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7th Asia Conference on Energy and Electrical Engineering (ACEEE)

作者： Zheng, Quan Yao, Yongqi Zhu, Jibin Zhang, Lixin Wang, Nan Yu, Miaoyuan Pinggao Grp Co Ltd Intelligent Energy Technol Res Inst Pingdingshan Peoples R China Pinggao Grp Co Ltd Mkt Serv Ctr Pingdingshan Peoples R China

ISBN: (纸本)9798350350258;9798350350241

Wind-photovoltaic-hydro hybrid power generation is an effective means to increase the power grid's ability to consume wind and solar energy, and the rational allocation of various power source capacities is the prerequisite for its effective application. The optimization model for the capacity allocation of wind-photovoltaic-hydro hybrid power generation systems aims to minimize the annual equivalent investment cost of the system as the objective function, with constraints such as capacity range, loss of power supply probability (LPSP), and power curtailment rate. To address the limitation of traditional particle swarm optimization (pso) algorithms in tending to get stuck in local optima during model solving, an innovative pso strategy is designed, which significantly improves the global search ability of the particles. Finally, the optimal ratio of wind and photovoltaic power generation capacities under a typical case system is studied, which verifies the effectiveness of the proposed model and the new algorithm, The impact of different LPSP on the calculation results is analyzed.

关键词： Wind-Photovoltaic-Hydro Hybrid Power Generation System Annual Equivalent Investment Cost pso algorithm LPSP

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State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-pso-TCN model

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ENERGY 2024年 289卷

作者： Li, Feng Zuo, Wei Zhou, Kun Li, Qingqing Huang, Yuhan Zhang, Guangde Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Peoples R China Univ Technol Sydney Ctr Green Technol Sch Civil & Environm Engn Sydney NSW 2007 Australia Univ Oxford Dept Engn Sci Oxford OX1 3PJ England Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430065 Peoples R China Wuhan Univ Sci & Technol Natl Prov Joint Engn Res Ctr High Temp Mat & Linin Wuhan 430081 Peoples R China

Accurate state-of-charge (SOC) estimation of lithium-ion battery is directly related to the reliability, performance, and safety of the battery. In this work, the second order resistor-capacitance (RC) circuit is equivalent to the battery model and the particle swarm optimization (pso) algorithm is employed for achieving accurate identification of the parameters of circuit model under dynamic stress test (DST) conditions. Furthermore, the values of open circuit voltage (OCV) obtained from the identification results are input into the temporal convolutional network instead of the terminal voltages, and then the SOC of the Li-ion battery is estimated by directly learning the mapping relationship of OCV-SOC curves, which further improves the estimation accuracy and robustness of the proposed method. Finally, the SOC estimation is validated with the public dataset of LiFePO4 batteries under all driving conditions at different temperature and compared with the individual TCN method. Results show that the SOC estimation of the second order resistor-capacitance circuit-pso-TCN model is optimal with a root mean square error (RMSE) and maximum error (MAXE) less than 1.8% and 7.65%, respectively.

关键词： Lithium-ion battery State of charge estimation Parameter identification pso algorithm OCV-SOC curve TCN

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Applying IoT for Operations Management and Production Planning in Industrial Sewing 3rd

Applying IoT for Operations Management and Production Planni...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Quoc, Huu Dang Thuy, Tram Tran Thi Sinh, Quan Nguyen Danh, Hao Bui Lan, Anh Vu Thi Department of Economic Information System and Electronic Commerce Thuong Mai University 79 Ho Tung Mau Cau Giay Ha Noi Viet Nam Thuong Mai University 79 Ho Tung Mau Cau Giay Ha Noi Viet Nam

ISBN: (纸本)9783031809422

Implementing IoT systems in industrial production enables the collection of real-time data from production lines and factories. This data is synthesized, analyzed, and used to generate reports that assist manufacturers. Business managers can quickly and easily grasp the situation, facilitating timely and effective management decisions. Specifically, in industrial sewing, IoT applications collect production data from industrial sewing machines and transmit it to cloud-based systems. This process enhances the synthesis and analysis of production data, thereby improving business management capabilities. This article explores the implementation of IoT applications in industrial garment enterprises to collect production data and proposes a data structure for integrating this information with the enterprise's MIS system. Additionally, it investigates the application of the Real-RCPSP problem to help businesses automate production planning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： IoT in sewing production production planning pso algorithm Real-RCPSP problem

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Reconfiguration of the Radial Distribution for Multiple DGs by Using an Improved pso 4th

Reconfiguration of the Radial Distribution for Multiple DGs ...

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4th International Conference on Networks, Intelligent Systems and Security (NISS)

作者： M'dioud, Meriem Bannari, Rachid Elkafazi, Ismail Ibn Tofail Univ Lab Sci Engn ENSA Kenitra Morocco Moroccan Sch Engn Sci Lab SMARTILAB EMSI Rabat Morocco

ISBN: (纸本)9789811636370;9789811636363

pso is one of the famous algorithms that help to find the global solution;in this study, our main objective is to improve the result found by the pso algorithm to find the optimal reconfiguration by adjusting the inertia weight parameter. In this paper, I select the chaotic inertia weight parameter and the hybrid strategy using the combination between the chaotic inertia weight and the success rate, these kinds of parameters are chosen due to their accuracy, and they give the best solution compared with other types of parameter. To test the performance of this study, I used the IEEE 33 bus in the case of the presence of the DGs, and a comparative study is done to check the reliability and the quality of these two suggested strategies. In the end, it is noticed that the reconfiguration by using the chaotic inertia weight gives a better result than the hybrid strategy and the other studies: reduce losses, improve the voltage profile at each node, and give the solution at a significant time.

关键词： Chaotic inertia weight Distributed generation pso algorithm Radial distribution network Reconfiguration Success rate

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A new development of ANFIS-GMDH optimized by pso to predict pile bearing capacity based on experimental datasets

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ENGINEERING WITH COMPUTERS 2021年第1期37卷 685-700页

作者： Harandizadeh, Hooman Jahed Armaghani, Danial Khari, Mahdy Shahid Bahonar Univ Kerman Fac Engn Dept Civil Engn Pajoohesh SqPOB 76169133 Kerman Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Islamic Azad Univ East Tehran Branch Dept Civil Engn Tehran Iran

Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (pso) algorithm called as ANFIS-GMDH-pso model;the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN-GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS-GMDH model achieved relatively higher performance compared to ANN and FPNN-GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

关键词： Ultimate pile bearing capacity Deep foundation ANFIS– GMDH– pso model pso algorithm FPNN– GMDH model GMDH network

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Trajectory Planning of Industrial Robot Based on Improved Radial Basis Function Neural Network 3

Trajectory Planning of Industrial Robot Based on Improved Ra...

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3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Li, Xiong Du, Yuyuan Qu, Dongxu Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350352634;9798350352627

With the more and more extensive use of robot technology in industrial production, the trajectory planning requirements of industrial robots are constantly increasing. In order to solve the problem that the trajectory is difficult to fit due to the nonlinearity in the continuous trajectory planning of robot, this paper presents a trajectory planning method of industrial robots which combines RBF (radial basis function) neural network and pso (particle swarm optimization) algorithm. When RBF neural network has fewer hidden layer nodes, pso algorithm is used to optimize the connection weight and threshold of RBF neural network. Through simulation analysis, the RBF neural network before and after improvement is compared. The results show that compared with the trajectory planning algorithm before improvement, the pso-RBF neural network has smaller error and stronger adaptability, which meets the expected requirements of industrial robot trajectory planning.

关键词： industrial robot trajectory planning RBF neural network pso algorithm

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BECT Spike Detection algorithm Based on Optimal Template Matching and Morphological Feature Selection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第4期69卷 2366-2370页

作者： Wu, Duanpo Shi, Haichao Jiang, Lurong Dong, Fang Liu, Junbiao Cao, Jiuwen Jiang, Tiejia Wu, Xunyi Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Zhejiang Univ City Coll Coll Informat & Elect Engn Hangzhou 310015 Peoples R China Zhejiang Univ City Coll Artist Design & Creat Sch Hangzhou 310015 Peoples R China Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejian Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Sch Med Childrens Hosp Hangzhou 310052 Peoples R China Fudan Univ Huashan Hosp Dept Neurol Shanghai 200040 Peoples R China

Over 15% of children with epilepsy belong to benign childhood epilepsy with centro-temporal spikes (BECT), facing with educational difficulties. The accurate recognition of spikes in electroencephalogram (EEG) signals collected from BECT patients can help the doctor to effectively make diagnosis and give therapeutic schedule. Traditionally, template matching method can extract spike-like waves from EEG signals and is adopted by many researches. However, the patterns of the spikes appeared in different patients or different time in one patient varies greatly. The brief proposes a spike detection algorithm based on optimal template matching and morphological feature selection, which includes universal template matching, spike clustering, universal template optimization based on particle swarm optimization (pso) algorithm and false positive spike (FPS) elimination based on spike morphological feature. Based on the testing EEG data set adopted in this brief, the sensitivity (Sen), specificity (Spe) and accuracy (AC) of the proposed algorithm reaches 98.2%, 95.1% and 96.5%, respectively.

关键词： Electroencephalography Feature extraction Clustering algorithms Signal processing algorithms Detection algorithms Pediatrics Morphology BECT spikes pso algorithm FPS elimination spike morphological feature

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Research on multi-parameter inversion of the phenomenon of gob breathing induced by atmospheric pressure fluctuation

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ENERGY EXPLORATION & EXPLOITATION 2022年第5期40卷 1482-1493页

作者： Wu, Bing He, Binbin Zhao, Chenguang Lei, Baiwei China Univ Min & Technol Beijing Sch Emergency Management & Safety Engn Beijing 100083 Peoples R China State Key Lab Coal Mine Safety Technol Shenyang 110016 Peoples R China

It is significant to predict the gas emission in the mine gob accurately for gas drainage to prevent accidents and ensure safe production. The change of atmospheric pressure makes the gas emission in the mine gob presents a "breathing phenomenon", which causes the gas concentration in the return airway of the working face fluctuates even if there are no other disturbances. The gas emission of the return airway in mine working face is affected by many factors, so it is difficult to analyze the influences of atmospheric pressure fluctuations on the gas emission of mine gob. In order to reduce interference factors, by continuously monitoring the gas concentration in the return airway of a does not work mine, in the meantime, simultaneously monitoring and analyzing the atmospheric pressure on the ground, this paper studies the influence rules of atmospheric pressure on gas emission in goaf. And a new multi-parameter mathematical model for gas emission in gob is developed, the Particle Swarm Optimization algorithm (pso) is used to invert the optimal parameters, and the model is verified by random monitoring data. The results show that the model can accurately indicate the influence of atmospheric pressure on gas emission in the gob. The faster the ground atmospheric pressure decreases, the higher the gas emission rate of the gob is, and the faster the ground atmospheric pressure increases, the lower the gas emission rate is. In addition, the mathematical model can also invert the effective permeability and effective porosity of the gob, which provides a new idea for the prediction of gas emission from goaf and the evolution characteristics of permeability in goaf.

关键词： Atmospheric pressure breathing phenomenon gas emission from goaf pso algorithm parameter inversion

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AquaCrop Plug-in-pso: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation

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AGRICULTURAL WATER MANAGEMENT 2024年 306卷

作者： Sharafkhane, Mahdi Gholami Ziaei, Ali Naghi Naghedifar, Seyed Mohammadreza Akbari, Amir Verdi, Amir Ferdowsi Univ Mashhad FUM Coll Agr Dept Water Sci & Engn Mashhad *** Iran Univ Calif Riverside Dept Environm Sci Riverside CA 92521 USA

Maize is among the most important crops in Iran. Enhancing maize irrigation management is critical for alleviating the pressure on limited freshwater resources in Khorasan Razavi province. The main objective of this study was to develop a novel irrigation scheduling optimization framework (AquaCrop plug-in-pso) for maize, based on in-season weather forecasts integrated with the AquaCrop plug-in model and the particle swarm optimization (pso) algorithm. During the growing season, weather forecasts combined with AquaCrop plug-inpso algorithm create optimal irrigation plans for different crop growth stages, maximizing crop water productivity (WPC). A two-year (2021-2022) maize field irrigation experiment was conducted in Mashhad, Iran, to collect all necessary data for calibration (2021 data) and validation (2022 data) of the AquaCrop model. Three irrigation cases were then simulated (i.e., full irrigation and deficit irrigation: 70 % and 90 % of full irrigation) to evaluate the performance of the AquaCrop plug-in-pso approach against the typical irrigation management of local farmers, as well as ET-based and soil moisture-based irrigation scheduling methods. Additionally, AquaCrop plug-in-pso was used to evaluate the algorithm's performance with historical weather data. The simulation results showed that the AquaCrop Plug-in-pso, when used with weather forecast data (AquaCrop plug-in-pso dynamic approach), outperformed all other irrigation scheduling methods for both deficit and full irrigation cases, achieving the highest WPC, ranging from 2.21 to 3.12 kg m(-3). Our simulation results demonstrate that the AquaCrop plug-in-pso dynamic approach can be effectively used for efficient autonomous full and maize deficit irrigation scheduling in arid and semiarid regions.

关键词： AquaCrop Deficit irrigation Optimization pso algorithm Weather forecasts

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The adaptive consensus reaching process with the ER-pso interaction mechanism and its application in public charging stations evaluation

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Tao, Xiwen Jiang, Wenqi Jin, Weijian Wang, Jiali Nanjing Inst Technol Sch Econ & Management Nanjing 211167 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G2E1 Canada

Background: Public charging stations (PCSs) have become integral public infrastructure in China, responding to the considerable and rapidly growing demand. Evaluating the service quality of PCSs by a diverse user base has become essential for the effective management of PCSs. Objectives: In the group evaluation of PCSs' service quality, which encompasses broad public interests, consensus reaching process (CRP) is commonly utilized to manage group conflicts and achieve evaluation consensus. The intricate interactions among decision makers (DMs) significantly influence the evolution and resilience of collective viewpoints. Against this backdrop, this research aims to develop an adaptive CRP to enhance the discriminatory power of group consensus and improve consensus robustness in the presence of non-cooperative behavior. Methodology: With the above objectives, this study proposes an adaptive CRP supported by a novel interaction mechanism, consisting of two stages: (1) In the interaction network generation stage, based on the Erd & odblac;s-R & eacute;nyi (ER) random network model, innovative interaction signals are generated to guide the generation of an ER-based interaction network, enhancing the dissemination of dominant viewpoints, and constraining the influence of minority opinions. (2) In the decision opinion interaction stage, based on the Particle Swarm Optimization (pso) algorithm, utilizing novel dual individual fitness functions, the pso algorithm is employed to formulate the expected updated belief, driving the internal evolution and convergence of opinions. The proposed adaptive CRP is implemented in the group evaluation of PCSs' service quality in Nanjing. Findings: Comparative analysis reveals that the proposed CRP offers significant advantages in consensus efficiency, the discriminatory power of group consensus, and consensus robustness in the presence of non-cooperative behavior compared to existing CRPs. Novelty: Differing from previous CRP research p

关键词： Consensus decision ER network pso algorithm Non-cooperator management Discrimination degree

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