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- 摘要
- 关键词
- 实验方案
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Human-Computer Interaction using Finger Signing Recognition with Hand Palm Centroid PSO Search and Skin-Color Classification and Segmentation
摘要： This paper presents a novel image processing technique for recognizing finger signs language alphabet. A human-computer interaction system is built based on the recognition of sign language which constitutes an interface between the computer and hearing-impaired persons, or as an assistive technology in industrial robotics. The sign language recognition is articulated on the extraction of the contours of the sign language alphabets, therefore, converting into one dimensional signal processing, which improves the recognition efficiency and significantly reduces the processing time. The pre-processing of images is performed by a novel skin-color region segmentation defined inside the standard RGB (sRGB) color space, then a morphological filtering is used for non-skin residuals removal. Afterwards, a circular correlation achieves the identification of the sign language after extracting the sign closed contour vector and performing matching between extracted vector and target alphabets vectors. The closed contour vector is generated around the hand palm centroid with position optimized by a particle swarm optimization algorithm search. Finally, a multi-objective function is used for computing the recognition score. The results presented in this paper for skin color segmentation, centroid search and pattern recognition show high effectiveness of the novel artificial vision engine.
关键词： Skin-color,Pattern recognition,Sign language,Segmentation,Particle Swarm Optimization,Human-Machine Interaction
更新于2025-09-19 17:15:36
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[IEEE 2018 18th Mediterranean Microwave Symposium (MMS) - Istanbul, Turkey (2018.10.31-2018.11.2)] 2018 18th Mediterranean Microwave Symposium (MMS) - PSO Based Approach to the Synthesis of a Cylindrical-Rectangular Ring Microstrip Conformal Antenna Using SVR Models with RBF and Wavelet Kernels
摘要： In this work, particle swarm optimization (PSO) based approach to the synthesis of a cylindrical-rectangular ring microstrip conformal antenna using support vector regression (SVR) models is presented. Resonant frequency of the antenna is obtained by PSO of trained SVR models. Radial basis function (RBF) and wavelet kernel functions are used in SVR models. Simulation examples are given and the results are compared.
关键词： support vector regression,rectangular ring microstrip antenna,particle swarm optimization,wavelet kernel,conformal antennas
更新于2025-09-19 17:15:36
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The Development of Supervised Motion Learning and Vision System for Humanoid Robot
摘要： With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.
关键词： vision system,particle swarm optimization,humanoid robot,convolutional neural network
更新于2025-09-19 17:15:36
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Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
摘要： This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO (AWPSOc f ) and sigmoid function PSO (SFPSOc f ), are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed AW/SFPSOc f methods give various con?gurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of SFPSOc f in terms of accuracy is selecting an optimal con?guration at ?tness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of AW/SFPSOc f methods is veri?ed by using the iterative method.
关键词： levelized cost of energy (LCE),multi-objective optimization,particle swarm optimization,standalone PV system,loss of load probability (LLP),life cycle cost (LCC)
更新于2025-09-19 17:13:59
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Goethite Quantum Dots as Multifunctional Additives for Highly Efficient and Stable Perovskite Solar Cells
摘要： The “curse of dimensionality” is one of the largest problems that influences the quality of the optimization process in most data mining, pattern recognition, and machine learning tasks. Using high-dimensional datasets to train a classification model may reduce the generalization performance of the learned model. In addition, high dimensionality of the dataset results in high computational and memory costs. Feature selection is an important data preprocessing approach in many practical application domains that are relevant to expert and intelligent systems. Feature selection aims at selecting a subset of informative and relevant features from an original feature dataset. Therefore, using a feature selection approach to process the original data prior to the learning process is essential for enhancing the performance on the classification task. In this paper, hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) is proposed for selecting the optimal feature subset for classification via a wrapper-based approach. In HPSO-SSM, we make three improvements: First, a logistic map sequence is used to enhance the diversity in the search process. Second, two new parameters are introduced into the original position update formula, which can effectively improve the position quality of the next generation. Finally, a spiral-shaped mechanism is adopted as a local search operator around the known optimal solution region. For a complete evaluation, the proposed HPSO-SSM method is compared with six state-of-the-art meta-heuristic optimization algorithms, ten well-known wrapper-based feature selection techniques, and six classic filter-based feature selection methods. Various assessment indicators are used to properly evaluate and compare the performances of these approaches on twenty classic benchmark classification datasets from the UCI machine learning repository. According to the experimental results and statistical tests, the developed methods effectively and efficiently improve the classification accuracy compared with other wrapper-based approaches and filter-based approaches. The results demonstrate the high performance of the HPSO-SSM method in searching the feasible feature space and selecting the most informative attributes for solving classification problems. Therefore, the HPSO-SSM method has broad application prospects as a new feature selection approach.
关键词： Feature selection,Optimization,Particle swarm optimization,Classification
更新于2025-09-19 17:13:59
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Grasshopper optimization algorithm utilized Xilinx controller for maximum power generation in photovoltaic system
摘要： A novel Grasshopper Optimization (GOA) based Xilinx System Generator (XSG) controller is proposed for the purpose of evaluating the Maximum Power Point Tracking in the grid integrated Photovoltaic (PV) power generation system. The proposed controller functions with the assistance of the GOA algorithm and the XSG procedures. The innovation of the controller is intended to collect the maximum power produced from the PV array in accordance with the solar irradiance and temperature of the array. The proposed GOA algorithm based XSG controller is achieved by maximum power from the PV array, it is necessary to adjust the switching signal for the voltage source inverter (VSI). The proposed PV structure is elegantly designed in the mighty platform of the MATLAB/Simulink and the switching schemes are produced in accordance with the XSG controller. At last, the output response of the presented GOA based XSG controller is analyzed and compared with conventional techniques such as the Particle Swarm Optimization (PSO) and the Artificial Bee Colony (ABC) algorithms.
关键词： Voltage source inverter,Maximum power point tracking,Artificial bee colony,Particle swarm optimization,Photovoltaic array,Grasshopper optimization algorithm,Xilinx system generator controller
更新于2025-09-19 17:13:59
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Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
摘要： The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
关键词： solar irradiation,adaptive neuro-fuzzy inference systems,PVs power output forecasting,particle swarm optimization-artificial neural networks
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Imaging Rydberg States of Atoms and Molecules with a Weak DC Field
摘要： This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO.
关键词： particle swarm optimization (PSO),Steiner tree problem (STP),Communication networks,multicast routing problem (MRP)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII) - Seoul, Korea (South) (2019.7.12-2019.7.15)] 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII) - Improved Hybrid Simulated Annealing and Particle Swarm Optimization for Maximum Power Point Tracking
摘要： This paper proposes an Improved Hybrid Simulated Annealing and Particle Swarm Optimization (ISA-PSO) which is applied in the maximum power point tracking (MPPT) of photovoltaic generation system. ISA-PSO combined with Simulated Annealing (SA) and Particle Swarm Optimization (PSO), using SA's escaping mechanism to improve the shortcomings of PSO slow convergence and easy to fall into the local optimal solution. The overall search ability is increase and the tracking time is reduced. This paper uses MATLAB to simulate MPPT. Through the simulation comparison with PSO, SA, SA-PSO, it can be verified that ISA-PSO scheme is better than other algorithms. In the temperature changing simulation, the efficiency is 97.03% and the tracking speed is reduced to 2.31 second. In the illumination changing simulation, the efficiency is 97.12% and the tracking speed is reduced to 2.27 second.
关键词： Photovoltaic Generation System,Maximum Power Point Tracking,Improved Hybrid Simulated Annealing and Particle Swarm Optimization
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Chalcogenide Glass Fiber Components for the Fabrication of Mid-Infrared Optical Sources
摘要： The positioning of electromagnetic (EM) sources on the complex plane, though a mathematical construct, is often applied in solving EM problems with directive confined (collimated) propagation characteristics. Equivalent dipole modeling, which finds its application in characterizing various current sources can be computationally expensive for large structures. Here, the complex localization of equivalent source points combined with the particle swarm optimization is used to improve the performance of the equivalent dipole modeling.
关键词： Complex source point,edge dipoles,diffraction,particle swarm optimization,equivalent dipole model,feature selective validation
更新于2025-09-19 17:13:59