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Trajectory planning and inverse kinematics solution of Kuka robot using COA along with pick and place application

INTELLIGENT SERVICE ROBOTICS 2024年第2期17卷 289-302页

作者： Kaur, Manpreet Yanumula, Venkata Karteek Sondhi, Swati Thapar Inst Engn & Technol Dept Elect & Instrumentat Engn Patiala 147004 Punjab India

In this work, coyote optimization algorithm (COA) is used for inverse kinematics optimization of a 7 degrees-of-freedom Kuka robot. The Denavit-Hartenberg (D-H) Convention approach is used to compute the forward kinematics of the robotic arm. The fitness functions based on sum of squares of distance and torque are employed to compute the optimized inverse kinematics solution using the COA. A comparative analysis has been conducted with other optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO) and Grey wolf optimization (GWO), artificial bee colony (ABC) optimization, and whale optimization algorithm (WOA) to evaluate the performance of the proposed approach. The experimental results show that the COA leads to least computation error of 3.59 x 10(-7) and computation time of 1.405 s as compared to GA, PSO, GWO, ABC, and WOA algorithms. Further, jerk being control input has a major impact on the efficiency of robotic arm. COA is employed to obtain the optimal joint parameters, such as joint velocity, joint acceleration, and joint jerk, respectively. This leads to a minimum jerk trajectory which contributes to the smooth movement of Kuka arm. The simulation of Kuka robotic arm for pick and place operations is performed in CoppeliaSim, which further justifies its usage for real-time applications.

关键词： Robotic manipulator Inverse kinematics Heuristic approach coyote optimization algorithm Minimum jerk trajectory

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Parameter identification and SOC estimation of lithium-ion batteries based on AGCOA-ASRCKF

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JOURNAL OF POWER ELECTRONICS 2023年第2期23卷 308-319页

作者： Chu, Yunkun Li, Junhong Gu, Juping Qiang, Yujian Nantong Univ Sch Elect Engn Nantong Peoples R China

The state of charge (SOC) is an important parameter in battery management systems (BMS), and its accuracy is very important. In this paper, a co-estimation with the adaptive global optimal guided coyote optimization algorithm and the adaptive square root cubature Kalman filter (AGCOA-ASRCKF) is used to perform the parameter identification and SOC estimation of a lithium-ion second-order RC model. The AGCOA effectively solves the problems where traditional heuristic algorithms tend to fall into local optimum and have a slow convergence speed. The AGCOA can accurately identify the parameters of the battery model. At the same time, when compared with the cubature Kalman filter, the ASRCKF introduces a square root filter and adds a residual sequence to adaptively update the covariance of the process noise and measurement noise, which improves the estimation accuracy of the SOC. The method proposed in this paper is verified by intermittent constant current test, dynamic stress test, and the federal urban driving schedule. Simulation results show that a high-precision battery model can be established by AGCOA-ASRCKF. In addition, the predicted value of the terminal voltage is basically consistent with the actual value. At the same time, the SOC estimation error can be controlled to within 1.5%, and the algorithm has good robustness and reliability in the presence of errors in the initial SOC.

关键词： Lithium-ion battery Second-order RC model State of charge coyote optimization algorithm Cubature Kalman filter

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Nonlinear Fast Modeling Method of Flux Linkage and Torque for a 128 Switched Reluctance Motors

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第5期39卷 5298-5309页

作者： Sun, Xiaodong Wang, Nuonuo Cao, Yunfei Guo, Dong Yao, Ming Sun, Yueping Xiong, Yefei Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Chongqing Univ Technol Sch Vehicle Engn Int Joint Lab Intelligent Mfg & Control Key Parts Minist Educ Chongqing 400054 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

This article presents a nonlinear modeling method for the flux linkage and torque of a switched reluctance motor. The method is based on universal weighted least squares support vector machine regression (WLSSVR), combined with entropy method (EM) and improved coyote optimization algorithm (COA) to optimize the kernel parameters. Among them, EM can well improve the error of the general WLSSVR in the selection of sample weights, and the improved COA can prevent the system from falling into local optimum and improve the iteration speed. This SRM modeling method, which combines WLSSVR based on entropy and improved COA, can improve the modeling speed on the premise of ensuring the modeling accuracy mode speed. Finally, the effectiveness of the algorithm is verified by experiments.

关键词： coyote optimization algorithm nonlinear modeling switched reluctance motor weighted least squares support vector machine regression

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A novel hybrid approach to flood susceptibility assessment based on machine learning and land use change. Case study: a river watershed in Vietnam

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HYDROLOGICAL SCIENCES JOURNAL 2022年第7期67卷 1065-1083页

作者： Nguyen, Huu Duy Bui, Quang-Thanh Nguyen, Quoc-Huy Nguyen, Tien Giang Pham, Le Tuan Nguyen, Xuan Linh Vu, Phuong Lan Nguyen, Thi Ha Thanh Nguyen, Anh Tuan Petrisor, Alexandru-Ionut Vietnam Natl Univ VNU Univ Sci Fac Geog Hanoi Vietnam Vietnam Natl Univ VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & Gis Hanoi Vietnam Vietnam Natl Univ VNU Univ Sci Fac Hydrol Meteorol & Oceanog Hanoi Vietnam Vietnam Natl Univ Int Sch VNU IS Hanoi Vietnam Ion Mincu Univ Architecture & Urbanism Doctoral Sch Urban Planning Bucharest Romania

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the relevance vector machine (RVM) and coyote optimization algorithm (COA) models, applied to Gianh River watershed, Quang Binh province, Central Vietnam. Standard statistical indices, e.g. area under the curve (AUC), were used to assess the model performance. Comparative analyses emphasize that the COA successfully improves the performance of the RVM model (AUC = 0.99) and is also better than the reference models such as support vector machine (AUC = 0.98), gradient boosting machine (AUC = 0.97), random forest (AUC = 0.99), extra trees regressor (AUC = 0.98), and AdaBoost (AUC = 0.96). The improved model, when used in conjunction with land use maps, is able to show that urbanization has increased in flood-susceptible areas. The results highlight that urbanization has increased in the low and very low flood susceptibility areas by 110% between 2005 and 2020, while in the high and very high areas it has increased by 30 to 40%, despite urban and demographic growth.

关键词： flood relevance vector machine coyote optimization algorithm Gianh River

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Heuristic optimization algorithm Based Watermarking on Content Authentication and Tampering Detection for English Text

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IEEE ACCESS 2023年 11卷 86104-86111页

作者： Al-Wesabi, Fahd N. Alrowais, Fadwa Mohamed, Heba G. Duhayyim, Mesfer Al Hilal, Anwer Mustafa Motwakel, Abdelwahed King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha 62529 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Engn Dept Elect Engn Riyadh 11671 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Al Kharj 16278 Saudi Arabia Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

Text information is a natural language dependent, which enhances reliability and security of text data exchanged through Internet networks and is becoming main concern for researchers. Content authentication, Tampering detection, and integrity verification of digital content are considered challenges in the information and communication exchange area through the Internet. Watermarking algorithms emerge as a potential method for achieving this with text. In simple words, text watermarking is embedding imperceptible text messages within the content that serve as unique signatures or identifiers. The text message has data regarding the content creator, copyright data, or other metadata utilized for authentication purposes. Therefore, this study presents a new coyote optimization algorithm with Watermarking-based Content Authentication and Tampering Detection (COAW-CATD) technique for English text. The presented COAW-CATD technique aims to secure the English text via content authentication and tampering recognition. To accomplish this, the presented COAW-CATD technique designs a zero-watermarking (ZWM) approach to produce watermarks depending on the textual content. The generated watermark can be extracted to assure the authentication of the text document. Moreover, the COA can be utilized to optimize the placement of the watermarks in the content to ensure that it is imperceptible and robust to tampering. The experimental outcomes of the COAW-CATD algorithm are tested utilizing a series of simulations and the comparative study reported its superior performance with improved tampering detection accuracy of 95.93%.

关键词： INDEX TERMS Watermarking algorithm content authentication tampering detection coyote optimization algorithm security

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A Novel Visual-Textual Sentiment Analysis Framework for Social Media Data

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COGNITIVE COMPUTATION 2021年第6期13卷 1433-1450页

作者： Jindal, Kanika Aron, Rajni Lovely Profess Univ Jalandhar Punjab India SVKMs Narsee Monjee Inst Management Studies NMIMS Mumbai Maharashtra India

Background Sentiment analysis (SA) has turned out to be a new pattern in social networking, avidly helping people to realize views expressed in user-generated content and conventional platforms of social media. For performing numerous social media analytics tasks, SA of online user-produced content is vital. The performance of the sentiment classifiers utilizing a single modality, i.e., visual or textual, is still not matured because of the wide variety of data platforms. Methods In this paper, we propose a new framework called VIsual-TExtual SA (VITESA) that carries out visual analysis and textual analysis for polarity classification. In the VITESA framework, Brownian Movement-based Meerkat Clan algorithm-centered DenseNet (BMMCA-DenseNet) is proposed that integrates textual and visual information for robust SA. In the visual phase, the images that are in the Flickr dataset are taken as input, and the operations: (1) preprocessing (2) feature extraction, and (3) feature selection utilizing Improved coyote optimization algorithm (ICOA) are executed. In the textual phase, the user comments as of the Twitter dataset are taken as input, and the operations: (1) preprocessing (2) word embedding using adaptive Embedding for Language Models (ELMo), (3) emoticon and non-emoticon feature extraction, and SentiWordNet polarity assignment is carried out. The final stages of both phases are given as input to the proposed BMMCA-DenseNet classifier and intended to categorize the data into positive and negative polarity. The performance of BMMCA-DenseNet is compared with certain existing algorithms, and various performance metrics are evaluated. Results The proposed BMMCA-DenseNet classifier performs the polarity classification of the visual-textual data into two classes: positive or negative. The classifier categorizes the polarity of visual-textual data comprising 97% of accuracy, 94.44% of precision, 94.41% of recall, 94.41% of F-measure, 91.75% of Matthew's Correlation Coeffici

关键词： Sentiment analysis Multimodality analysis coyote optimization algorithm Visual-textual sentiment analysis DenseNet Convolutional neural network Meerkat Clan algorithm

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Parameter identification and SOC estimation of lithium-ion battery based on AGCOA optimization 34

Parameter identification and SOC estimation of lithium-ion b...

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34th Chinese Control and Decision Conference (CCDC)

作者： Chu, Yunkun Li, Junhong Li, Lei Qiang, Yujian Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

ISBN: (纸本)9781665478960

Considering the polarization effect of lithium-ion battery, this paper establishes a second-order RC model, and proposes an adaptive global optimal guided coyote optimal identification algorithm (AGCOA). It effectively solves the problems of traditional heuristic algorithms that are easy to fall into local optima and slow convergence. The battery testing equipment NEWARE BTS-4008 is used to charge and discharge the lithium-ion battery. The parameters identified by the AGCOA are used to predict the terminal voltage, and the extended Kalman filter algorithm (EKF) is used to estimate the battery SOC. The results show that the predicted voltage obtained by the identification is basically the same as the actual voltage, and the SOC estimation error is small, which verifies the efficiency and accuracy of the algorithm.

关键词： Lithium-ion battery second-order RC model coyote optimization algorithm extended Kalman filter

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Performance Investigation for Tracking GMPP of Photovoltaic System Under Partial Shading Condition Using coyote algorithm 21

Performance Investigation for Tracking GMPP of Photovoltaic ...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Mostafa, Hazem H. Ibrahim, Amr M. Egyptian Chinese Univ Energy & Renewable Energy Dept Cairo Egypt Ain Shams Univ Elect Power & Machines Dept Cairo Egypt

ISBN: (纸本)9781728152899

this paper presents a methodology utilizing coyote optimization algorithm (COA) for Global Maximum Power Point Tracking (GMPPT) considering the impact of partial shading on a PV system. COA is utilized to clarify GMPP by comparing all the existing peaks on the PV curve. COA is also employed to control the boost converter. Different shading patterns are applied to a photovoltaic system. Simulations of this process are established utilizing MATLAB software. The suggested algorithm is compared with enhanced Grey Wolf optimization (E-GWO), Dragonfly algorithm (DA), Ant Lion optimization (ALO), and Particle Swarm optimization (PSO). According to the results, the suggested COA improves the tracking speed and accuracy of MPPT during partial shading conditions. Also, the suggested methodology is providing high efficiency for solar PV systems in any irradiation conditions.

关键词： Bio-Inspired algorithm coyote optimization algorithm Maximum power point tracking Photovoltaic Partial shading conditions

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Energy Aware Reliable Sleep Wakeup based Clustering Scheme in WSN

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IETE JOURNAL OF RESEARCH 2024年第8期70卷 7069-7081页

作者： Chithra, T. V. Karthikeyan, K. Hussain, D. Mansoor Muthukumaran, N. Arunachala Coll Engn Women Dept Comp Sci & Engn Nagercoil 629203 Tamil Nadu India Ramco Inst Technol Dept Elect & Elect Engn Rajapalayam Tamil Nadu India VIT Univ Sch Comp Sci & Engn SCOPE Chennai Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India

Wireless sensor networks (WSNs) are composed of spatially dispersed nodes monitored by sensors that communicate their collected data to a sink node. The primary method used by the WSN to spread the message throughout the whole network is flooding. Retransmission and rebroadcasting of the same message during flooding are important for reliability and energy efficiency. In this paper a novel Clustering and Sleep Wakeup based energy efficient Flooding (CSW-E2F) technique is proposed which improves the network lifetime and reliability. Initially, the proposed technique will form the nodes as clusters using the Gaussian mixture model (GMM). After clustering, the sleeping nodes (SN) and waking nodes (WN) were selected, and the awake nodes will take part in the random cluster head (CH) election process which is done by using the coyote optimization algorithm. After CH selection, the stability of the link has been checked using various parameters which will then choose the best route for message transmission. The suggested CSW-E2F approach was assessed in terms of network lifetime, energy consumption, average delivery delay, network coverage rate, average flooding delay, and throughput. When compared to existing schemes like Evolution Multipath Energy-Efficient Routing (EMEER), Binary Particle Swarm Optimisation (BPSO), and Energy-Balanced Power-Efficient Gathering in Sensor Information Systems (EB-PEGASIS), the proposed CSW-E2F improved network throughput by 17.5%, 19%, and 27%, respectively. The suggested solution exceeds previous approaches in terms of latency and energy cost by about 50% and 60%, respectively, thereby prolonging the system lifetime.

关键词： coyote optimization algorithm Flooding Gaussian mixture model Sleep wakeup

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A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

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JOURNAL OF ENERGY STORAGE 2020年 29卷

作者： Yuan, Zhi Wang, Weiqing Wang, Haiyun Yildizbasi, Abdullah Xinjiang Univ Minist Educ Engn Res Ctr Renewable Energy Power Generat & Gri Urumqi 830047 Xinjiang Peoples R China Ankara Yildirim Beyazit Univ AYBU Dept Ind Engn TR-06010 Ankara Turkey

In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote optimization algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly algorithm (FA), Whale optimization algorithm (WOA), and Particle Swarm optimization (PSO) to show the proposed method's prominence efficiency.

关键词： Battery energy storage system Lithium-ion battery Optimal allocation Optimal sizing coyote optimization algorithm Improved

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