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IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG)

作者： Prajwal K. Gautam Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA

To characterize the power performance of wind turbine generators (WTGs), the International Electrotechnical Commission provides a power curve for each one based on ten minutes average. This approach causes the problem of systematic errors because of the nonlinear relationship between power and wind speed. In this paper, recurrent neural networks are introduced as an alternative approach to model this nonlinear relationship. Based on actual wind speed input and wind power output, an input-output relationship is established for a permanent magnet synchronous machine based wind power generator. Experimental studies are carried out to the develop power curve that characterize dynamic power performance of the wind turbine generator. These dynamic power performance models of wind turbine generators can be used as operational and planning models in control centers. Some preliminary results on the integration of a neural network wind generator model in a micro-grid simulation is presented.

关键词： Wind turbines Wind speed Wind power generation Generators Neural networks Testing

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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

Pedestrian detection based on LIDAR-driven sliding window an...

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IEEE Symposium on intelligent Vehicle

作者： Luciano Oliveira Urbano Nunes Intelligent Vision Research Laboratory Federal University Bahia Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.

关键词： Detectors Laser radar Feature extraction Support vector machines Training Probabilistic logic Vectors

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Method of SLAM with Kinect

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 2013年第SUPPL.2期44卷 28-32页

作者： Li, Haiyang Li, Hongbo Lin, Ying Liu, Heping Yang, Dongfang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China

A method for Kinect sensor was presented based on SLAM (simultaneous localization and mapping) problem via inverse depth parameterization model, where the depth of features from Kinect was employed to improve the convergence of method. To further improve the accuracy and robustness, SURF (speed up robust features) feature detection operator was adopted to extract image features, and extended Kalman filter (EKF) was used to estimate the trajectory of camera and the position of features. Experimental results are given to demonstrate the effectiveness and applicability of the proposed method.

关键词： Extended Kalman filters

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CNN based power system transient stability margin and voltage stability index prediction

CNN based power system transient stability margin and voltag...

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IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG)

作者： Karthikeyan Balasubramaniam Ganesh Kumar Venayagamoorthy Neville Watson Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical & Computer Engineering Clemson University Clemson SC USA Department of Electrical Engineering University of Canterbury Christchurch New Zealand

Operators at electric grid control centers are faced with the task of making important decisions in real-time. With the plethora of data available it becomes important to extract information from the available data, based on which knowledge of system condition can be formed. This knowledge can then be used in decision making. Metrics such as transient stability margin (TSM) and voltage stability load index (VSLI) help in assessing the stability of the system. In this study, cellular neural network (CNN) based stability margin prediction system is developed in a distributed computing framework. The developed system not only extracts information from available data but also predicts the same, one step ahead of time. Moreover, the framework employed uses distributed computing and hence could be used on a large scale power system with a linear increase in computation time instead of an exponential increase. A reduced version of New Zealand's South Island power system is used as the test system to demonstrate the feasibility of CNNs for TSM and VSLI prediction.

关键词： Power system stability Generators Stability criteria Rotors Transient analysis

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Power system controller design using multi-population PBIL

Power system controller design using multi-population PBIL

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IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG)

作者： Komla Agbenyo Folly Ganesh Kumar Venayagamoorthy Department of Electrical Engineering University of Cape Town Cape Town South Africa Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical & Computer Engineering Clemson University SC USA

The application of a multi-population based Population-Based Incremental Learning (PBIL) to power system controller design is presented in this paper. PBIL is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. Single population PBIL has recently received increasing attention in various engineering fields due to its effectiveness., easy implementation and robustness. Despite these strengths., PBIL still suffers from issues of loss of diversity in the population. The use of multi-population is seen as one way of increasing the diversity in the population. The approach is applied to power system controller design. Simulations results show that the multi-population PBIL approach performs better than the standard PBIL and is as effective as PBIL where adaptive learning is used.

关键词： Vectors Sociology Statistics Standards Power system stability Optimization Control systems

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A hybrid genetic algorithm-neural network for modeling of periodic nonlinearity in three-longitudinal-mode laser heterodyne interferometer

A hybrid genetic algorithm-neural network for modeling of pe...

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Iranian Conference on electrical engineering (ICEE)

作者： Saeed Olyaee Reza Ebrahimpur Somayeh Esfandeh Nano-photonics and Optoelectronics Research Laboratory (NORLab) and Brain and Intelligent Systems Research Laboratory Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University Tehran Iran

ISBN: (纸本)9781467356329

Laser heterodyne interferometer is a common nano-metrology system which is used for high-accuracy displacement measurements in industry. Measurement accuracy in this system is limited by the periodic nonlinearity error. In this paper, the nonlinearity error of the nano-metrology interferometric system based on three-longitudinal-mode laser heterodyne interferometer has been modeled by artificial neural network (ANN) and hybrid genetic algorithm-neural network (hybrid GA-ANN). The real code version of genetic algorithm (GA) is used. Genetic operators and parameters are set and designed accurately for optimizing the neural network. The results indicate that nonlinearity error can be effectively modeled by hybrid GA-ANN method and contains minimum mean square error (MSE) compared to the neural network.

关键词： Genetic algorithms Neural networks Training Measurement by laser beam Neurons Optical interferometry Laser modes

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Incommensurate order fractional optimal control: Application to treatment of psychiatric disorders

Incommensurate order fractional optimal control: Application...

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Iranian Conference on electrical engineering (ICEE)

作者： S. S. Tabatabaei M. J. Yazdanpanah M. S. Tavazoei Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Advanced Control Systems Laboratory Control and Intelligent Processing Center of Excellence School of Electrical and Computer Tehran Iran Electrical Engineering Department Sharif University of Technology Tehran Iran

ISBN: (纸本)9781467356329

The main point of this paper is to present a time domain strategy for drug dosage to treat psychiatric disorders. A time domain model of emotion is obtained from an extension of a recently developed fractional nonlinear dynamic model of happiness. First, the Fractional Optimal Control law for incommensurate multi state systems is obtained. It will be then applied as an optimal drug administration procedure in the line of psychiatric disorders treatment. Results of this paper show that optimal control scheme is a proper approach to face the difficulties of analysis and control the incommensurate systems. It can be also clearly seen from the simulation results that this approach is very effective in the case that control methods are used as treatment techniques.

关键词： Mathematical model Drugs Optimal control Numerical models Differential equations Equations

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Using general sound descriptors for early autism detection

Using general sound descriptors for early autism detection

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Asian Control Conference

作者： Seyyed Hamid R. Ebrahimi Motlagh Hadi Moradi Hamidreza Pouretemad Advanced Robotics and Intelligent Systems Laboratory Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran ARIS and CIPCE School of ECE College of Engineering University of Tehran Iran Shahid Beheshti University Tehran Tehran IR

ISBN: (纸本)9781467357661

Early detection of autism is crucial for successfully dealing with it and reduce/eliminate its effects. In other words, early treatment can make a big difference in the lives of many children with this disorder. Consequently, in this study the pattern recognition algorithms are used to determine the unique features of the voice of autistic children to distinguish between the autistic children and normal children between ages 2 and 3. These descriptors extract various audio features such as temporal features, energy features, harmonic features, perceptual and spectral features. Two feature selection methods are used and the results are compared. One method is based on comparing the effect of using all of a group features together and another method compares the effect of using features one by one. The selected features are used to classify selected children into autistic and non-autistic ones. The results show 96.17 percent accuracy. After feature selection, we classified data using S.V.M classifier for recognizing two types of input data.

关键词： Autism Feature extraction Mel frequency cepstral coefficient Educational institutions Harmonic analysis Accuracy Motion pictures

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Finding Objects with Segmentation Strategy based Multi Robot Exploration in Unknown Environment

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Procedia - Social and Behavioral Sciences 2013年 97卷 580-586页

作者： Reza Arezoumand Syamsiah Mashohor Mohammad Hamiruce Marhaban Intelligent System and Robotics Laboratory Institute of Advanced Technology Universiti Putra Malaysia 43400 Serdang Selangor Malaysia Department of Computer & Communication Systems Faculty of Engineering Universiti Putra Malaysia 43400 Serdang Selangor Malaysia Department of Electrical and Electronic Engineering University Putra Malaysia 43400 Serdang Selangor Malaysia

In this paper we address exploration algorithm in flat experimental environment with colored objects for multi robots system. The aim of exploration in unknown environment is finding target points like mine detection in outdoor environment without any positioning device. Two algorithms were investigated in this paper one is frontier based random search algorithm and the second is efficient algorithm based on segmentation strategy. To improve efficiency, each robot had to go to different regions to avoid cumulating robots in one region. Constructed maps for all four regions could be shared and navigation could be done more effectively. For constructing map robot can use on built range finder sensor or using vision based systems. Also the algorithm using segmentation strategy is using frontier base algorithm for exploring divided area. Both algorithm implemented and analyzed in Player/Stage simulation. The result was compared and showed the efficiency of the designed algorithm based segmentation strategy. In simulation this algorithm is tested with different number of robots to achieve better view of efficiency for proposed algorithm in different type of environment like harsh environment as possibility of losing some robots.

关键词： Multi-robot exploration Flocking Exploration ratio Trajectory prediction

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COMPRESSIVE SENSING-BASED IMAGE DENOISING USING ADAPTIVE MULTIPLE SAMPLING AND OPTIMAL ERROR TOLERANCE

COMPRESSIVE SENSING-BASED IMAGE DENOISING USING ADAPTIVE MUL...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Wonseok Kang Eunsung Lee Eunjung Chea Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479903573

In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.

关键词： Tolerances physiologic tolerance Sampling system recovery image denoising image representation images Signal analysis Statistical analysis Statistical methods image denoising algorithm

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