Each hazard has its own different rate of onset, duration, and return period. Multiple hazards tend to complicate the combined risk due to their interactions. Prioritising interventions for minimising risk in this context is challenging. We developed a probabilistic multi-hazard model to help inform decision making in multi-hazard areas. The model is applied to a case study region in the Sichuan province in China, using information from satellite imagery and in-situ data. The model is not intended as a predictive model, but rather as a tool which takes stakeholder input and can be used to explore plausible hazard scenarios over time. By using a Monte Carlo framework and varrying uncertain parameters for each of the hazards, the model can be used to explore the effect of different mitigation interventions aimed at reducing the disaster risk within an uncertain hazard context.

Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization.

PubMed

Linguraru, Marius George; Pura, John A; Chowdhury, Ananda S; Summers, Ronald M

2010-01-01

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D erosion using population historic information of contrast-enhanced liver, spleen, and kidneys was applied to multi-phase data to initialize the 4D graph and adapt to patient specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance and enhancement, and shape and location on organ segmentation.

Multi-sources data fusion framework for remote triage prioritization in telehealth.

PubMed

Salman, O H; Rasid, M F A; Saripan, M I; Subramaniam, S K

2014-09-01

The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely.

Command Filtering-Based Fuzzy Control for Nonlinear Systems With Saturation Input.

PubMed

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Lin, Chong

2017-09-01

In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.

Adaptive/learning control of large space structures - System identification techniques. [for multi-configuration flexible spacecraft

NASA Technical Reports Server (NTRS)

Thau, F. E.; Montgomery, R. C.

1980-01-01

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

Control law synthesis and optimization software for large order aeroservoelastic systems

NASA Technical Reports Server (NTRS)

Mukhopadhyay, V.; Pototzky, A.; Noll, Thomas

1989-01-01

A flexible aircraft or space structure with active control is typically modeled by a large-order state space system of equations in order to accurately represent the rigid and flexible body modes, unsteady aerodynamic forces, actuator dynamics and gust spectra. The control law of this multi-input/multi-output (MIMO) system is expected to satisfy multiple design requirements on the dynamic loads, responses, actuator deflection and rate limitations, as well as maintain certain stability margins, yet should be simple enough to be implemented on an onboard digital microprocessor. A software package for performing an analog or digital control law synthesis for such a system, using optimal control theory and constrained optimization techniques is described.

Using the OCLC union listing component for a statewide health sciences union list of serials.

PubMed Central

Sutton, L S; Wolfgram, P A

1986-01-01

Union lists of serials are critical to the effective operation of interlibrary loan networks. The Michigan Health Sciences Libraries Association used the OCLC union list component to produce the Michigan Statewide Health Sciences Union List of Serials (MISHULS). MISHULS, which includes the serials holdings of ninety-three hospital health sciences libraries, is a subset of a statewide multi-type union list maintained on OCLC. The rationale for a statewide list and the criteria for choosing vendors are discussed. Typical costs are provided. Funding sources are identified and a unique approach to decentralized input is described. The benefits of resource sharing in a larger, multi-type library network are also explored. PMID:3708192

Using the OCLC union listing component for a statewide health sciences union list of serials.

PubMed

Sutton, L S; Wolfgram, P A

1986-04-01

Union lists of serials are critical to the effective operation of interlibrary loan networks. The Michigan Health Sciences Libraries Association used the OCLC union list component to produce the Michigan Statewide Health Sciences Union List of Serials (MISHULS). MISHULS, which includes the serials holdings of ninety-three hospital health sciences libraries, is a subset of a statewide multi-type union list maintained on OCLC. The rationale for a statewide list and the criteria for choosing vendors are discussed. Typical costs are provided. Funding sources are identified and a unique approach to decentralized input is described. The benefits of resource sharing in a larger, multi-type library network are also explored.

Robust, Decoupled, Flight Control Design with Rate Saturating Actuators

NASA Technical Reports Server (NTRS)

Snell, S. A.; Hess, R. A.

1997-01-01

Techniques for the design of control systems for manually controlled, high-performance aircraft must provide the following: (1) multi-input, multi-output (MIMO) solutions, (2) acceptable handling qualities including no tendencies for pilot-induced oscillations, (3) a tractable approach for compensator design, (4) performance and stability robustness in the presence of significant plant uncertainty, and (5) performance and stability robustness in the presence actuator saturation (particularly rate saturation). A design technique built upon Quantitative Feedback Theory is offered as a candidate methodology which can provide flight control systems meeting these requirements, and do so over a considerable part of the flight envelope. An example utilizing a simplified model of a supermaneuverable fighter aircraft demonstrates the proposed design methodology.

Advanced wireless mobile collaborative sensing network for tactical and strategic missions

NASA Astrophysics Data System (ADS)

Xu, Hao

2017-05-01

In this paper, an advanced wireless mobile collaborative sensing network will be developed. Through properly combining wireless sensor network, emerging mobile robots and multi-antenna sensing/communication techniques, we could demonstrate superiority of developed sensing network. To be concrete, heterogeneous mobile robots including unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) are equipped with multi-model sensors and wireless transceiver antennas. Through real-time collaborative formation control, multiple mobile robots can team the best formation that can provide most accurate sensing results. Also, formatting multiple mobile robots can also construct a multiple-input multiple-output (MIMO) communication system that can provide a reliable and high performance communication network.

Membrane Specifications for Multi-Configuration Membrane Distillation Model

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

Villa, Daniel; Vanneste, Johan; Cath, Tzahi

The data includes the membrane properties and specifications used for multi-configuration membrane distillation desalination. In this study, membranes from CLARCOR, 3M, and Aquastill are tested in counter-current, co-current and air-gap configurations at Colorado School of Mines (CSM), Advanced Water technology Center ( Aqwatech) laboratories. In the data sheets: The "theoretical" worksheet, contains steady-state values of the experimental runs and also provides several calculated values. The "Specifications" worksheet contains the inputs to the experiment. The "Data" spreadsheet contains the entire set of data and the rest of the sheets "20-40", "20-45", ...etc., contain individual portions of the data with variation ofmore » feed temperatures.« less

Aeroservoelastic wind-tunnel investigations using the Active Flexible Wing Model: Status and recent accomplishments

NASA Technical Reports Server (NTRS)

Noll, Thomas E.; Perry, Boyd, III; Tiffany, Sherwood H.; Cole, Stanley R.; Buttrill, Carey S.; Adams, William M., Jr.; Houck, Jacob A.; Srinathkumar, S.; Mukhopadhyay, Vivek; Pototzky, Anthony S.

1989-01-01

The status of the joint NASA/Rockwell Active Flexible Wing Wind-Tunnel Test Program is described. The objectives are to develop and validate the analysis, design, and test methodologies required to apply multifunction active control technology for improving aircraft performance and stability. Major tasks include designing digital multi-input/multi-output flutter-suppression and rolling-maneuver-load alleviation concepts for a flexible full-span wind-tunnel model, obtaining an experimental data base for the basic model and each control concept and providing comparisons between experimental and analytical results to validate the methodologies. The opportunity is provided to improve real-time simulation techniques and to gain practical experience with digital control law implementation procedures.

Automatic insulation resistance testing apparatus

DOEpatents

Wyant, Francis J.; Nowlen, Steven P.; Luker, Spencer M.

2005-06-14

An apparatus and method for automatic measurement of insulation resistances of a multi-conductor cable. In one embodiment of the invention, the apparatus comprises a power supply source, an input measuring means, an output measuring means, a plurality of input relay controlled contacts, a plurality of output relay controlled contacts, a relay controller and a computer. In another embodiment of the invention the apparatus comprises a power supply source, an input measuring means, an output measuring means, an input switching unit, an output switching unit and a control unit/data logger. Embodiments of the apparatus of the invention may also incorporate cable fire testing means. The apparatus and methods of the present invention use either voltage or current for input and output measured variables.

Multi-Sensory Input in the Non-Academic ESL Classroom.

ERIC Educational Resources Information Center

Bassano, Sharron

Teaching approaches for adult English as second language students with little previous formal education or native language literacy cannot rely on the traditional written materials. For students who cannot be reached through the written word, approaches must be devised that engage other channels of perceptions. Classroom activities are suggested…

Multi-Layered Feedforward Neural Networks for Image Segmentation

DTIC Science & Technology

1991-12-01

the Gram-Schmidt Network ...................... 80 xi Preface WILLIAM SHAKESPEARE 1564-1616 Is this a dagger which I see before me, The handle toward...any input-output mapping with a single hidden layer of non-linear nodes, the result may be like proving that a monkey could write Hamlet . Certainly it

DOTAGWA: A CASE STUDY IN WEB-BASED ARCHITECTURES FOR CONNECTING SURFACE WATER MODELS TO SPATIALLY ENABLED WEB APPLICATIONS

EPA Science Inventory

The Automated Geospatial Watershed Assessment (AGWA) tool is a desktop application that uses widely available standardized spatial datasets to derive inputs for multi-scale hydrologic models (Miller et al., 2007). The required data sets include topography (DEM data), soils, clima...

Developing a multi-modal freight movement plan for the sustainable growth of wind energy related industries.

DOT National Transportation Integrated Search

2012-06-01

As the demand for wind power increases so too has the popularity of larger and larger turbines. Larger turbines are able to : produce more power for the input they receive but they are also much more difficult to transport. This project examines thes...

Quality Assurance Decisions with Air Models: A Case Study of ImputatIon of Missing Input Data Using EPA's Multi-Layer Model

EPA Science Inventory

Abstract Environmental models are frequently used within regulatory and policy frameworks to estimate environmental metrics that are difficult or impossible to physically measure. As important decision tools, the uncertainty associated with the model outputs should impact their ...

Multi-Dimensional Planning/Evaluation Schema for Community Education.

ERIC Educational Resources Information Center

Merkel-Keller, Claudia; Herr, Audrey

A model for planning and evaluating community education programs--Stufflebeam's context, input, process, product (CIPP) evaluation model--was described and field-tested with the community education programs in Lakewood, New Jersey. Community education was defined as a concern for everything that affects the well-being of all citizens within a…

'Commitment' secures rooftop SSD contract.

PubMed

Davies, John

2010-01-01

John Davies, operations manager of multi-disciplinary mechanical and electrical and construction contractor cfes, describes a "turnkey" project that the company is undertaking for the Yeovil District Hospital (YDH) NHS Foundation Trust, involving both the design and construction of a new sterile services department, and substantial input into the facility's subsequent operation and management.

Analysis of continuous beams with joint slip

Treesearch

L. A. Soltis

1981-01-01

A computer analysis with user guidelines to analyze partially continuous multi-span beams is presented. Partial continuity is due to rotational slip which occurs at spliced joints at the supports of continuous beams such as floor joists. Beam properties, loads, and joint slip are input; internal forces, reactions, and deflections are output.

Applying Recursive Sensitivity Analysis to Multi-Criteria Decision Models to Reduce Bias in Defense Cyber Engineering Analysis

DTIC Science & Technology

2015-10-28

techniques such as regression analysis, correlation, and multicollinearity assessment to identify the change and error on the input to the model...between many of the independent or predictor variables, the issue of multicollinearity may arise [18]. VII. SUMMARY Accurate decisions concerning

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

EPA Science Inventory

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the f...

Using High Frequency Monitoring of Environmental Factors to Predict Microcystin Concentrations in a Multi-use, Inland Reservoir

EPA Science Inventory

Cyanobacteria, known as blue-green algae, are photosynthetic bacteria found naturally in marine, freshwater, and estuarine ecosystems. An increase in nutrient input and changes in the climate have contributed to the proliferation of cyanobacteria, forming harmful algal blooms, or...

Intercropped watermelon for weed suppression in a low-input organic system.

USDA-ARS?s Scientific Manuscript database

A multi-layered canopy provided by intercropping species with different growth forms can provide barriers for the germination of weed seeds and can allow for a more efficient utilization of resources that reduce competition to target crops from weeds. Intercropping can thus be an effective cultural ...

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

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

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Planarity constrained multi-view depth map reconstruction for urban scenes

NASA Astrophysics Data System (ADS)

Hou, Yaolin; Peng, Jianwei; Hu, Zhihua; Tao, Pengjie; Shan, Jie

2018-05-01

Multi-view depth map reconstruction is regarded as a suitable approach for 3D generation of large-scale scenes due to its flexibility and scalability. However, there are challenges when this technique is applied to urban scenes where apparent man-made regular shapes may present. To address this need, this paper proposes a planarity constrained multi-view depth (PMVD) map reconstruction method. Starting with image segmentation and feature matching for each input image, the main procedure is iterative optimization under the constraints of planar geometry and smoothness. A set of candidate local planes are first generated by an extended PatchMatch method. The image matching costs are then computed and aggregated by an adaptive-manifold filter (AMF), whereby the smoothness constraint is applied to adjacent pixels through belief propagation. Finally, multiple criteria are used to eliminate image matching outliers. (Vertical) aerial images, oblique (aerial) images and ground images are used for qualitative and quantitative evaluations. The experiments demonstrated that the PMVD outperforms the popular multi-view depth map reconstruction with an accuracy two times better for the aerial datasets and achieves an outcome comparable to the state-of-the-art for ground images. As expected, PMVD is able to preserve the planarity for piecewise flat structures in urban scenes and restore the edges in depth discontinuous areas.

Stability and structural properties of gene regulation networks with coregulation rules.

PubMed

Warrell, Jonathan; Mhlanga, Musa

2017-05-07

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the dynamical stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model. Copyright © 2017. Published by Elsevier Ltd.

Simulations and Measurements of Human Middle Ear Vibrations Using Multi-Body Systems and Laser-Doppler Vibrometry with the Floating Mass Transducer.

PubMed

Böhnke, Frank; Bretan, Theodor; Lehner, Stefan; Strenger, Tobias

2013-10-22

The transfer characteristic of the human middle ear with an applied middle ear implant (floating mass transducer) is examined computationally with a Multi-body System approach and compared with experimental results. For this purpose, the geometry of the middle ear was reconstructed from μ-computer tomography slice data and prepared for a Multi-body System simulation. The transfer function of the floating mass transducer, which is the ratio of the input voltage and the generated force, is derived based on a physical context. The numerical results obtained with the Multi-body System approach are compared with experimental results by Laser Doppler measurements of the stapes footplate velocities of five different specimens. Although slightly differing anatomical structures were used for the calculation and the measurement, a high correspondence with respect to the course of stapes footplate displacement along the frequency was found. Notably, a notch at frequencies just below 1 kHz occurred. Additionally, phase courses of stapes footplate displacements were determined computationally if possible and compared with experimental results. The examinations were undertaken to quantify stapes footplate displacements in the clinical practice of middle ear implants and, also, to develop fitting strategies on a physical basis for hearing impaired patients aided with middle ear implants.

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI.

PubMed

Kratochvíla, Jiří; Jiřík, Radovan; Bartoš, Michal; Standara, Michal; Starčuk, Zenon; Taxt, Torfinn

2016-03-01

One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. © 2015 Wiley Periodicals, Inc.

Multi-Disciplinary, Multi-Fidelity Discrete Data Transfer Using Degenerate Geometry Forms

NASA Technical Reports Server (NTRS)

Olson, Erik D.

2016-01-01

In a typical multi-fidelity design process, different levels of geometric abstraction are used for different analysis methods, and transitioning from one phase of design to the next often requires a complete re-creation of the geometry. To maintain consistency between lower-order and higher-order analysis results, Vehicle Sketch Pad (OpenVSP) recently introduced the ability to generate and export several degenerate forms of the geometry, representing the type of abstraction required to perform low- to medium-order analysis for a range of aeronautical disciplines. In this research, the functionality of these degenerate models was extended, so that in addition to serving as repositories for the geometric information that is required as input to an analysis, the degenerate models can also store the results of that analysis mapped back onto the geometric nodes. At the same time, the results are also mapped indirectly onto the nodes of lower-order degenerate models using a process called aggregation, and onto higher-order models using a process called disaggregation. The mapped analysis results are available for use by any subsequent analysis in an integrated design and analysis process. A simple multi-fidelity analysis process for a single-aisle subsonic transport aircraft is used as an example case to demonstrate the value of the approach.

Tropical rainforests dominate multi-decadal variability of the global carbon cycle

NASA Astrophysics Data System (ADS)

Zhang, X.; Wang, Y. P.; Peng, S.; Rayner, P. J.; Silver, J.; Ciais, P.; Piao, S.; Zhu, Z.; Lu, X.; Zheng, X.

2017-12-01

Recent studies find that inter-annual variability of global atmosphere-to-land CO2 uptake (NBP) is dominated by semi-arid ecosystems. However, the NBP variations at decadal to multi-decadal timescales are still not known. By developing a basic theory for the role of net primary production (NPP) and heterotrophic respiration (Rh) on NBP and applying it to 100-year simulations of terrestrial ecosystem models forced by observational climate, we find that tropical rainforests dominate the multi-decadal variability of global NBP (48%) rather than the semi-arid lands (35%). The NBP variation at inter-annual timescales is almost 90% contributed by NPP, but across longer timescales is progressively controlled by Rh that constitutes the response from the NPP-derived soil carbon input (40%) and the response of soil carbon turnover rates to climate variability (60%). The NBP variations of tropical rainforests is modulated by the ENSO and the PDO through their significant influences on temperature and precipitation at timescales of 2.5-7 and 25-50 years, respectively. This study highlights the importance of tropical rainforests on the multi-decadal variability of global carbon cycle, suggesting that we need to carefully differentiate the effect of NBP long-term fluctuations associated with ocean-related climate modes on the long-term trend in land sink.

Collaborative identification method for sea battlefield target based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Zheng, Guangdi; Pan, Mingbo; Liu, Wei; Wu, Xuetong

2018-03-01

The target identification of the sea battlefield is the prerequisite for the judgment of the enemy in the modern naval battle. In this paper, a collaborative identification method based on convolution neural network is proposed to identify the typical targets of sea battlefields. Different from the traditional single-input/single-output identification method, the proposed method constructs a multi-input/single-output co-identification architecture based on optimized convolution neural network and weighted D-S evidence theory. The simulation results show that

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

Identification of biomechanical nonlinearity in whole-body vibration using a reverse path multi-input-single-output method

NASA Astrophysics Data System (ADS)

Huang, Ya; Ferguson, Neil S.

2018-04-01

The study implements a classic signal analysis technique, typically applied to structural dynamics, to examine the nonlinear characteristics seen in the apparent mass of a recumbent person during whole-body horizontal random vibration. The nonlinearity in the present context refers to the amount of 'output' that is not correlated or coherent to the 'input', usually indicated by values of the coherence function that are less than unity. The analysis is based on the longitudinal horizontal inline and vertical cross-axis apparent mass of twelve human subjects exposed to 0.25-20 Hz random acceleration vibration at 0.125 and 1.0 ms-2 r.m.s. The conditioned reverse path frequency response functions (FRF) reveal that the uncorrelated 'linear' relationship between physical input (acceleration) and outputs (inline and cross-axis forces) has much greater variation around the primary resonance frequency between 0.5 and 5 Hz. By reversing the input and outputs of the physical system, it is possible to assemble additional mathematical inputs from the physical output forces and mathematical constructs (e.g. square root of inline force). Depending on the specific construct, this can improve the summed multiple coherence at frequencies where the response magnitude is low. In the present case this is between 6 and 20 Hz. The statistical measures of the response force time histories of each of the twelve subjects indicate that there are potential anatomical 'end-stops' for the sprung mass in the inline axis. No previous study has applied this reverse path multi-input-single-output approach to human vibration kinematic and kinetic data before. The implementation demonstrated in the present study will allow new and existing data to be examined using this different analytical tool.

An inductorless multi-mode RF front end for GNSS receiver in 55 nm CMOS

NASA Astrophysics Data System (ADS)

Yanbin, Luo; Chengyan, Ma; Yebing, Gan; Min, Qian; Tianchun, Ye

2015-10-01

An inductorless multi-mode RF front end for a global navigation satellite system (GNSS) receiver is presented. Unlike the traditional topology of a low noise amplifier (LNA), the inductorless current-mode noise-canceling LNA is applied in this design. The high-impedance-input radio frequency amplifier (RFA) further amplifies the GNSS signals and changes the single-end signal path into fully differential. The passive mixer down-converts the signals to the intermediate frequency (IF) band and conveys the signals to the analogue blocks. The local oscillator (LO) buffer divides the output frequency of the voltage controlled oscillator (VCO) and generates 25%-duty-cycle quadrature square waves to drive the mixer. Our measurement results display that the implemented RF front end achieves good overall performance while consuming only 6.7 mA from 1.2 V supply. The input return loss is better than -26 dB and the ultra low noise figure of 1.43 dB leads to high sensitivity of the GNSS receiver. The input 1 dB compression point is -43 dBm at the high gain of 48 dB. The designed circuit is fabricated in 55 nm CMOS technology and the die area, which is much smaller than traditional circuit, is around 220 × 280 μm2.

Effective channel estimation and efficient symbol detection for multi-input multi-output underwater acoustic communications

NASA Astrophysics Data System (ADS)

Ling, Jun

Achieving reliable underwater acoustic communications (UAC) has long been recognized as a challenging problem owing to the scarce bandwidth available and the reverberant spread in both time and frequency domains. To pursue high data rates, we consider a multi-input multi-output (MIMO) UAC system, and our focus is placed on two main issues regarding a MIMO UAC system: (1) channel estimation, which involves the design of the training sequences and the development of a reliable channel estimation algorithm, and (2) symbol detection, which requires interference cancelation schemes due to simultaneous transmission from multiple transducers. To enhance channel estimation performance, we present a cyclic approach for designing training sequences with good auto- and cross-correlation properties, and a channel estimation algorithm called the iterative adaptive approach (IAA). Sparse channel estimates can be obtained by combining IAA with the Bayesian information criterion (BIC). Moreover, we present sparse learning via iterative minimization (SLIM) and demonstrate that SLIM gives similar performance to IAA but at a much lower computational cost. Furthermore, an extension of the SLIM algorithm is introduced to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). Regarding symbol detection, a linear minimum mean-squared error based detection scheme, called RELAX-BLAST, which is a combination of vertical Bell Labs layered space-time (V-BLAST) algorithm and the cyclic principle of the RELAX algorithm, is presented and it is shown that RELAX-BLAST outperforms V-BLAST. We show that RELAX-BLAST can be implemented efficiently by making use of the conjugate gradient method and diagonalization properties of circulant matrices. This fast implementation approach requires only simple fast Fourier transform operations and facilitates parallel implementations. The effectiveness of the proposed MIMO schemes is verified by both computer simulations and experimental results obtained by analyzing the measurements acquired in multiple in-water experiments.

Subgrid-scale stresses and scalar fluxes constructed by the multi-scale turnover Lagrangian map

NASA Astrophysics Data System (ADS)

AL-Bairmani, Sukaina; Li, Yi; Rosales, Carlos; Xie, Zheng-tong

2017-04-01

The multi-scale turnover Lagrangian map (MTLM) [C. Rosales and C. Meneveau, "Anomalous scaling and intermittency in three-dimensional synthetic turbulence," Phys. Rev. E 78, 016313 (2008)] uses nested multi-scale Lagrangian advection of fluid particles to distort a Gaussian velocity field and, as a result, generate non-Gaussian synthetic velocity fields. Passive scalar fields can be generated with the procedure when the fluid particles carry a scalar property [C. Rosales, "Synthetic three-dimensional turbulent passive scalar fields via the minimal Lagrangian map," Phys. Fluids 23, 075106 (2011)]. The synthetic fields have been shown to possess highly realistic statistics characterizing small scale intermittency, geometrical structures, and vortex dynamics. In this paper, we present a study of the synthetic fields using the filtering approach. This approach, which has not been pursued so far, provides insights on the potential applications of the synthetic fields in large eddy simulations and subgrid-scale (SGS) modelling. The MTLM method is first generalized to model scalar fields produced by an imposed linear mean profile. We then calculate the subgrid-scale stress, SGS scalar flux, SGS scalar variance, as well as related quantities from the synthetic fields. Comparison with direct numerical simulations (DNSs) shows that the synthetic fields reproduce the probability distributions of the SGS energy and scalar dissipation rather well. Related geometrical statistics also display close agreement with DNS results. The synthetic fields slightly under-estimate the mean SGS energy dissipation and slightly over-predict the mean SGS scalar variance dissipation. In general, the synthetic fields tend to slightly under-estimate the probability of large fluctuations for most quantities we have examined. Small scale anisotropy in the scalar field originated from the imposed mean gradient is captured. The sensitivity of the synthetic fields on the input spectra is assessed by using truncated spectra or model spectra as the input. Analyses show that most of the SGS statistics agree well with those from MTLM fields with DNS spectra as the input. For the mean SGS energy dissipation, some significant deviation is observed. However, it is shown that the deviation can be parametrized by the input energy spectrum, which demonstrates the robustness of the MTLM procedure.

Multi-metric calibration of hydrological model to capture overall flow regimes

NASA Astrophysics Data System (ADS)

Zhang, Yongyong; Shao, Quanxi; Zhang, Shifeng; Zhai, Xiaoyan; She, Dunxian

2016-08-01

Flow regimes (e.g., magnitude, frequency, variation, duration, timing and rating of change) play a critical role in water supply and flood control, environmental processes, as well as biodiversity and life history patterns in the aquatic ecosystem. The traditional flow magnitude-oriented calibration of hydrological model was usually inadequate to well capture all the characteristics of observed flow regimes. In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm. Two headwater watersheds in the arid Hexi Corridor were selected for the case study. Sixteen metrics were selected as optimization objectives, which could represent the major characteristics of flow regimes. Model performance was compared with that of the single objective calibration. Results showed that most metrics were better simulated by the multi-objective approach than those of the single objective calibration, especially the low and high flow magnitudes, frequency and variation, duration, maximum flow timing and rating. However, the model performance of middle flow magnitude was not significantly improved because this metric was usually well captured by single objective calibration. The timing of minimum flow was poorly predicted by both the multi-metric and single calibrations due to the uncertainties in model structure and input data. The sensitive parameter values of the hydrological model changed remarkably and the simulated hydrological processes by the multi-metric calibration became more reliable, because more flow characteristics were considered. The study is expected to provide more detailed flow information by hydrological simulation for the integrated water resources management, and to improve the simulation performances of overall flow regimes.

Integrated seismic stochastic inversion and multi-attributes to delineate reservoir distribution: Case study MZ fields, Central Sumatra Basin

NASA Astrophysics Data System (ADS)

Haris, A.; Novriyani, M.; Suparno, S.; Hidayat, R.; Riyanto, A.

2017-07-01

This study presents the integration of seismic stochastic inversion and multi-attributes for delineating the reservoir distribution in term of lithology and porosity in the formation within depth interval between the Top Sihapas and Top Pematang. The method that has been used is a stochastic inversion, which is integrated with multi-attribute seismic by applying neural network Probabilistic Neural Network (PNN). Stochastic methods are used to predict the probability mapping sandstone as the result of impedance varied with 50 realizations that will produce a good probability. Analysis of Stochastic Seismic Tnversion provides more interpretive because it directly gives the value of the property. Our experiment shows that AT of stochastic inversion provides more diverse uncertainty so that the probability value will be close to the actual values. The produced AT is then used for an input of a multi-attribute analysis, which is used to predict the gamma ray, density and porosity logs. To obtain the number of attributes that are used, stepwise regression algorithm is applied. The results are attributes which are used in the process of PNN. This PNN method is chosen because it has the best correlation of others neural network method. Finally, we interpret the product of the multi-attribute analysis are in the form of pseudo-gamma ray volume, density volume and volume of pseudo-porosity to delineate the reservoir distribution. Our interpretation shows that the structural trap is identified in the southeastern part of study area, which is along the anticline.

Multi-Grid detector for neutron spectroscopy: results obtained on time-of-flight spectrometer CNCS

NASA Astrophysics Data System (ADS)

Anastasopoulos, M.; Bebb, R.; Berry, K.; Birch, J.; Bryś, T.; Buffet, J.-C.; Clergeau, J.-F.; Deen, P. P.; Ehlers, G.; van Esch, P.; Everett, S. M.; Guerard, B.; Hall-Wilton, R.; Herwig, K.; Hultman, L.; Höglund, C.; Iruretagoiena, I.; Issa, F.; Jensen, J.; Khaplanov, A.; Kirstein, O.; Lopez Higuera, I.; Piscitelli, F.; Robinson, L.; Schmidt, S.; Stefanescu, I.

2017-04-01

The Multi-Grid detector technology has evolved from the proof-of-principle and characterisation stages. Here we report on the performance of the Multi-Grid detector, the MG.CNCS prototype, which has been installed and tested at the Cold Neutron Chopper Spectrometer, CNCS at SNS. This has allowed a side-by-side comparison to the performance of 3He detectors on an operational instrument. The demonstrator has an active area of 0.2 m2. It is specifically tailored to the specifications of CNCS. The detector was installed in June 2016 and has operated since then, collecting neutron scattering data in parallel to the He-3 detectors of CNCS. In this paper, we present a comprehensive analysis of this data, in particular on instrument energy resolution, rate capability, background and relative efficiency. Stability, gamma-ray and fast neutron sensitivity have also been investigated. The effect of scattering in the detector components has been measured and provides input to comparison for Monte Carlo simulations. All data is presented in comparison to that measured by the 3He detectors simultaneously, showing that all features recorded by one detector are also recorded by the other. The energy resolution matches closely. We find that the Multi-Grid is able to match the data collected by 3He, and see an indication of a considerable advantage in the count rate capability. Based on these results, we are confident that the Multi-Grid detector will be capable of producing high quality scientific data on chopper spectrometers utilising the unprecedented neutron flux of the ESS.

Advances in integrated photonic circuits for packet-switched interconnection

NASA Astrophysics Data System (ADS)

Williams, Kevin A.; Stabile, Ripalta

2014-03-01

Sustained increases in capacity and connectivity are needed to overcome congestion in a range of broadband communication network nodes. Packet routing and switching in the electronic domain are leading to unsustainable energy- and bandwidth-densities, motivating research into hybrid solutions: optical switching engines are introduced for massive-bandwidth data transport while the electronic domain is clocked at more modest GHz rates to manage routing. Commercially-deployed optical switching engines using MEMS technologies are unwieldy and too slow to reconfigure for future packet-based networking. Optoelectronic packet-compliant switch technologies have been demonstrated as laboratory prototypes, but they have so far mostly used discretely pigtailed components, which are impractical for control plane development and product assembly. Integrated photonics has long held the promise of reduced hardware complexity and may be the critical step towards packet-compliant optical switching engines. Recently a number of laboratories world-wide have prototyped optical switching circuits using monolithic integration technology with up to several hundreds of integrated optical components per chip. Our own work has focused on multi-input to multi-output switching matrices. Recently we have demonstrated 8×8×8λ space and wavelength selective switches using gated cyclic routers and 16×16 broadband switching chips using monolithic multi-stage networks. We now operate these advanced circuits with custom control planes implemented with FPGAs to explore real time packet routing in multi-wavelength, multi-port test-beds. We review our contributions in the context of state of the art photonic integrated circuit technology and packet optical switching hardware demonstrations.

A multi-purpose readout electronics for CdTe and CZT detectors for x-ray imaging applications

NASA Astrophysics Data System (ADS)

Yue, X. B.; Deng, Z.; Xing, Y. X.; Liu, Y. N.

2017-09-01

A multi-purpose readout electronics based on the DPLMS digital filter has been developed for CdTe and CZT detectors for X-ray imaging applications. Different filter coefficients can be synthesized optimized either for high energy resolution at relatively low counting rate or for high rate photon-counting with reduced energy resolution. The effects of signal width constraints, sampling rate and length were numerical studied by Mento Carlo simulation with simple CRRC shaper input signals. The signal width constraint had minor effect and the ENC was only increased by 6.5% when the signal width was shortened down to 2 τc. The sampling rate and length depended on the characteristic time constants of both input and output signals. For simple CR-RC input signals, the minimum number of the filter coefficients was 12 with 10% increase in ENC when the output time constant was close to the input shaping time. A prototype readout electronics was developed for demonstration, using a previously designed analog front ASIC and a commercial ADC card. Two different DPLMS filters were successfully synthesized and applied for high resolution and high counting rate applications respectively. The readout electronics was also tested with a linear array CdTe detector. The energy resolutions of Am-241 59.5 keV peak were measured to be 6.41% in FWHM for the high resolution filter and to be 13.58% in FWHM for the high counting rate filter with 160 ns signal width constraint.

Symbolic Analysis of Concurrent Programs with Polymorphism

NASA Technical Reports Server (NTRS)

Rungta, Neha Shyam

2010-01-01

The current trend of multi-core and multi-processor computing is causing a paradigm shift from inherently sequential to highly concurrent and parallel applications. Certain thread interleavings, data input values, or combinations of both often cause errors in the system. Systematic verification techniques such as explicit state model checking and symbolic execution are extensively used to detect errors in such systems [7, 9]. Explicit state model checking enumerates possible thread schedules and input data values of a program in order to check for errors [3, 9]. To partially mitigate the state space explosion from data input values, symbolic execution techniques substitute data input values with symbolic values [5, 7, 6]. Explicit state model checking and symbolic execution techniques used in conjunction with exhaustive search techniques such as depth-first search are unable to detect errors in medium to large-sized concurrent programs because the number of behaviors caused by data and thread non-determinism is extremely large. We present an overview of abstraction-guided symbolic execution for concurrent programs that detects errors manifested by a combination of thread schedules and data values [8]. The technique generates a set of key program locations relevant in testing the reachability of the target locations. The symbolic execution is then guided along these locations in an attempt to generate a feasible execution path to the error state. This allows the execution to focus in parts of the behavior space more likely to contain an error.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Using Captioned Television To Improve the Reading Proficiency of Language Minority students. Research Study.

ERIC Educational Resources Information Center

National Captioning Inst., Inc., Falls Church, VA.

This study proposed that captioned television, as a multi-sensory, largely entertaining medium might be an important source of comprehensive input for bilingual students in learning language and literacy. To explore this issue, the study investigated the following questions: (1) can bilingual students acquire vocabulary incidentally through…

Conceptualizing Indicator Domains for Evaluating Action Research

ERIC Educational Resources Information Center

Piggot-Irvine, Eileen; Rowe, Wendy; Ferkins, Lesley

2015-01-01

The focus of this paper is to share thinking about meta-level evaluation of action research (AR), and to introduce indicator domains for assessing and measuring inputs, outputs and outcomes. Meta-level and multi-site evaluation has been rare in AR beyond project implementation and participant satisfaction. The paper is the first of several…

77 FR 29033 - Medicare and Medicaid Programs; Reform of Hospital and Critical Access Hospital Conditions of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-16

... organizational structure, whereby multi- hospital systems have integrated their governing body functions to... based on nationally recognized and evidence-based guidelines and recommendations. Verbal Orders: We have..., Outpatient services ($300 million). Our estimates were based on input from stakeholders as well as on our own...

Computer-Aided Assessment in Mechanics: Question Design and Test Evaluation

ERIC Educational Resources Information Center

Gill, M.; Greenhow, M.

2007-01-01

This article describes pedagogic issues in setting objective tests in mechanics using Question Mark Perception, coupled with MathML mathematics mark-up and the Scalable Vector Graphics (SVG) syntax for producing diagrams. The content of the questions (for a range of question types such as multi-choice, numerical input and variants such as…

Global Journal of Computer Science and Technology. Volume 9, Issue 5 (Ver. 2.0)

ERIC Educational Resources Information Center

Dixit, R. K.

2010-01-01

This is a special issue published in version 1.0 of "Global Journal of Computer Science and Technology." Articles in this issue include: (1) [Theta] Scheme (Orthogonal Milstein Scheme), a Better Numerical Approximation for Multi-dimensional SDEs (Klaus Schmitz Abe); (2) Input Data Processing Techniques in Intrusion Detection…

Staring 2-D hadamard transform spectral imager

DOEpatents

Gentry, Stephen M [Albuquerque, NM; Wehlburg, Christine M [Albuquerque, NM; Wehlburg, Joseph C [Albuquerque, NM; Smith, Mark W [Albuquerque, NM; Smith, Jody L [Albuquerque, NM

2006-02-07

A staring imaging system inputs a 2D spatial image containing multi-frequency spectral information. This image is encoded in one dimension of the image with a cyclic Hadamarid S-matrix. The resulting image is detecting with a spatial 2D detector; and a computer applies a Hadamard transform to recover the encoded image.

An Experimental Evaluation of Generalized Predictive Control for Tiltrotor Aeroelastic Stability Augmentation in Airplane Mode of Flight

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.; Piatak, David J.; Nixon, Mark W.; Langston, Chester W.; Singleton, Jeffrey D.; Bennett, Richard L.; Brown, Ross K.

2001-01-01

The results of a joint NASA/Army/Bell Helicopter Textron wind-tunnel test to assess the potential of Generalized Predictive Control (GPC) for actively controlling the swashplate of tiltrotor aircraft to enhance aeroelastic stability in the airplane mode of flight are presented. GPC is an adaptive time-domain predictive control method that uses a linear difference equation to describe the input-output relationship of the system and to design the controller. The test was conducted in the Langley Transonic Dynamics Tunnel using an unpowered 1/5-scale semispan aeroelastic model of the V-22 that was modified to incorporate a GPC-based multi-input multi-output control algorithm to individually control each of the three swashplate actuators. Wing responses were used for feedback. The GPC-based control system was highly effective in increasing the stability of the critical wing mode for all of the conditions tested, without measurable degradation of the damping in the other modes. The algorithm was also robust with respect to its performance in adjusting to rapid changes in both the rotor speed and the tunnel airspeed.

CLARO: an ASIC for high rate single photon counting with multi-anode photomultipliers

NASA Astrophysics Data System (ADS)

Baszczyk, M.; Carniti, P.; Cassina, L.; Cotta Ramusino, A.; Dorosz, P.; Fiorini, M.; Gotti, C.; Kucewicz, W.; Malaguti, R.; Pessina, G.

2017-08-01

The CLARO is a radiation-hard 8-channel ASIC designed for single photon counting with multi-anode photomultiplier tubes. Each channel outputs a digital pulse when the input signal from the photomultiplier crosses a configurable threshold. The fast return to baseline, typically within 25 ns, and below 50 ns in all conditions, allows to count up to 107 hits/s on each channel, with a power consumption of about 1 mW per channel. The ASIC presented here is a much improved version of the first 4-channel prototype. The threshold can be precisely set in a wide range, between 30 ke- (5 fC) and 16 Me- (2.6 pC). The noise of the amplifier with a 10 pF input capacitance is 3.5 ke- (0.6 fC) RMS. All settings are stored in a 128-bit configuration and status register, protected against soft errors with triple modular redundancy. The paper describes the design of the ASIC at transistor-level, and demonstrates its performance on the test bench.

Observer-based distributed adaptive fault-tolerant containment control of multi-agent systems with general linear dynamics.

PubMed

Ye, Dan; Chen, Mengmeng; Li, Kui

2017-11-01

In this paper, we consider the distributed containment control problem of multi-agent systems with actuator bias faults based on observer method. The objective is to drive the followers into the convex hull spanned by the dynamic leaders, where the input is unknown but bounded. By constructing an observer to estimate the states and bias faults, an effective distributed adaptive fault-tolerant controller is developed. Different from the traditional method, an auxiliary controller gain is designed to deal with the unknown inputs and bias faults together. Moreover, the coupling gain can be adjusted online through the adaptive mechanism without using the global information. Furthermore, the proposed control protocol can guarantee that all the signals of the closed-loop systems are bounded and all the followers converge to the convex hull with bounded residual errors formed by the dynamic leaders. Finally, a decoupled linearized longitudinal motion model of the F-18 aircraft is used to demonstrate the effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Method and system for providing precise multi-function modulation

NASA Technical Reports Server (NTRS)

Davarian, Faramaz (Inventor); Sumida, Joe T. (Inventor)

1989-01-01

A method and system is disclosed which provides precise multi-function digitally implementable modulation for a communication system. The invention provides a modulation signal for a communication system in response to an input signal from a data source. A digitized time response is generated from samples of a time domain representation of a spectrum profile of a selected modulation scheme. The invention generates and stores coefficients for each input symbol in accordance with the selected modulation scheme. The output signal is provided by a plurality of samples, each sample being generated by summing the products of a predetermined number of the coefficients and a predetermined number of the samples of the digitized time response. In a specific illustrative implementation, the samples of the output signals are converted to analog signals, filtered and used to modulate a carrier in a conventional manner. The invention is versatile in that it allows for the storage of the digitized time responses and corresponding coefficient lookup table of a number of modulation schemes, any of which may then be selected for use in accordance with the teachings of the invention.

Warping an atlas derived from serial histology to 5 high-resolution MRIs.

PubMed

Tullo, Stephanie; Devenyi, Gabriel A; Patel, Raihaan; Park, Min Tae M; Collins, D Louis; Chakravarty, M Mallar

2018-06-19

Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

Multi-Party Quantum Private Comparison Protocol Based on Entanglement Swapping of Bell Entangled States

NASA Astrophysics Data System (ADS)

Ye, Tian-Yu

2016-09-01

Recently, Liu et al. proposed a two-party quantum private comparison (QPC) protocol using entanglement swapping of Bell entangled state (Commun. Theor. Phys. 57 (2012) 583). Subsequently Liu et al. pointed out that in Liu et al.'s protocol, the TP can extract the two users' secret inputs without being detected by launching the Bell-basis measurement attack, and suggested the corresponding improvement to mend this loophole (Commun. Theor. Phys. 62 (2014) 210). In this paper, we first point out the information leakage problem toward TP existing in both of the above two protocols, and then suggest the corresponding improvement by using the one-way hash function to encrypt the two users' secret inputs. We further put forward the three-party QPC protocol also based on entanglement swapping of Bell entangled state, and then validate its output correctness and its security in detail. Finally, we generalize the three-party QPC protocol into the multi-party case, which can accomplish arbitrary pair's comparison of equality among K users within one execution. Supported by the National Natural Science Foundation of China under Grant No. 61402407

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation

PubMed Central

Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack). PMID:27326763

LiPISC: A Lightweight and Flexible Method for Privacy-Aware Intersection Set Computation.

PubMed

Ren, Wei; Huang, Shiyong; Ren, Yi; Choo, Kim-Kwang Raymond

2016-01-01

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack).

A multi-mode manipulator display system for controlling remote robotic systems

NASA Technical Reports Server (NTRS)

Massimino, Michael J.; Meschler, Michael F.; Rodriguez, Alberto A.

1994-01-01

The objective and contribution of the research presented in this paper is to provide a Multi-Mode Manipulator Display System (MMDS) to assist a human operator with the control of remote manipulator systems. Such systems include space based manipulators such as the space shuttle remote manipulator system (SRMS) and future ground controlled teleoperated and telescience space systems. The MMDS contains a number of display modes and submodes which display position control cues position data in graphical formats, based primarily on manipulator position and joint angle data. Therefore the MMDS is not dependent on visual information for input and can assist the operator especially when visual feedback is inadequate. This paper provides descriptions of the new modes and experiment results to date.

Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging

NASA Astrophysics Data System (ADS)

Lee, Jason Sang Hun; Park, Inkyu; Park, Sangnam

2018-06-01

Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective preprocessing step which transforms each realvalued observational quantity or input feature into a binary number with a fixed number of digits. Each binary digit represents the quantity or magnitude in different scales. We have shown that this approach improves the performance of DNNs significantly for some specific tasks without any further complication in feature engineering. We apply this multi-scale distributed binary representation to deep learning on b-jet tagging using daughter particles' momenta and vertex information.

A superconducting large-angle magnetic suspension

NASA Technical Reports Server (NTRS)

Downer, James; Goldie, James; Torti, Richard

1991-01-01

The component technologies were developed required for an advanced control moment gyro (CMG) type of slewing actuator for large payloads. The key component of the CMG is a large-angle magnetic suspension (LAMS). The LAMS combines the functions of the gimbal structure, torque motors, and rotor bearings of a CMG. The LAMS uses a single superconducting source coil and an array of cryoresistive control coils to produce a specific output torque more than an order of magnitude greater than conventional devices. The designed and tested LAMS system is based around an available superconducting solenoid, an array of twelve room-temperature normal control coils, and a multi-input, multi-output control system. The control laws were demonstrated for stabilizing and controlling the LAMS system.

Tunable plasmonic dual wavelength multi/demultiplexer based on graphene sheets and cylindrical resonator

NASA Astrophysics Data System (ADS)

Asgari, Somayyeh; Granpayeh, Nosrat

2017-06-01

Two parallel graphene sheet waveguides and a graphene cylindrical resonator between them is proposed, analyzed, and simulated numerically by using the finite-difference time-domain method. One end of each graphene waveguide is the input and output port. The resonance and the prominent mid-infrared band-pass filtering effect are achieved. The transmittance spectrum is tuned by varying the radius of the graphene cylindrical resonator, the dielectric inside it, and also the chemical potential of graphene utilizing gate voltage. Simulation results are in good agreement with theoretical calculations. As an application, a multi/demultiplexer is proposed and analyzed. Our studies demonstrate that graphene based ultra-compact, nano-scale devices can be designed for optical processing and photonic integrated devices.

MIMO Sliding Mode Control for a Tailless Fighter Aircraft, An Alternative to Reconfigurable Architectures

NASA Technical Reports Server (NTRS)

Wells, S. R.; Hess, R. A.

2002-01-01

A frequency-domain procedure for the design of sliding mode controllers for multi-input, multi-output (MIMO) systems is presented. The methodology accommodates the effects of parasitic dynamics such as those introduced by unmodeled actuators through the introduction of multiple asymptotic observers and model reference hedging. The design procedure includes a frequency domain approach to specify the sliding manifold, the observer eigenvalues, and the hedge model. The procedure is applied to the development of a flight control system for a linear model of the Innovative Control Effector (ICE) fighter aircraft. The stability and performance robustness of the resulting design is demonstrated through the introduction of significant degradation in the control effector actuators and variation in vehicle dynamics.

A Comparative Study of the Dispersion of Multi-Wall Carbon Nanotubes Made by Arc-Discharge and Chemical Vapour Deposition.

PubMed

Frømyr, Tomas-Roll; Bourgeaux-Goget, Marie; Hansen, Finn Knut

2015-05-01

A method has been developed to characterize the dispersion of multi-wall carbon nanotubes in water using a disc centrifuge for the detection of individual carbon nanotubes, residual aggregates, and contaminants. Carbon nanotubes produced by arc-discharge have been measured and compared with carbon nanotubes produced by chemical vapour deposition. Studies performed on both pristine (see text) arc-discharge nanotubes is rather strong and that high ultra-sound intensity is required to achieve complete dispersion of carbon nanotube bundles. The logarithm of the mode of the particle size distribution of the arc-discharge carbon nanotubes was found to be a linear function of the logarithm of the total ultrasonic energy input in the dispersion process.

Variable Density Multilayer Insulation for Cryogenic Storage

NASA Technical Reports Server (NTRS)

Hedayat, A.; Brown, T. M.; Hastings, L. J.; Martin, J.

2000-01-01

Two analytical models for a foam/Variable Density Multi-Layer Insulation (VD-MLI) system performance are discussed. Both models are one-dimensional and contain three heat transfer mechanisms, namely conduction through the spacer material, radiation between the shields, and conduction through the gas. One model is based on the methodology developed by McIntosh while the other model is based on the Lockheed semi-empirical approach. All models input variables are based on the Multi-purpose Hydrogen Test Bed (MHTB) geometry and available values for material properties and empirical solid conduction coefficient. Heat flux predictions are in good agreement with the MHTB data, The heat flux predictions are presented for the foam/MLI combinations with 30, 45, 60, and 75 MLI layers

Non-cross talk multi-channel photomultiplier using guided electron multipliers

DOEpatents

Gomez, J.; Majewski, S.; Weisenberger, A.G.

1995-09-26

An improved multi-channel electron multiplier is provided that exhibits zero cross-talk and high rate operation. Resistive material input and output masks are employed to control divergence of electrons. Electron multiplication takes place in closed channels. Several embodiments are provided for these channels including a continuous resistive emissive multiplier and a discrete resistive multiplier with discrete dynode chains interspaced with resistive layers-masks. Both basic embodiments provide high gain multiplication of electrons without accumulating surface charges while containing electrons to their proper channels to eliminate cross-talk. The invention can be for example applied to improve the performance of ion mass spectrometers, positron emission tomography devices, in DNA sequencing and other beta radiography applications and in many applications in particle physics. 28 figs.

Non cross talk multi-channel photomultiplier using guided electron multipliers

DOEpatents

Gomez, Javier; Majewski, Stanislaw; Weisenberger, Andrew G.

1995-01-01

An improved multi-channel electron multiplier is provided that exhibits zero cross-talk and high rate operation. Resistive material input and output masks are employed to control divergence of electrons. Electron multiplication takes place in closed channels. Several embodiments are provided for these channels including a continuous resistive emissive multiplier and a discrete resistive multiplier with discrete dynode chains interspaced with resistive layers-masks. Both basic embodiments provide high gain multiplication of electrons without accumulating surface charges while containing electrons to their proper channels to eliminate cross-talk. The invention can be for example applied to improve the performance of ion mass spectrometers, positron emission tomography devices, in DNA sequencing and other beta radiography applications and in many applications in particle physics.

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

NASA Astrophysics Data System (ADS)

Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

2016-09-01

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

Using a smart wheelchair as a gaming device for floor-projected games: a mixed-reality environment for training powered-wheelchair driving skills.

PubMed

Secoli, R; Zondervan, D; Reinkensmeyer, D

2012-01-01

For children with a severe disability, such as can arise from cerebral palsy, becoming independent in mobility is a critical goal. Currently, however, driver's training for powered wheelchair use is labor intensive, requiring hand-over-hand assistance from a skilled therapist to keep the trainee safe. This paper describes the design of a mixed reality environment for semi-autonomous training of wheelchair driving skills. In this system, the wheelchair is used as the gaming input device, and users train driving skills by maneuvering through floor-projected games created with a multi-projector system and a multi-camera tracking system. A force feedback joystick assists in steering and enhances safety.

The optimal location of piezoelectric actuators and sensors for vibration control of plates

NASA Astrophysics Data System (ADS)

Kumar, K. Ramesh; Narayanan, S.

2007-12-01

This paper considers the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor-actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input-multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem. A genetic algorithm (GA) is used to solve the zero-one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.

Switched-capacitor isolated LED driver

DOEpatents

Sanders, Seth R.; Kline, Mitchell

2016-03-22

A switched-capacitor voltage converter which is particularly well-suited for receiving a line voltage from which to drive current through a series of light emitting diodes (LEDs). Input voltage is rectified in a multi-level rectifier network having switched capacitors in an ascending-bank configuration for passing voltages in uniform steps between zero volts up to full received voltage V.sub.DC. A regulator section, operating on V.sub.DC, comprises switched-capacitor stages of H-bridge switching and flying capacitors. A current controlled oscillator drives the states of the switched-capacitor stages and changes its frequency to maintain a constant current to the load. Embodiments are described for isolating the load from the mains, utilizing an LC tank circuit or a multi-primary-winding transformer.

Investigation, sensitivity analysis, and multi-objective optimization of effective parameters on temperature and force in robotic drilling cortical bone.

PubMed

Tahmasbi, Vahid; Ghoreishi, Majid; Zolfaghari, Mojtaba

2017-11-01

The bone drilling process is very prominent in orthopedic surgeries and in the repair of bone fractures. It is also very common in dentistry and bone sampling operations. Due to the complexity of bone and the sensitivity of the process, bone drilling is one of the most important and sensitive processes in biomedical engineering. Orthopedic surgeries can be improved using robotic systems and mechatronic tools. The most crucial problem during drilling is an unwanted increase in process temperature (higher than 47 °C), which causes thermal osteonecrosis or cell death and local burning of the bone tissue. Moreover, imposing higher forces to the bone may lead to breaking or cracking and consequently cause serious damage. In this study, a mathematical second-order linear regression model as a function of tool drilling speed, feed rate, tool diameter, and their effective interactions is introduced to predict temperature and force during the bone drilling process. This model can determine the maximum speed of surgery that remains within an acceptable temperature range. Moreover, for the first time, using designed experiments, the bone drilling process was modeled, and the drilling speed, feed rate, and tool diameter were optimized. Then, using response surface methodology and applying a multi-objective optimization, drilling force was minimized to sustain an acceptable temperature range without damaging the bone or the surrounding tissue. In addition, for the first time, Sobol statistical sensitivity analysis is used to ascertain the effect of process input parameters on process temperature and force. The results show that among all effective input parameters, tool rotational speed, feed rate, and tool diameter have the highest influence on process temperature and force, respectively. The behavior of each output parameters with variation in each input parameter is further investigated. Finally, a multi-objective optimization has been performed considering all the aforementioned parameters. This optimization yielded a set of data that can considerably improve orthopedic osteosynthesis outcomes.

A Literature Review and Compilation of Nuclear Waste Management System Attributes for Use in Multi-Objective System Evaluations.

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

Kalinina, Elena Arkadievna; Samsa, Michael

The purpose of this work was to compile a comprehensive initial set of potential nuclear waste management system attributes. This initial set of attributes is intended to serve as a starting point for additional consideration by system analysts and planners to facilitate the development of a waste management system multi-objective evaluation framework based on the principles and methodology of multi-attribute utility analysis. The compilation is primarily based on a review of reports issued by the Canadian Nuclear Waste Management Organization (NWMO) and the Blue Ribbon Commission on America's Nuclear Future (BRC), but also an extensive review of the available literaturemore » for similar and past efforts as well. Numerous system attributes found in different sources were combined into a single objectives-oriented hierarchical structure. This study provides a discussion of the data sources and the descriptions of the hierarchical structure. A particular focus of this study was on collecting and compiling inputs from past studies that involved the participation of various external stakeholders. However, while the important role of stakeholder input in a country's waste management decision process is recognized in the referenced sources, there are only a limited number of in-depth studies of the stakeholders' differing perspectives. Compiling a comprehensive hierarchical listing of attributes is a complex task since stakeholders have multiple and often conflicting interests. The BRC worked for two years (January 2010 to January 2012) to "ensure it has heard from as many points of view as possible." The Canadian NWMO study took four years and ample resources, involving national and regional stakeholders' dialogs, internet-based dialogs, information and discussion sessions, open houses, workshops, round tables, public attitude research, website, and topic reports. The current compilation effort benefited from the distillation of these many varied inputs conducted by the previous studies.« less

Optogenetic Modulation and Multi-Electrode Analysis of Cerebellar Networks In Vivo

PubMed Central

Kruse, Wolfgang; Krause, Martin; Aarse, Janna; Mark, Melanie D.; Manahan-Vaughan, Denise; Herlitze, Stefan

2014-01-01

The firing patterns of cerebellar Purkinje cells (PCs), as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs), climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2) expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice. PMID:25144735

Glassy carbon/multi walled carbon nanotube/cadmium sulphide photoanode for light energy storage in vanadium photoelectrochemical cell

NASA Astrophysics Data System (ADS)

Peimanifard, Zahra; Rashid-Nadimi, Sahar

2015-12-01

The aim of this study is utilizing the artificial photosynthesis, which is an attractive and challenging theme in the photoelectrocatalytic water splitting, to charge the vanadium redox flow battery (VRFB). In this work multi walled carbon nanotube/cadmium sulphide hybrid is employed as a photoanode material to oxidize VO2+ toVO2+ for charging the positive vanadium redox flow battery's half-cell. Characterization studies are also described using the scanning electron microscopic-energy-dispersive X-ray spectroscopy (SEM-EDS), inductively coupled plasma atomic emission spectroscopy (ICP-AES) and UV-Visible methods. The phtoelectrochemical performance is characterized by cyclic voltammetry and chronoamperometry. Applied bias photon-to-current efficiency (ABPE) is achieved for both two and three-electrode configurations. The glassy carbon/multi walled carbon nanotube/cadmium sulphide yields high maximum ABPE of 2.6% and 2.12% in three and two-electrode setups, respectively. These results provide a useful guideline in designing photoelectrochemical cells for charging the vanadium redox flow batteries by sunlight as a low cost, free and abundant energy source, which does not rely on an external power input.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task.

PubMed

Bonnet, Vincent; Azevedo-Coste, Christine; Robert, Thomas; Fraisse, Philippe; Venture, Gentiane

2017-07-01

This paper aims at developing and evaluating a new practical method for the real-time estimate of joint torques and external wrenches during multi-contact sit-to-stand (STS) task using kinematics data only. The proposed method allows also identifying subject specific body inertial segment parameters that are required to perform inverse dynamics. The identification phase is performed using simple and repeatable motions. Thanks to an accurately identified model the estimate of the total external wrench can be used as an input to solve an under-determined multi-contact problem. It is solved using a constrained quadratic optimization process minimizing a hybrid human-like energetic criterion. The weights of this hybrid cost function are adjusted and a sensitivity analysis is performed in order to reproduce robustly human external wrench distribution. The results showed that the proposed method could successfully estimate the external wrenches under buttocks, feet, and hands during STS tasks (RMS error lower than 20 N and 6 N.m). The simplicity and generalization abilities of the proposed method allow paving the way of future diagnosis solutions and rehabilitation applications, including in-home use.

Real-time multi-mode neutron multiplicity counter

DOEpatents

Rowland, Mark S; Alvarez, Raymond A

2013-02-26

Embodiments are directed to a digital data acquisition method that collects data regarding nuclear fission at high rates and performs real-time preprocessing of large volumes of data into directly useable forms for use in a system that performs non-destructive assaying of nuclear material and assemblies for mass and multiplication of special nuclear material (SNM). Pulses from a multi-detector array are fed in parallel to individual inputs that are tied to individual bits in a digital word. Data is collected by loading a word at the individual bit level in parallel, to reduce the latency associated with current shift-register systems. The word is read at regular intervals, all bits simultaneously, with no manipulation. The word is passed to a number of storage locations for subsequent processing, thereby removing the front-end problem of pulse pileup. The word is used simultaneously in several internal processing schemes that assemble the data in a number of more directly useable forms. The detector includes a multi-mode counter that executes a number of different count algorithms in parallel to determine different attributes of the count data.

Performance analysis of cooperative virtual MIMO systems for wireless sensor networks.

PubMed

Rafique, Zimran; Seet, Boon-Chong; Al-Anbuky, Adnan

2013-05-28

Multi-Input Multi-Output (MIMO) techniques can be used to increase the data rate for a given bit error rate (BER) and transmission power. Due to the small form factor, energy and processing constraints of wireless sensor nodes, a cooperative Virtual MIMO as opposed to True MIMO system architecture is considered more feasible for wireless sensor network (WSN) applications. Virtual MIMO with Vertical-Bell Labs Layered Space-Time (V-BLAST) multiplexing architecture has been recently established to enhance WSN performance. In this paper, we further investigate the impact of different modulation techniques, and analyze for the first time, the performance of a cooperative Virtual MIMO system based on V-BLAST architecture with multi-carrier modulation techniques. Through analytical models and simulations using real hardware and environment settings, both communication and processing energy consumptions, BER, spectral efficiency, and total time delay of multiple cooperative nodes each with single antenna are evaluated. The results show that cooperative Virtual-MIMO with Binary Phase Shift Keying-Wavelet based Orthogonal Frequency Division Multiplexing (BPSK-WOFDM) modulation is a promising solution for future high data-rate and energy-efficient WSNs.

Performance Analysis of Cooperative Virtual MIMO Systems for Wireless Sensor Networks

PubMed Central

Rafique, Zimran; Seet, Boon-Chong; Al-Anbuky, Adnan

2013-01-01

Multi-Input Multi-Output (MIMO) techniques can be used to increase the data rate for a given bit error rate (BER) and transmission power. Due to the small form factor, energy and processing constraints of wireless sensor nodes, a cooperative Virtual MIMO as opposed to True MIMO system architecture is considered more feasible for wireless sensor network (WSN) applications. Virtual MIMO with Vertical-Bell Labs Layered Space-Time (V-BLAST) multiplexing architecture has been recently established to enhance WSN performance. In this paper, we further investigate the impact of different modulation techniques, and analyze for the first time, the performance of a cooperative Virtual MIMO system based on V-BLAST architecture with multi-carrier modulation techniques. Through analytical models and simulations using real hardware and environment settings, both communication and processing energy consumptions, BER, spectral efficiency, and total time delay of multiple cooperative nodes each with single antenna are evaluated. The results show that cooperative Virtual-MIMO with Binary Phase Shift Keying-Wavelet based Orthogonal Frequency Division Multiplexing (BPSK-WOFDM) modulation is a promising solution for future high data-rate and energy-efficient WSNs. PMID:23760087

Software Defined Radio with Parallelized Software Architecture

NASA Technical Reports Server (NTRS)

Heckler, Greg

2013-01-01

This software implements software-defined radio procession over multicore, multi-CPU systems in a way that maximizes the use of CPU resources in the system. The software treats each processing step in either a communications or navigation modulator or demodulator system as an independent, threaded block. Each threaded block is defined with a programmable number of input or output buffers; these buffers are implemented using POSIX pipes. In addition, each threaded block is assigned a unique thread upon block installation. A modulator or demodulator system is built by assembly of the threaded blocks into a flow graph, which assembles the processing blocks to accomplish the desired signal processing. This software architecture allows the software to scale effortlessly between single CPU/single-core computers or multi-CPU/multi-core computers without recompilation. NASA spaceflight and ground communications systems currently rely exclusively on ASICs or FPGAs. This software allows low- and medium-bandwidth (100 bps to approx.50 Mbps) software defined radios to be designed and implemented solely in C/C++ software, while lowering development costs and facilitating reuse and extensibility.

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

Generation of tunable chain of three-dimensional optical bottle beams via focused multi-ring hollow Gaussian beam.

PubMed

Philip, Geo M; Viswanathan, Nirmal K

2010-11-01

We report here the generation of a chain of three-dimensional (3-D) optical bottle beams by focusing a π-phase shifted multi-ring hollow Gaussian beam (HGB) using a lens with spherical aberration. The rings of the HGB of suitable radial (k(r)) and axial (k(z)) wave vectors are generated using a double-negative axicon chemically etched in the optical fiber tips. Moving the lens position with respect to the fiber tip results in variation of the semi-angle of the cones of wave vectors of the HGBs and their diameter, using which we demonstrate tunability in the size and the periodicity of the 3-D optical bottle beams over a wide range, from micrometers to millimeters. The propagation characteristics of the beams resulting from focusing of single- and multi-ring HGBs and resulting in a quasi-non-diffracting beam and a chain of 3-D optical bottle beams, respectively, are simulated using only the input beam parameters and are found to agree well with experimental results.

Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang

2018-04-01

Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

iTOUGH2 V6.5

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

Finsterle, Stefan A.

2010-11-01

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional , multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. It performs sensitivity analysis, parameter estimation, and uncertainty propagation, analysis in geosciences and reservoir engineering and other application areas. It supports a number of different combination of fluids and components [equation-of-state (EOS) modules]. In addition, the optimization routines implemented in iTOUGH2 can also be used or sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files. This link is achieved by means of the PEST application programmingmore » interface. iTOUGH2 solves the inverse problem by minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative fee, gradient-based and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlos simulation for uncertainty propagation analysis. A detailed residual and error analysis is provided. This upgrade includes new EOS modules (specifically EOS7c, ECO2N and TMVOC), hysteretic relative permeability and capillary pressure functions and the PEST API. More details can be found at http://esd.lbl.gov/iTOUGH2 and the publications cited there. Hardware Req.: Multi-platform; Related/auxiliary software PVM (if running in parallel).« less

Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

NASA Astrophysics Data System (ADS)

Okamura, Rintaro; Iwabuchi, Hironobu; Schmidt, K. Sebastian

2017-12-01

Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

PubMed

Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

2012-09-01

Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

A graphical user interface (GUI) toolkit for the calculation of three-dimensional (3D) multi-phase biological effective dose (BED) distributions including statistical analyses.

PubMed

Kauweloa, Kevin I; Gutierrez, Alonso N; Stathakis, Sotirios; Papanikolaou, Niko; Mavroidis, Panayiotis

2016-07-01

A toolkit has been developed for calculating the 3-dimensional biological effective dose (BED) distributions in multi-phase, external beam radiotherapy treatments such as those applied in liver stereotactic body radiation therapy (SBRT) and in multi-prescription treatments. This toolkit also provides a wide range of statistical results related to dose and BED distributions. MATLAB 2010a, version 7.10 was used to create this GUI toolkit. The input data consist of the dose distribution matrices, organ contour coordinates, and treatment planning parameters from the treatment planning system (TPS). The toolkit has the capability of calculating the multi-phase BED distributions using different formulas (denoted as true and approximate). Following the calculations of the BED distributions, the dose and BED distributions can be viewed in different projections (e.g. coronal, sagittal and transverse). The different elements of this toolkit are presented and the important steps for the execution of its calculations are illustrated. The toolkit is applied on brain, head & neck and prostate cancer patients, who received primary and boost phases in order to demonstrate its capability in calculating BED distributions, as well as measuring the inaccuracy and imprecision of the approximate BED distributions. Finally, the clinical situations in which the use of the present toolkit would have a significant clinical impact are indicated. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Computational tools for multi-linked flexible structures

NASA Technical Reports Server (NTRS)

Lee, Gordon K. F.; Brubaker, Thomas A.; Shults, James R.

1990-01-01

A software module which designs and tests controllers and filters in Kalman Estimator form, based on a polynomial state-space model is discussed. The user-friendly program employs an interactive graphics approach to simplify the design process. A variety of input methods are provided to test the effectiveness of the estimator. Utilities are provided which address important issues in filter design such as graphical analysis, statistical analysis, and calculation time. The program also provides the user with the ability to save filter parameters, inputs, and outputs for future use.

Realization of optimized quantum controlled-logic gate based on the orbital angular momentum of light.

PubMed

Zeng, Qiang; Li, Tao; Song, Xinbing; Zhang, Xiangdong

2016-04-18

We propose and experimentally demonstrate an optimized setup to implement quantum controlled-NOT operation using polarization and orbital angular momentum qubits. This device is more adaptive to inputs with various polarizations, and can work both in classical and quantum single-photon regime. The logic operations performed by such a setup not only possess high stability and polarization-free character, they can also be easily extended to deal with multi-qubit input states. As an example, the experimental implementation of generalized three-qubit Toffoli gate has been presented.

CREATE Wellness: A multi-component behavioral intervention for patients not responding to traditional Cardiovascular disease management.

PubMed

Miller-Rosales, Chris; Sterling, Stacy A; Wood, Sabrina B; Ross, Thekla; Makki, Mojdeh; Zamudio, Cindy; Kane, Irene M; Richardson, Megan C; Samayoa, Claudia; Charvat-Aguilar, Nancy; Lu, Wendy Y; Vo, Michelle; Whelan, Kimberly; Uratsu, Connie S; Grant, Richard W

2017-12-01

Cardiovascular disease (CVD) is the leading cause of death in the US. Many patients do not benefit from traditional disease management approaches to CVD risk reduction. Here we describe the rationale, development, and implementation of a multi-component behavioral intervention targeting patients who have persistently not met goals of CVD risk factor control. Informed by published evidence, relevant theoretical frameworks, stakeholder advice, and patient input, we developed a group-based intervention (Changing Results: Engage and Activate to Enhance Wellness; "CREATE Wellness") to address the complex needs of patients with elevated or unmeasured CVD-related risk factors. We are testing this intervention in a randomized trial among patients with persistent (i.e > 2 years) sub-optimal risk factor control despite being enrolled in an advanced and highly successful CVD disease management program. The CREATE Wellness intervention is designed as a 3 session, group-based intervention combining proven elements of patient activation, health system engagement skills training, shared decision making, care planning, and identification of lifestyle change barriers. Our key learnings in designing the intervention included the value of multi-level stakeholder input and the importance of pragmatic skills training to address barriers to care. The CREATE Wellness intervention represents an evidence-based, patient-centered approach for patients not responding to traditional disease management. The trial is currently underway at three medical facilities within Kaiser Permanente Northern California and next steps include an evaluation of efficacy, adaptation for non-English speaking patient populations, and modification of the curriculum for web- or phone-based versions. NCT02302612.

Statistical uncertainty analysis applied to the DRAGONv4 code lattice calculations and based on JENDL-4 covariance data

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

Hernandez-Solis, A.; Demaziere, C.; Ekberg, C.

2012-07-01

In this paper, multi-group microscopic cross-section uncertainty is propagated through the DRAGON (Version 4) lattice code, in order to perform uncertainty analysis on k{infinity} and 2-group homogenized macroscopic cross-sections predictions. A statistical methodology is employed for such purposes, where cross-sections of certain isotopes of various elements belonging to the 172 groups DRAGLIB library format, are considered as normal random variables. This library is based on JENDL-4 data, because JENDL-4 contains the largest amount of isotopic covariance matrixes among the different major nuclear data libraries. The aim is to propagate multi-group nuclide uncertainty by running the DRAGONv4 code 500 times, andmore » to assess the output uncertainty of a test case corresponding to a 17 x 17 PWR fuel assembly segment without poison. The chosen sampling strategy for the current study is Latin Hypercube Sampling (LHS). The quasi-random LHS allows a much better coverage of the input uncertainties than simple random sampling (SRS) because it densely stratifies across the range of each input probability distribution. Output uncertainty assessment is based on the tolerance limits concept, where the sample formed by the code calculations infers to cover 95% of the output population with at least a 95% of confidence. This analysis is the first attempt to propagate parameter uncertainties of modern multi-group libraries, which are used to feed advanced lattice codes that perform state of the art resonant self-shielding calculations such as DRAGONv4. (authors)« less

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex.

PubMed

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs.

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex

PubMed Central

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs. PMID:29674950

A comparison of partial order technique with three methods of multi-criteria analysis for ranking of chemical substances.

PubMed

Lerche, Dorte; Brüggemann, Rainer; Sørensen, Peter; Carlsen, Lars; Nielsen, Ole John

2002-01-01

An alternative to the often cumbersome and time-consuming risk assessments of chemical substances could be more reliable and advanced priority setting methods. An elaboration of the simple scoring methods is provided by Hasse Diagram Technique (HDT) and/or Multi-Criteria Analysis (MCA). The present study provides an in depth evaluation of HDT relative to three MCA techniques. The new and main methodological step in the comparison is the use of probability concepts based on mathematical tools such as linear extensions of partially ordered sets and Monte Carlo simulations. A data set consisting of 12 High Production Volume Chemicals (HPVCs) is used for illustration. It is a paradigm in this investigation to claim that the need of external input (often subjective weightings of criteria) should be minimized and that the transparency should be maximized in any multicriteria prioritisation. The study illustrates that the Hasse diagram technique (HDT) needs least external input, is most transparent and is least subjective. However, HDT has some weaknesses if there are criteria which exclude each other. Then weighting is needed. Multi-Criteria Analysis (i.e. Utility Function approach, PROMETHEE and concordance analysis) can deal with such mutual exclusions because their formalisms to quantify preferences allow participation e.g. weighting of criteria. Consequently MCA include more subjectivity and loose transparency. The recommendation which arises from this study is that the first step in decision making is to run HDT and as the second step possibly is to run one of the MCA algorithms.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Effects of Variance and Input Distribution on the Training of L2 Learners' Tone Categorization

ERIC Educational Resources Information Center

Liu, Jiang

2013-01-01

Recent psycholinguistic findings showed that (a) a multi-modal phonetic training paradigm that encodes visual, interactive information is more effective in training L2 learners' perception of novel categories, (b) decreasing the acoustic variance of a phonetic dimension allows the learners to more effectively shift the perceptual weight towards…

A Multi-Media CAI Terminal Based upon a Microprocessor with Applications for the Handicapped.

ERIC Educational Resources Information Center

Brebner, Ann; Hallworth, H. J.

The design of the CAI interface described is based on the microprocessor in order to meet three basic requirements for providing appropriate instruction to the developmentally handicapped: (1) portability, so that CAI can be taken into the customary learning environment; (2) reliability; and (3) flexibility, to permit use of new input and output…

The Neurophysiology of Autonomic Dysfunction in SCI: Plasticity in the Input and Output Neurons

DTIC Science & Technology

2014-04-01

multi-segmental spinal pain reflex Location: Halls B-H Presentation Time: Tuesday , Nov 16, 2010, 8:00 AM - 9:00 AM Authors: *K. E. TANSEY1, M...neurological scoring methods. Cognitive functions were tested using Fear Conditioning tests at 8–10 days post- injury and Morris Water Maze tests 11–15

Phonological Constraint Induction in a Connectionist Network: Learning OCP-Place Constraints from Data

ERIC Educational Resources Information Center

Alderete, John; Tupper, Paul; Frisch, Stefan A.

2013-01-01

A significant problem in computational language learning is that of inferring the content of well-formedness constraints from input data. In this article, we approach the constraint induction problem as the gradual adjustment of subsymbolic constraints in a connectionist network. In particular, we develop a multi-layer feed-forward network that…

Response of North American ecosystem models to multi-annual periodicities in temperature and precipitation

Treesearch

J. Alan Yeakley; Ron A. Moen; David D. Breshears; Martha K. Nungesser

1994-01-01

Ecosystem models typically use input temperature and precipitation data generated stochastically from weather station means and variances. Although the weather station data are based on measurements taken over a few decades, model simulations are usually on the order of centuries. Consequently, observed periodicities in temperature and precipitation at the continental...

The Correlation of Human Capital on Costs of Air Force Acquisition Programs

DTIC Science & Technology

2009-03-01

6.78 so our model does not exhibit the presence of multi-collinearity. We empirically tested for heteroskedasticity using the Breusch - Pagan -Godfrey...inputs to outputs. The output in this study is the average cost overrun of Aeronautical Systems Center research, development, test , and evaluation...32 Pre-Estimation Specification Tests ............................................................................34 Post

Children's Text Messaging: Abbreviations, Input Methods and Links with Literacy

ERIC Educational Resources Information Center

Kemp, N.; Bushnell, C.

2011-01-01

This study investigated the effects of mobile phone text-messaging method (predictive and multi-press) and experience (in texters and non-texters) on children's textism use and understanding. It also examined popular claims that the use of text-message abbreviations, or "textese" spelling, is associated with poor literacy skills. A sample of 86…

Gender Equality and Girls' Education: Investigating Frameworks, Disjunctures and Meanings of Quality Education

ERIC Educational Resources Information Center

Aikman, Sheila; Rao, Nitya

2012-01-01

The article draws on qualitative educational research across a diversity of low-income countries to examine the gendered inequalities in education as complex, multi-faceted and situated rather than a series of barriers to be overcome through linear input-output processes focused on isolated dimensions of quality. It argues that frameworks for…

Vocal Development in Young Children with Cochlear Implants: Profiles and Implications for Intervention.

ERIC Educational Resources Information Center

Ertmer, David J.; Young, Nancy; Grohne, Kristine; Mellon, Jennifer A.; Johnson, Claire; Corbett, Kristin; Saindon, Kathy

2002-01-01

This article describes prelinguistic vocal development in two prelingually deaf children who received multi-channel cochlear implants at 10 and 28 months. The older child made rapid progress in vocal development, while the other showed slower progress Use of short periods of prelinguistic input for stimulating vocal development is discussed.…

Postoptimality analysis in the selection of technology portfolios

NASA Technical Reports Server (NTRS)

Adumitroaie, Virgil; Shelton, Kacie; Elfes, Alberto; Weisbin, Charles R.

2006-01-01

This paper describes an approach for qualifying optimal technology portfolios obtained with a multi-attribute decision support system. The goal is twofold: to gauge the degree of confidence in the optimal solution and to provide the decision-maker with an array of viable selection alternatives, which take into account input uncertainties and possibly satisfy non-technical constraints.

Design and characterization of MEMS interferometric sensing

NASA Astrophysics Data System (ADS)

Snyder, R.; Siahmakoun, A.

2010-02-01

A MEMS-based interferometric sensor is produced using the multi-user MEMS processing standard (MUMPS) micromirrors, movable by thermal actuation. The interferometer is comprised of gold reflection surfaces, polysilicon thermal actuators, hinges, latches and thin film polarization beam splitters. A polysilicon film of 3.5 microns reflects and transmits incident polarized light from an external laser source coupled to a multi-mode optical fiber. The input beam is shaped to a diameter of 10 to 20 microns for incidence upon the 100 micron mirrors. Losses in the optical path include diffraction effects from etch holes created in the manufacturing process, surface roughness of both gold and polysilicon layers, and misalignment of micro-scale optical components. Numerous optical paths on the chip vary by length, number of reflections, and mirror subsystems employed. Subsystems include thermal actuator batteries producing lateral position displacement, angularly tunable mirrors, double reflection surfaces, and static vertical mirrors. All mirror systems are raised via manual stimulation using two micron, residue-free probe tips and some may be aligned using electrical signals causing resistive heating in thermal actuators. The characterization of thermal actuator batteries includes maximum displacement, deflection, and frequency response that coincides with theoretical thermodynamic simulations using finite-element analysis. Maximum deflection of 35 microns at 400 mW input electrical power is shown for three types of actuator batteries as is deflection dependent frequency response data for electrical input signals up to 10 kHz.

Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks.

PubMed

Chao, Zhen; Kim, Dohyeon; Kim, Hee-Joung

2018-04-01

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. Recently, neural network technique was applied to medical image fusion by many researchers, but there are still many deficiencies. In this study, we propose a novel fusion method to combine multi-modality medical images based on the enhanced fuzzy radial basis function neural network (Fuzzy-RBFNN), which includes five layers: input, fuzzy partition, front combination, inference, and output. Moreover, we propose a hybrid of the gravitational search algorithm (GSA) and error back propagation algorithm (EBPA) to train the network to update the parameters of the network. Two different patterns of images are used as inputs of the neural network, and the output is the fused image. A comparison with the conventional fusion methods and another neural network method through subjective observation and objective evaluation indexes reveals that the proposed method effectively synthesized the information of input images and achieved better results. Meanwhile, we also trained the network by using the EBPA and GSA, individually. The results reveal that the EBPGSA not only outperformed both EBPA and GSA, but also trained the neural network more accurately by analyzing the same evaluation indexes. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method.

PubMed

Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac

2017-11-01

This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

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

Jensen, Colby B.; Folsom, Charles P.; Davis, Cliff B.

Experimental testing in the Multi-Static Environment Rodlet Transient Test Apparatus (SERTTA) will lead the rebirth of transient fuel testing in the United States as part of the Accident Tolerant Fuels (ATF) progam. The Multi-SERTTA is comprised of four isolated pressurized environments capable of a wide variety of working fluids and thermal conditions. Ultimately, the TREAT reactor as well as the Multi-SERTTA test vehicle serve the purpose of providing desired thermal-hydraulic boundary conditions to the test specimen. The initial ATF testing in TREAT will focus on reactivity insertion accident (RIA) events using both gas and water environments including typical PWR operatingmore » pressures and temperatures. For the water test environment, a test configuration is envisioned using the expansion tank as part of the gas-filled expansion volume seen by the test to provide additional pressure relief. The heat transfer conditions during the high energy power pulses of RIA events remains a subject of large uncertainty and great importance for fuel performance predictions. To support transient experiments, the Multi-SERTTA vehicle has been modeled using RELAP5 with a baseline test specimen composed of UO2 fuel in zircaloy cladding. The modeling results show the influence of the designs of the specimen, vehicle, and transient power pulses. The primary purpose of this work is to provide input and boundary conditions to fuel performance code BISON. Therefore, studies of parameters having influence on specimen performance during RIA transients are presented including cladding oxidation, power pulse magnitude and width, cladding-to-coolant heat fluxes, fuel-to-cladding gap, transient boiling effects (modified CHF values), etc. The results show the great flexibility and capacity of the TREAT Multi-SERTTA test vehicle to provide testing under a wide range of prototypic thermal-hydraulic conditions as never done before.« less

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

A KLM-circuit model of a multi-layer transducer for acoustic bladder volume measurements.

PubMed

Merks, E J W; Borsboom, J M G; Bom, N; van der Steen, A F W; de Jong, N

2006-12-22

In a preceding study a new technique to non-invasively measure the bladder volume on the basis of non-linear wave propagation was validated. It was shown that the harmonic level generated at the posterior bladder wall increases for larger bladder volumes. A dedicated transducer is needed to further verify and implement this approach. This transducer must be capable of both transmission of high-pressure waves at fundamental frequency and reception of up to the third harmonic. For this purpose, a multi-layer transducer was constructed using a single element PZT transducer for transmission and a PVDF top-layer for reception. To determine feasibility of the multi-layer concept for bladder volume measurements, and to ensure optimal performance, an equivalent mathematical model on the basis of KLM-circuit modeling was generated. This model was obtained in two subsequent steps. Firstly, the PZT transducer was modeled without PVDF-layer attached by means of matching the model with the measured electrical input impedance. It was validated using pulse-echo measurements. Secondly, the model was extended with the PVDF-layer. The total model was validated by considering the PVDF-layer as a hydrophone on the PZT transducer surface and comparing the measured and simulated PVDF responses on a wave transmitted by the PZT transducer. The obtained results indicated that a valid model for the multi-layer transducer was constructed. The model showed feasibility of the multi-layer concept for bladder volume measurements. It also allowed for further optimization with respect to electrical matching and transmit waveform. Additionally, the model demonstrated the effect of mechanical loading of the PVDF-layer on the PZT transducer.

A versatile, C-band spanning, high repetition rate, cascaded four wave mixing based multi-wavelength source

NASA Astrophysics Data System (ADS)

Vikram, B. S.; Prakash, Roopa; K. P., Nagarjun; Selvaraja, Shankar Kumar; Supradeepa, V. R.

2018-02-01

Demand for bandwidth in optical communications necessitates the development of scalable transceivers that cater to these needs. For this, in DWDM systems with/without Superchannels, the optical source needs to provide a large number of optical carriers. The conventional method of utilizing separate lasers makes the system bulky and inefficient. A multi-wavelength source which spans the entire C-band with sufficient power is needed to replace individual lasers. In addition, multi-wavelength sources at high repetition rates are necessary in various applications such as spectroscopy, astronomical spectrograph calibration, microwave photonics and arbitrary waveform generation. Here, we demonstrate a novel technique for equalized, multi-wavelength source generation which generates over 160 lines at 25GHz repetition rate, spanning the entire C-band with total power >700mW. A 25GHz Comb with 16 lines is generated around 1550nm starting with two individual lasers using a system of directly driven, cascaded intensity and phase modulators. This is then amplified to >1W using an optimized, Erbium-Ytterbium co-doped fiber amplifier. Subsequently, they are passed through Highly NonLinear Fiber at its zero-dispersion wavelength. Through cascaded Four Wave Mixing, a ten-fold increase in the number of lines is demonstrated. A bandwidth of 4.32 THz (174 lines, SNR>15 dB), covering the entire C-band is generated. Enhanced spectral broadening is enabled by two key aspects - Dual laser input provides the optimal temporal profile for spectral broadening while the comb generation prior to amplification enables greater power scaling by suppression of Brillouin scattering. The multi-wavelength source is extremely agile with tunable center frequency and repetition rate.

Balancing the health workforce: breaking down overall technical change into factor technical change for labour-an empirical application to the Dutch hospital industry.

PubMed

Blank, Jos L T; van Hulst, Bart L

2017-02-17

Well-trained, well-distributed and productive health workers are crucial for access to high-quality, cost-effective healthcare. Because neither a shortage nor a surplus of health workers is wanted, policymakers use workforce planning models to get information on future labour markets and adjust policies accordingly. A neglected topic of workforce planning models is productivity growth, which has an effect on future demand for labour. However, calculating productivity growth for specific types of input is not as straightforward as it seems. This study shows how to calculate factor technical change (FTC) for specific types of input. The paper first theoretically derives FTCs from technical change in a consistent manner. FTC differs from a ratio of output and input, in that it deals with the multi-input, multi-output character of the production process in the health sector. Furthermore, it takes into account substitution effects between different inputs. An application of the calculation of FTCs is given for the Dutch hospital industry for the period 2003-2011. A translog cost function is estimated and used to calculate technical change and FTC for individual inputs, especially specific labour inputs. The results show that technical change increased by 2.8% per year in Dutch hospitals during 2003-2011. FTC differs amongst the various inputs. The FTC of nursing personnel increased by 3.2% per year, implying that fewer nurses were needed to let demand meet supply on the labour market. Sensitivity analyses show consistent results for the FTC of nurses. Productivity growth, especially of individual outputs, is a neglected topic in workforce planning models. FTC is a productivity measure that is consistent with technical change and accounts for substitution effects. An application to the Dutch hospital industry shows that the FTC of nursing personnel outpaced technical change during 2003-2011. The optimal input mix changed, resulting in fewer nurses being needed to let demand meet supply on the labour market. Policymakers should consider using more detailed and specific data on the nature of technical change when forecasting the future demand for health workers.

VERAIn

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

Simunovic, Srdjan

2015-02-16

CASL's modeling and simulation technology, the Virtual Environment for Reactor Applications (VERA), incorporates coupled physics and science-based models, state-of-the-art numerical methods, modern computational science, integrated uncertainty quantification (UQ) and validation against data from operating pressurized water reactors (PWRs), single-effect experiments, and integral tests. The computational simulation component of VERA is the VERA Core Simulator (VERA-CS). The core simulator is the specific collection of multi-physics computer codes used to model and deplete a LWR core over multiple cycles. The core simulator has a single common input file that drives all of the different physics codes. The parser code, VERAIn, converts VERAmore » Input into an XML file that is used as input to different VERA codes.« less

Pipeline monitoring with unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Kochetkova, L. I.

2018-05-01

Pipeline leakage during transportation of combustible substances leads to explosion and fire thus causing death of people and destruction of production and accommodation facilities. Continuous pipeline monitoring allows identifying leaks in due time and quickly taking measures for their elimination. The paper describes the solution of identification of pipeline leakage using unmanned aerial vehicles. It is recommended to apply the spectral analysis with input RGB signal to identify pipeline damages. The application of multi-zone digital images allows defining potential spill of oil hydrocarbons as well as possible soil pollution. The method of multi-temporal digital images within the visible region makes it possible to define changes in soil morphology for its subsequent analysis. The given solution is cost efficient and reliable thus allowing reducing timing and labor resources in comparison with other methods of pipeline monitoring.

Spatio-mechanical EphA2/ephrin-A1 Signaling in Cancer Cells

NASA Astrophysics Data System (ADS)

Xu, Qian

2011-12-01

Communication strategies in nature are an integral part to the survival of multi-cellular organisms. Cell membranes provide the chemical environment in which intercellular signaling begins. The vast complexity of this signaling requires that a relatively conserved set of chemical constituents be able to generate enormous signal diversity. Spatial sorting of signaling molecules within the membrane allows for this diversity. My research uses synthetic lipid membranes, solid-state nanostructures, and high-resolution imaging to study a potentially novel spatio-mechanical regulatory mechanism in the EphA2 signaling pathway. My hypothesis is that the multi-scale organization of the EphA2 receptor in the cell membrane regulates its biochemical function. This hypothesis is motivated by the idea that extracellular mechanical inputs have an important role in intracellular signaling cascades.

Reconfigurable and non-volatile vertical magnetic logic gates

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

Butler, J., E-mail: jbutl001@ucr.edu; Lee, B.; Shachar, M.

2014-04-28

In this paper, we discuss the concept and prototype fabrication of reconfigurable and non-volatile vertical magnetic logic gates. These gates consist of two input layers and a RESET layer. The RESET layer allows the structure to be used as either an AND or an OR gate, depending on its magnetization state. To prove this concept, the gates were fabricated using a multi-layered patterned magnetic media, in which three magnetic layers are stacked and exchange-decoupled via non-magnetic interlayers. We demonstrate the functionality of these logic gates by conducting atomic force microscopy and magnetic force microscopy (MFM) analysis of the multi-layered patternedmore » magnetic media. The logic gates operation mechanism and fabrication feasibility are both validated by the MFM imaging results.« less

Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

NASA Astrophysics Data System (ADS)

Li, Zhanjie; Yu, Jingshan; Xu, Xinyi; Sun, Wenchao; Pang, Bo; Yue, Jiajia

2018-06-01

Hydrological models are important and effective tools for detecting complex hydrological processes. Different models have different strengths when capturing the various aspects of hydrological processes. Relying on a single model usually leads to simulation uncertainties. Ensemble approaches, based on multi-model hydrological simulations, can improve application performance over single models. In this study, the upper Yalongjiang River Basin was selected for a case study. Three commonly used hydrological models (SWAT, VIC, and BTOPMC) were selected and used for independent simulations with the same input and initial values. Then, the BP neural network method was employed to combine the results from the three models. The results show that the accuracy of BP ensemble simulation is better than that of the single models.

Flight Test of ASAC Aircraft Interior Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, Dan; Cabell, Ran; Cline, John; Sullivan, Brenda

1999-01-01

A flight test is described in which an active structural/acoustic control system reduces turboprop induced interior noise on a Raytheon Aircraft Company 1900D airliner. Control inputs to 21 inertial force actuators were computed adaptively using a transform domain version of the multichannel filtered-X LMS algorithm to minimize the mean square response of 32 microphones. A combinatorial search algorithm was employed to optimize placement of the force actuators on the aircraft frame. Both single frequency and multi-frequency results are presented. Reductions of up to 15 dB were obtained at the blade passage frequency (BPF) during single frequency control tests. Simultaneous reductions of the BPF and next 2 harmonics of 10 dB, 2.5 dB and 3.0 dB, were obtained in a multi-frequency test.

Xarray: multi-dimensional data analysis in Python

NASA Astrophysics Data System (ADS)

Hoyer, Stephan; Hamman, Joe; Maussion, Fabien

2017-04-01

xarray (http://xarray.pydata.org) is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays, which are the bread and butter of modern geoscientific data analysis. Key features of the package include label-based indexing and arithmetic, interoperability with the core scientific Python packages (e.g., pandas, NumPy, Matplotlib, Cartopy), out-of-core computation on datasets that don't fit into memory, a wide range of input/output options, and advanced multi-dimensional data manipulation tools such as group-by and resampling. In this contribution we will present the key features of the library and demonstrate its great potential for a wide range of applications, from (big-)data processing on super computers to data exploration in front of a classroom.

A high performance parallel algorithm for 1-D FFT

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

Agarwal, R.C.; Gustavson, F.G.; Zubair, M.

1994-12-31

In this paper the authors propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. They use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. They show that the multi-dimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. They implementedmore » this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.« less

6.4 Tb/s (32 × 200 Gb/s) WDM direct-detection transmission with twin-SSB modulation and Kramers-Kronig receiver

NASA Astrophysics Data System (ADS)

Zhu, Yixiao; Jiang, Mingxuan; Ruan, Xiaoke; Chen, Zeyu; Li, Chenjia; Zhang, Fan

2018-05-01

We experimentally demonstrate 6.4 Tb/s wavelength division multiplexed (WDM) direct-detection transmission based on Nyquist twin-SSB modulation over 25 km SSMF with bit error rates (BERs) below the 20% hard-decision forward error correction (HD-FEC) threshold of 1.5 × 10-2. The two sidebands of each channel are separately detected using Kramers-Kronig receiver without MIMO equalization. We also carry out numerical simulations to evaluate the system robustness against I/Q amplitude imbalance, I/Q phase deviation and the extinction ratio of modulator, respectively. Furthermore, we show in simulation that the requirement of steep edge optical filter can be relaxed if multi-input-multi-output (MIMO) equalization between the two sidebands is used.

Controllers, observers, and applications thereof

NASA Technical Reports Server (NTRS)

Gao, Zhiqiang (Inventor); Zhou, Wankun (Inventor); Miklosovic, Robert (Inventor); Radke, Aaron (Inventor); Zheng, Qing (Inventor)

2011-01-01

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed.

Method and system to perform energy-extraction based active noise control

NASA Technical Reports Server (NTRS)

Kelkar, Atul (Inventor); Joshi, Suresh M. (Inventor)

2009-01-01

A method to provide active noise control to reduce noise and vibration in reverberant acoustic enclosures such as aircraft, vehicles, appliances, instruments, industrial equipment and the like is presented. A continuous-time multi-input multi-output (MIMO) state space mathematical model of the plant is obtained via analytical modeling and system identification. Compensation is designed to render the mathematical model passive in the sense of mathematical system theory. The compensated system is checked to ensure robustness of the passive property of the plant. The check ensures that the passivity is preserved if the mathematical model parameters are perturbed from nominal values. A passivity-based controller is designed and verified using numerical simulations and then tested. The controller is designed so that the resulting closed-loop response shows the desired noise reduction.

Variable Neural Adaptive Robust Control: A Switched System Approach

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

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Integrating Syntax, Semantics, and Discourse DARPA Natural Language Understanding Program. Volume 1. Technical Report

DTIC Science & Technology

1989-09-30

26 9.1 Overview of SPQR ............................................................................. 26 9.2...domain. The ISR is the input to the selection component SPQR , whose function is to block semantically anomalous parses before they are sent to the...frequently occurring pairs of words, which is useful for identifying fixed multi-word expressions. 9. SELECTION The SPQR module (Selectional Pattern

Industry Strength Tool and Technology for Automated Synthesis of Safety-Critical Applications from Formal Specifications

DTIC Science & Technology

2015-11-01

28 2.3.4 Input/Output Automata ...various other modeling frameworks such as I/O Automata , Kahn Process Networks, Petri-nets, Multi-dimensional SDF, etc. are also used for designing...Formal Ideally suited to model DSP applications 3 Petri Nets Graphical Formal Used for modeling distributed systems 4 I/O Automata Both Formal

Assisting People with Developmental Disabilities to Improve Computer Pointing Efficiency through Multiple Mice and Automatic Pointing Assistive Programs

ERIC Educational Resources Information Center

Shih, Ching-Hsiang

2011-01-01

This study combines multi-mice technology (people with disabilities can use standard mice, instead of specialized alternative computer input devices, to achieve complete mouse operation) with an assistive pointing function (i.e. cursor-capturing, which enables the user to move the cursor to the target center automatically), to assess whether two…

Making a Difference. Visual Health Needs of People with a Learning Disability

ERIC Educational Resources Information Center

McGlade, Anne; Bickerstaff, David; Lindsay, Jennifer; McConkey, Roy; Jackson, Jonathan

2010-01-01

This article discusses the findings from a study to assess the impact of corrective eye treatment in adults with a learning disability. The Special Visual Assessment Clinic (SVAC) is an optometry led multi professional service delivered in a Resource Centre in Belfast, Northern Ireland. The study, which included user and carer input in its design,…

College of DuPage Planning Process: College of DuPage Planning, the Foundation for Decision Making.

ERIC Educational Resources Information Center

College of DuPage, Glen Ellyn, IL. Office of Research and Planning.

At College of DuPage (CD), in Glen Ellyn, Illinois, institutional planning is a multi-level, on-going process involving continuous college-wide input. The nine schematic levels in the CD planning process are interconnected and progress from global and broad-based issues, answering such questions as why the college exists, to concrete levels…

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Eure, Kenneth W.

1998-01-01

Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

NASA Astrophysics Data System (ADS)

Rit, S.; Vila Oliva, M.; Brousmiche, S.; Labarbe, R.; Sarrut, D.; Sharp, G. C.

2014-03-01

We propose the Reconstruction Toolkit (RTK, http://www.openrtk.org), an open-source toolkit for fast cone-beam CT reconstruction, based on the Insight Toolkit (ITK) and using GPU code extracted from Plastimatch. RTK is developed by an open consortium (see affiliations) under the non-contaminating Apache 2.0 license. The quality of the platform is daily checked with regression tests in partnership with Kitware, the company supporting ITK. Several features are already available: Elekta, Varian and IBA inputs, multi-threaded Feldkamp-David-Kress reconstruction on CPU and GPU, Parker short scan weighting, multi-threaded CPU and GPU forward projectors, etc. Each feature is either accessible through command line tools or C++ classes that can be included in independent software. A MIDAS community has been opened to share CatPhan datasets of several vendors (Elekta, Varian and IBA). RTK will be used in the upcoming cone-beam CT scanner developed by IBA for proton therapy rooms. Many features are under development: new input format support, iterative reconstruction, hybrid Monte Carlo / deterministic CBCT simulation, etc. RTK has been built to freely share tomographic reconstruction developments between researchers and is open for new contributions.

Measuring and explaining multi-directional inefficiency in the Malaysian dairy industry.

PubMed

Mohd Suhaimi, Nurul Aisyah Binti; de Mey, Yann; Oude Lansink, Alfons

2017-01-01

The purpose of this paper is to measure the technical inefficiency of dairy farms and subsequently investigate the factors affecting technical inefficiency in the Malaysian dairy industry. This study uses multi-directional efficiency analysis to measure the technical inefficiency scores on a sample of 200 farm observations and single-bootstrap truncated regression model to define factors affecting technical inefficiency. Managerial and program inefficiency scores are presented for intensive and semi-intensive production systems. The results reveal marked differences in the inefficiency scores across inputs and between production systems. Intensive systems generally have lowest managerial and program inefficiency scores in the Malaysian dairy farming sector. Policy makers could use this information to advise dairy farmers to convert their farming system to the intensive system. The results suggest that the Malaysian Government should redefine its policy for providing farm finance and should target young farmers when designing training and extension programs in order to improve the performance of the dairy sector. The existing literature on Southeast Asian dairy farming has neither focused on investigating input-specific efficiency nor on comparing managerial and program efficiency. This paper aims to fill this gap.

Adaptive multi-node multiple input and multiple output (MIMO) transmission for mobile wireless multimedia sensor networks.

PubMed

Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

2013-10-02

Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.

Adaptive Multi-Node Multiple Input and Multiple Output (MIMO) Transmission for Mobile Wireless Multimedia Sensor Networks

PubMed Central

Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

2013-01-01

Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase. PMID:24152920

Creating a spatial multi-criteria decision support system for energy related integrated environmental impact assessment

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

Wanderer, Thomas, E-mail: thomas.wanderer@dlr.de; Herle, Stefan, E-mail: stefan.herle@rwth-aachen.de

2015-04-15

By their spatially very distributed nature, profitability and impacts of renewable energy resources are highly correlated with the geographic locations of power plant deployments. A web-based Spatial Decision Support System (SDSS) based on a Multi-Criteria Decision Analysis (MCDA) approach has been implemented for identifying preferable locations for solar power plants based on user preferences. The designated areas found serve for the input scenario development for a subsequent integrated Environmental Impact Assessment. The capabilities of the SDSS service get showcased for Concentrated Solar Power (CSP) plants in the region of Andalusia, Spain. The resulting spatial patterns of possible power plant sitesmore » are an important input to the procedural chain of assessing impacts of renewable energies in an integrated effort. The applied methodology and the implemented SDSS are applicable for other renewable technologies as well. - Highlights: • The proposed tool facilitates well-founded CSP plant siting decisions. • Spatial MCDA methods are implemented in a WebGIS environment. • GIS-based SDSS can contribute to a modern integrated impact assessment workflow. • The conducted case study proves the suitability of the methodology.« less

Design for performance enhancement in feedback control systems with multiple saturating nonlinearities. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kapasouris, Petros

1988-01-01

A systematic control design methodology is introduced for multi-input/multi-output systems with multiple saturations. The methodology can be applied to stable and unstable open loop plants with magnitude and/or rate control saturations and to systems in which state limitations are desired. This new methodology is a substantial improvement over previous heuristic single-input/single-output approaches. The idea is to introduce a supervisor loop so that when the references and/or disturbances are sufficiently small, the control system operates linearly as designed. For signals large enough to cause saturations, the control law is modified in such a way to ensure stability and to preserve, to the extent possible, the behavior of the linear control design. Key benefits of this methodology are: the modified compensator never produces saturating control signals, integrators and/or slow dynamics in the compensator never windup, the directional properties of the controls are maintained, and the closed loop system has certain guaranteed stability properties. The advantages of the new design methodology are illustrated by numerous simulations, including the multivariable longitudinal control of modified models of the F-8 (stable) and F-16 (unstable) aircraft.

Shapley value-based multi-objective data envelopment analysis application for assessing academic efficiency of university departments

NASA Astrophysics Data System (ADS)

Abing, Stephen Lloyd N.; Barton, Mercie Grace L.; Dumdum, Michael Gerard M.; Bongo, Miriam F.; Ocampo, Lanndon A.

2018-02-01

This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this limitation and reduce the number of decision variables, multi-objective data envelopment analysis (MODEA) approach previously presented in the literature is applied. The MODEA approach applies Shapley value as a cooperative game to determine the appropriate weights and efficiency score of each category of inputs. To illustrate the performance of the adopted approach, a case study is conducted in a university in the Philippines. The input variables are academic staff, non-academic staff, classrooms, laboratories, research grants, and department expenditures, while the output variables are the number of graduates and publications. The results of the case study revealed that all DMUs are inefficient. DMUs with efficiency scores close to the ideal efficiency score may be emulated by other DMUs with least efficiency scores.

Control oriented concentrating solar power (CSP) plant model and its applications

NASA Astrophysics Data System (ADS)

Luo, Qi

Solar receivers in concentrating solar thermal power plants (CSP) undergo over 10,000 start-ups and shutdowns, and over 25,000 rapid rate of change in temperature on receivers due to cloud transients resulting in performance degradation and material fatigue in their expected lifetime of over 30 years. The research proposes to develop a three-level controller that uses multi-input-multi-output (MIMO) control technology to minimize the effect of these disturbances, improve plant performance, and extend plant life. The controller can be readily installed on any vendor supplied state-of-the-art control hardware. We propose a three-level controller architecture using multi-input-multi-output (MIMO) control for CSP plants that can be implemented on existing plants to improve performance, reliability, and extend the life of the plant. This architecture optimizes the performance on multiple time scalesreactive level (regulation to temperature set points), tactical level (adaptation of temperature set points), and strategic level (trading off fatigue life due to thermal cycling and current production). This controller unique to CSP plants operating at temperatures greater than 550 °C, will make CSPs competitive with conventional power plants and contribute significantly towards the Sunshot goal of 0.06/kWh(e), while responding with agility to both market dynamics and changes in solar irradiance such as due to passing clouds. Moreover, our development of control software with performance guarantees will avoid early stage failures and permit smooth grid integration of the CSP power plants. The proposed controller can be implemented with existing control hardware infrastructure with little or no additional equipment. In the thesis, we demonstrate a dynamics model of CSP, of which different components are modelled with different time scales. We also show a real time control strategy of CSP control oriented model in steady state. Furthermore, we shown different controllers design for disturbance rejection and reference tracking to handle complex receiver dynamics under system disturbance and measurement noise. At last, we show different applications of this control oriented CSP model including life cycle enhancement and electricity load forecasting using both neural network and regression tree.

Large Scale Crop Classification in Ukraine using Multi-temporal Landsat-8 Images with Missing Data

NASA Astrophysics Data System (ADS)

Kussul, N.; Skakun, S.; Shelestov, A.; Lavreniuk, M. S.

2014-12-01

At present, there are no globally available Earth observation (EO) derived products on crop maps. This issue is being addressed within the Sentinel-2 for Agriculture initiative where a number of test sites (including from JECAM) participate to provide coherent protocols and best practices for various global agriculture systems, and subsequently crop maps from Sentinel-2. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this abstract, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. For this, an ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for large scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine [1-2]. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy and kappa coefficient. The obtained classification map is also validated through estimated crop and forest areas and comparison to official statistics. 1. A.Yu. Shelestov et al., "Geospatial information system for agricultural monitoring," Cybernetics Syst. Anal., vol. 49, no. 1, pp. 124-132, 2013. 2. J. Gallego et al., "Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations," J. Autom. Inform. Scie., vol. 44, no. 5, pp. 67-80, 2012.