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Articles 31 - 60 of 107
Full-Text Articles in Engineering
Modeling And Simulation Of Mooring Force Prediction Based On Improved Ga-Bp Network, Shifeng Li, Zhanzhi Qiu
Modeling And Simulation Of Mooring Force Prediction Based On Improved Ga-Bp Network, Shifeng Li, Zhanzhi Qiu
Journal of System Simulation
Abstract: According to the mooring security and early warning control requirement of the large open sea terminal, a ship mooring force prediction model based on genetic algorithm and BP network was studied. Environmental dynamic factors were considered and a model structure was determined by a weight statistics method; the learning method was improved by individual parent information and contemporary individual local gradient information; according to the improved model, a ship mooring force prediction method of the open sea terminal was proposed. The simulation results show that the performance of the prediction model has improved in the iteration number, …
Genetic Algorithm For Solving Multi-Objective Dynamic Flexible Job Shop Scheduling, Wang Chun, Zhang Ming, Zhicheng Ji, Wang Yan
Genetic Algorithm For Solving Multi-Objective Dynamic Flexible Job Shop Scheduling, Wang Chun, Zhang Ming, Zhicheng Ji, Wang Yan
Journal of System Simulation
Abstract: To solve the scheduling problem of mold workshop in a toy factory with dynamic and flexible features, a mathematical model was established by introducing virtual operation and virtual working hours. Based on the strategies of periodic scheduling combined with dynamic event scheduling as well as the rolling window scheduling operation technology, dynamic scheduling was transformed into several continuous static scheduling windows, under which multi-objective genetic algorithm was used to solve the model. The priority of operation scheduling was given in different dynamic events. In addition, the encoding and anti-encoding of chromosome's operation sequence were made based on the proposed …
A New Biometric Identity Recognition System Based On A Combination Of Superior Features In Finger Knuckle Print Images, Hadis Heidari, Abdolah Chalechale
A New Biometric Identity Recognition System Based On A Combination Of Superior Features In Finger Knuckle Print Images, Hadis Heidari, Abdolah Chalechale
Turkish Journal of Electrical Engineering and Computer Sciences
Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two …
A Fast Text Similarity Measure For Large Document Collections Using Multireference Cosine And Genetic Algorithm, Hamid Mohammadi, Seyed Hossein Khasteh
A Fast Text Similarity Measure For Large Document Collections Using Multireference Cosine And Genetic Algorithm, Hamid Mohammadi, Seyed Hossein Khasteh
Turkish Journal of Electrical Engineering and Computer Sciences
One of the critical factors that make a search engine fast and accurate is a concise and duplicate free index. In order to remove duplicate and near-duplicate (DND) documents from the index, a search engine needs a swift and reliable DND text document detection system. Traditional approaches to this problem, such as brute force comparisons or simple hash-based algorithms, are not suitable as they are not scalable and are not capable of detecting near-duplicate documents effectively. In this paper, a new signature-based approach to text similarity detection is introduced, which is fast, scalable, and reliable and needs less storage space. …
Chemical Disease Relation Extraction Task Using Genetic Algorithm With Two Novelvoting Methods For Classifier Subset Selection, Stanley Chika Onye, Nazi̇fe Di̇mi̇li̇ler, Ari̇f Akkeleş
Chemical Disease Relation Extraction Task Using Genetic Algorithm With Two Novelvoting Methods For Classifier Subset Selection, Stanley Chika Onye, Nazi̇fe Di̇mi̇li̇ler, Ari̇f Akkeleş
Turkish Journal of Electrical Engineering and Computer Sciences
Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system (MCS) for the extraction of chemical-induced disease relations. A genetic algorithm (GA) is employed to select classifier ensembles from a pool of base classifiers. Moreover, the voting method used for combining the members of each of the ensembles is also selected during evolution in the GA framework. The performances of the MCSs are determined by the algorithms used for selecting the classifiers, the diversity among the selected classifiers, and the voting method used in the classifier combination. The …
A Ga-Based Adaptive Mechanism For Sensorless Vector Control Of Induction Motor Drives For Urban Electric Vehicles, Asma Boulmane, Youssef Zidani, Driss Belkhayat, Marouane Bouchouirbat
A Ga-Based Adaptive Mechanism For Sensorless Vector Control Of Induction Motor Drives For Urban Electric Vehicles, Asma Boulmane, Youssef Zidani, Driss Belkhayat, Marouane Bouchouirbat
Turkish Journal of Electrical Engineering and Computer Sciences
Induction motors are more attractive to car manufacturers because they are more robust and more cost effective to maintain in comparison with other types of electric machines. The evolution of their control makes them more efficient and less expensive. However, a new control technique known as sensorless control is being used to simplify the implementation of electric machines in electric vehicles. This technique involves replacing the flux and speed sensors with an observer. The estimation of these elements is based on the measurement of currents and voltages. The main purpose of the present study is to design a novel robust …
An Improved Memetic Genetic Algorithm Based On A Complex Network As Asolution To The Traveling Salesman Problem, Hadi Mohammadi, Kamal Mirzaie, Mohammad Reza Mollakhalili Meybodi
An Improved Memetic Genetic Algorithm Based On A Complex Network As Asolution To The Traveling Salesman Problem, Hadi Mohammadi, Kamal Mirzaie, Mohammad Reza Mollakhalili Meybodi
Turkish Journal of Electrical Engineering and Computer Sciences
A genetic algorithm (GA) is not a good option for finding solutions around in neighborhoods. The current study applies a memetic algorithm (MA) with a proposed local search to the mutation operator of a genetic algorithm in order to solve the traveling salesman problem (TSP). The proposed memetic algorithm uses swap, reversion and insertion operations to make changes in the solution. In the basic GA, unlike in the real world, the relationship between generations has not been considered. This gap is resolved using the proposed complex network to allow selection among possible solutions. The degree measure has been used for …
Optimal Mission Planning Of Autonomous Mobile Agents For Applications In Microgrids, Sensor Networks, And Military Reconnaissance, Casey D. Majhor
Optimal Mission Planning Of Autonomous Mobile Agents For Applications In Microgrids, Sensor Networks, And Military Reconnaissance, Casey D. Majhor
Dissertations, Master's Theses and Master's Reports
As technology advances, the use of collaborative autonomous mobile systems for various applications will become evermore prevalent. One interesting application of these multi-agent systems is for autonomous mobile microgrids. These systems will play an increasingly important role in applications such as military special operations for mobile ad-hoc power infrastructures and for intelligence, surveillance, and reconnaissance missions. In performing these operations with these autonomous energy assets, there is a crucial need to optimize their functionality according to their specific application and mission. Challenges arise in determining mission characteristics such as how each resource should operate, when, where, and for how long. …
Fuzzy Genetic Based Dynamic Spectrum Allocation (Fgdsa) Approach For Cognitive Radio Sensor Networks, Ganesan Rajesh, Xavier Mercilin Raajini, Kulandairaj Martin Sagayam, Bharat Bhushan, Utku Kose
Fuzzy Genetic Based Dynamic Spectrum Allocation (Fgdsa) Approach For Cognitive Radio Sensor Networks, Ganesan Rajesh, Xavier Mercilin Raajini, Kulandairaj Martin Sagayam, Bharat Bhushan, Utku Kose
Turkish Journal of Electrical Engineering and Computer Sciences
Cognitive Radio Sensor Network (CRSN) is known as a distributed network of wireless cognitive radio sensor nodes. Such system senses an event signal and ensures collaborative dynamic communication processes over the spectrum bands. Here, the concept of Dynamic Spectrum Access (DSA) denes the method of reaching progressively to the unused range of spectrum band. As among the essential CRSN user types, the Primary User (PU) has the license to access the spectrum band. On the other hand, the Secondary User (SU) tries to access the unused spectrum eciently, by not disturbing the PU. Considering that issue, this study introduces a …
Research On Intelligent Clustering Algorithm For Complex Water Wireless Network Surveillance, Hua Xiang, Hongtao Liang, Zhaoxin Dong, Wang Zhao, Hongjuan Yao, Baohua Li, Bingqing Jiang
Research On Intelligent Clustering Algorithm For Complex Water Wireless Network Surveillance, Hua Xiang, Hongtao Liang, Zhaoxin Dong, Wang Zhao, Hongjuan Yao, Baohua Li, Bingqing Jiang
Journal of System Simulation
Abstract: The clustering of irregular networks will cause load imbalance, which results in the phenomenon of “energy hot zone”. Aiming at the unreasonable topology of irregular network clustering, an intelligent clustering algorithm based on genetic strategy is proposed for the wireless network surveillance of complex water system. An irregular complex water topology model and an energy consumption model are built, and a genetic clustering strategy is designed via the principle of minimum energy consumption. The P matrix coding method is given, which avoids the squared increment of data calculation. Simultaneously, an adaptive genetic operator and a fuzzy modified operator are …
Size Optimization Method Of 6r Manipulator Based On Global Maneuverability, Xianhua Li, Xuesong Shi, Lü Lei, Leigang Zhang, Song Tao
Size Optimization Method Of 6r Manipulator Based On Global Maneuverability, Xianhua Li, Xuesong Shi, Lü Lei, Leigang Zhang, Song Tao
Journal of System Simulation
Abstract: Aiming at the six-DOF manipulator of service robot, the structure size parameters are optimized and analyzed. The global manipulability index of the manipulator workspace is defined, the coordinate system of the position and attitude is established, and the reachability of the position and attitude is calculated with the inverse solution of the manipulator, and the global manipulability index of the manipulator is proposed. Taking the maximum value of this index as the optimization target, the size parameters of the connecting rod of the manipulator are optimized by genetic algorithm, the size of the optimized connecting rod and the global …
Path Planning For Mobile Sink Based On Enhanced Ant Colony Optimization Algorithm In Wireless Sensor Networks, Shanshan Ji
Path Planning For Mobile Sink Based On Enhanced Ant Colony Optimization Algorithm In Wireless Sensor Networks, Shanshan Ji
Journal of System Simulation
Abstract: To reduce the energy consumption and sink mobile distance of mobile sink wireless sensor networks simultaneously, a path planning algorithm for mobile sink based on enhanced ant colony optimization algorithm in wireless sensor networks is proposed. Genetic operators are introduced to ant colony optimization algorithm in order to prevent ant colony optimization premature. The non-uniform of data distribution is considered as the constraint condition, the network lifetime and sink mobile distance are considered as a multi-objective problem, and the enhanced ant colony optimization is adopted to search the Pareto sub-optimal sets of rendezvous points. The simulation results show that …
Ads-B In Based Conflict Prediction And Conflict-Free Trajectory Planning For Multi-Aircraft, Siyuan Zhang, Xianying Li, Xiaoyun Shen
Ads-B In Based Conflict Prediction And Conflict-Free Trajectory Planning For Multi-Aircraft, Siyuan Zhang, Xianying Li, Xiaoyun Shen
Journal of System Simulation
Abstract: For free flight of future, it is necessary to continuously detect conflicts and plan safe flight paths. Through in-depth analysis of detection principle of TCAS, combined with the characteristics of ADS-B data, the target within the scope of ADS-B IN surveillance is classified and given a risk factor, and the TCAS function is implemented in the ADS-B IN simulation software. For complex conflict scenarios with multi-aircraft, by meshing the conflict region and discretizing the flight procedure, the genetic algorithm is then used to calculate the optimal conflict-free trajectory based on risk factors. Two common multi-aircraft conflict scenarios are …
Multi-Channel Transmission Optimization Of Campus Internet Of Things Based On Pheromone Genetic Algorithm, Zhiyong Chen, Liu Hao
Multi-Channel Transmission Optimization Of Campus Internet Of Things Based On Pheromone Genetic Algorithm, Zhiyong Chen, Liu Hao
Journal of System Simulation
Abstract: Aiming at the difficulty in selecting and optimizing the multi-path transmission of campus Internet of Things information, an optimization algorithm of network multi-path transmission based on intelligent optimization algorithm is proposed. Based on the standard genetic algorithm and the concept of pheromone concentration in ant colony algorithm, this algorithm improves the global optimization ability and convergence efficiency by controlling the evolution direction of individuals, and designs and constructs an evaluation index mathematical model which conforms to the characteristics of multi-channel information transmission optimization in the Internet of Things. The mathematical model of the evaluation index realizes the multi-channel comprehensive …
Multi-Objective Operation Scheduling Optimization Of Shipborne-Equipment Based On Genetic Algorithm, Jinsong Bao, Zhiqiang Li, Yaqin Zhou
Multi-Objective Operation Scheduling Optimization Of Shipborne-Equipment Based On Genetic Algorithm, Jinsong Bao, Zhiqiang Li, Yaqin Zhou
Journal of System Simulation
Abstract: Multi-objective operation scheduling of shipborne equipment is a complex combinational optimization problem under multi-task system. Existing research focuses mainly on single-objective optimization while several other objectives need to be considered during real operation such as path, duration, resource, etc. Considering the operation scheduling before exporting of an amphibious landing ship as the research object, both scheduling duration and resource requirement under the precedence constraint are optimized. The mathematical model of this multi-objective operation scheduling is established and solved using genetic algorithm. A fitness function which can be self-adaptively adjusted is designed; an adapting encoding strategy, a crossover operator, and …
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Image Classification Based On Sparse Autoencoder And Support Vector Machine, Liu Fang, Lixia Lu, Hongjuan Wang, Wang Xin
Journal of System Simulation
Abstract: A new algorithm of image classification based on the sparse autoencoder and the support vector machine was proposed in view of the drawbacks that the single layer sparse autoencoder for feature learning is easy to lose the deep abstract feature and the features lack the robustness. The deep sparse autoencoder is constructed to learn each image layer and the feature of each layer is automatically extracted. The each feature weights and the reorganized set of feature are obtained according to the feature weighting method. By combining the strong global search ability of genetic algorithm and the excellent performance of …
Current Sensor Fault Diagnosis For Induction Motor In Vector Control System, Sun Kai, Baina He, Sarah Odofin, Gu Yu
Current Sensor Fault Diagnosis For Induction Motor In Vector Control System, Sun Kai, Baina He, Sarah Odofin, Gu Yu
Journal of System Simulation
Abstract: A current sensor fault diagnosis method of induction motor in vector control system is proposed. A state-space form including sensor faults and environmental disturbances/noises of induction machine is described. An augmented observer is designed to simultaneously estimate system states, and current sensor faults. To attenuate the effects from the environmental disturbances/noises, a genetic algorithm is employed to design observer gain by minimizing the estimation error against environmental disturbances and noises. A simulation model based on Matlab and real-data of the induction motor collected by experiment is utilized to validate the proposed methods, which show the efficiency of the proposed …
Modeling And Simulation Of Pid Networked Control Systems Based On Neural Network, Zhanzhi Qiu, Shifeng Li
Modeling And Simulation Of Pid Networked Control Systems Based On Neural Network, Zhanzhi Qiu, Shifeng Li
Journal of System Simulation
Abstract: According to the problems of the delay compensation and PID parameters tuning of networked control systems, a class of rapid PID networked control systems based on improved BP network was proposed. Considering the problems of obtaining hidden layer nodes number and local optimum of the BP network delay prediction model, a calculation method was proposed to obtain hidden layer nodes number, and an improved genetic algorithm was proposed to train the BP network. Considering the problems of integral saturation, parameters tuning and model mismatch of the PID network control systems, a PID parameter adjuster was designed based on …
Improved Bp Neural Network Of Heat Load Forecasting Based On Temperature And Date Type, Li Qi, Zhao Feng
Improved Bp Neural Network Of Heat Load Forecasting Based On Temperature And Date Type, Li Qi, Zhao Feng
Journal of System Simulation
Abstract: The heat load forecasting provides data support for urban district heating systems, which is the basis of need-based heating. The change of heat load is greatly influenced by various exterior factors, especially the outdoor temperature. To meet demand of heating system, save energy and balance the comfort of human body, a kind of improved BP neural network method is proposed by temperature and date type. The temperature and date type are quantified and the heat load forecasting model is established by using BP neural network. To guarantee prediction accuracy, the genetic algorithm is used to optimize the weights and …
Simulation Of Wind Power Prediction Based On Improved Elm, Wang Hao, Wang Yan, Zhicheng Ji
Simulation Of Wind Power Prediction Based On Improved Elm, Wang Hao, Wang Yan, Zhicheng Ji
Journal of System Simulation
Abstract: To predict the range of ultra-short-term wind power fluctuation effectively, a combined forecasting model based on fuzzy information granulation (FIG) and genetic algorithm optimization extreme learning machine (GA-ELM) is proposed. The parameters of wind power are granulated by fuzzy information, and the corresponding valid information including the maximal value, the minimum value, and the general average value in time series window is further extracted. By integrating the effective components of each parameter as training samples, the GA-ELM-based prediction model is established. The range of wind power fluctuation in next time series is forecasted through using the optimized model. The …
Fuzzy Sliding Backstepping Mode Control For Flight Simulator Servo Based On Friction And Disturbance Compensation, Huibo Liu, Shanglei Liu
Fuzzy Sliding Backstepping Mode Control For Flight Simulator Servo Based On Friction And Disturbance Compensation, Huibo Liu, Shanglei Liu
Journal of System Simulation
Abstract: Considering friction, modeling errors and other uncertainties of flight simulator servo system, a compensation strategy which combines model-based friction compensation with nonlinear disturbance observer compensation was proposed. First, the friction is modeled , whose parameters are identified by using genetic algorithm, and using the identified model to compensate. Second, using a nonlinear disturbance observer to estimate the modeling errors, friction less-compensation or over-compensation and other uncertainties, and using this observed value to compensate. The system adopted sliding backstepping controller to ensure the stabilization of the system. Finally, the fuzzy algorithm is adopted to adjust the switching gain of sliding …
Simulation Optimization On Multi-Ports Slot Plan Problem Considering Dispatching Sequence Of Containers In Yard, Zhang Yu, Huimin Cheng, Xu Jin, Tian Wei, Junfeng Sun
Simulation Optimization On Multi-Ports Slot Plan Problem Considering Dispatching Sequence Of Containers In Yard, Zhang Yu, Huimin Cheng, Xu Jin, Tian Wei, Junfeng Sun
Journal of System Simulation
Abstract: The multi-ports slot plan problem considering dispatching sequence of containers in yard is solved by an integer linear programming model, which minimizes heeling moment. The influences of different dispatching rules on solving the problem are simulated and analyzed through the programming model. Accordingly, a simulation optimization model based on genetic algorithm is constructed in order to enhance the computational efficiency. The simulation optimization model simulates the process of dispatching containers and loading vessel. The feasible solution is constructed through rules sets and inputted into the optimization model. An efficient encoding and decoding solutions are developed in genetic algorithm, …
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the …
Performance Comparison Of Optimization Algorithms In Lqr Controller Design For A Nonlinear System, Ümi̇t Önen, Abdullah Çakan, İlhan İlhan
Performance Comparison Of Optimization Algorithms In Lqr Controller Design For A Nonlinear System, Ümi̇t Önen, Abdullah Çakan, İlhan İlhan
Turkish Journal of Electrical Engineering and Computer Sciences
The development and improvement of control techniques has attracted many researchers for many years. Especially in the controller design of complex and nonlinear systems, various methods have been proposed to determine the ideal control parameters. One of the most common and effective of these methods is determining the controller parameters with optimization algorithms.In this study, LQR controller design was implemented for position control of the double inverted pendulum system on a cart. First of all, the equations of motion of the inverted pendulum system were obtained by using Lagrange formulation. These equations were linearized by Taylor series expansion around the …
Transmission Expansion Planning Based On A Hybrid Genetic Algorithm Approachunder Uncertainty, Ercan Şenyi̇ği̇t, Selçuk Mutlu, Bi̇lal Babayi̇ği̇t
Transmission Expansion Planning Based On A Hybrid Genetic Algorithm Approachunder Uncertainty, Ercan Şenyi̇ği̇t, Selçuk Mutlu, Bi̇lal Babayi̇ği̇t
Turkish Journal of Electrical Engineering and Computer Sciences
Transmission expansion planning (TEP) is one of the key decisions in power systems. Its impact on the system?s operation is excessive and long-lived. The aim of TEP is to determine new transmission lines effectively for a current transmission grid to fulfill the model objectives. However, to obtain a solution, especially under uncertainty, is extremely difficult due to the nonlinear mixed-integer structure of the TEP problem. In this paper, first genetic algorithm (GA) approaches for TEP are reviewed in the literature and then a new hybrid GA with linear modeling is proposed. The proposed GA method has a flexible structure and …
Automatic Prostate Segmentation Using Multiobjective Active Appearance Model In Mr Images, Ahad Salimi, Mohammad Ali Pourmina, Mohamma-Shahram Moien
Automatic Prostate Segmentation Using Multiobjective Active Appearance Model In Mr Images, Ahad Salimi, Mohammad Ali Pourmina, Mohamma-Shahram Moien
Turkish Journal of Electrical Engineering and Computer Sciences
Prostate cancer is the second largest cause of mortality among men. Prostate segmentation, i.e. the precise determination of the prostate region in magnetic resonance imaging (MRI), is generally used for prostate volume measurement, which can be used as a potential prostate cancer indicator. This paper presents a new fully automatic statistical model called the multiobjective active appearance model (MOAAM) for prostate segmentation in MR images. First, in the training stage, the appearance model, including the shape and texture model, is developed by applying principal component analysis to the training images, already outlined by a physician. Then noise and roughness are …
Gacnn Sleeptunenet: A Genetic Algorithm Designing The Convolutional Neural Network Architecture For Optimal Classification Of Sleep Stages From A Single Eeg Channel, Shahnawaz Qureshi, Seppo Karilla, Sirirut Vanichayobon
Gacnn Sleeptunenet: A Genetic Algorithm Designing The Convolutional Neural Network Architecture For Optimal Classification Of Sleep Stages From A Single Eeg Channel, Shahnawaz Qureshi, Seppo Karilla, Sirirut Vanichayobon
Turkish Journal of Electrical Engineering and Computer Sciences
This study presents a method for designing--by a genetic algorithm, without manual intervention--the feature learning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neural network called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, from two available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn the architecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, …
Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony
Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, enormous progress has been made on power generation using photovoltaic (PV) system. Solar power is one of the most promising renewable energy sources that is providing its benefit specifically in rural areas. With the increasing need for solar energy, it becomes necessary to extract maximum power from the PV array. The output power of the solar cells varies directly with the ambient temperature and Irradiation. Therefore, the challenge is to track maximum power from the PV array when environmental factors change. This paper focuses on increasing the efficiency of a PV array by incorporating artificial intelligence techniques. …
Generation Rescheduling Using Multiobjective Bilevel Optimization, Kiran Babu Vakkapatla, Srinivasa Varma Pinni
Generation Rescheduling Using Multiobjective Bilevel Optimization, Kiran Babu Vakkapatla, Srinivasa Varma Pinni
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a new multiobjective optimization method that can be used for generation rescheduling in power systems. Generation rescheduling in restructured power systems is performed by the system operator for different operations like congestion management, day-ahead scheduling, and preventive maintenance. The nonlinear nature of the equations involved and the constraints on decision variables pose a challenge to find the global optimum. In order to find the global optimum using a genetic algorithm, a bilevel optimization method is proposed. In the proposed multiobjective optimization method, the objectives are classified as primary and secondary based on their relative importance. The best …
Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage
Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage
Electronic Theses and Dissertations
The use of neural prostheses to improve health of paraplegics has been a prime interest of neuroscientists over the last few decades. Scientists have performed experiments with spinal cord stimulation (SCS) to enable voluntary motor function of paralyzed patients. However, the experimentation on the human spinal cord is not a trivial task. Therefore, modeling and simulation techniques play a significant role in understanding the underlying concepts and mechanics of the spinal cord stimulation. In this work, simulation and modeling techniques related to spinal cord stimulation were investigated. The initial work was intended to visualize the electric field distribution patterns in …