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Design and optimization of Personalized Education System Based on Intelligent algorithms

Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm Decision Tree algorithm Personalized Education System particle swarm optimization algorithm

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Application of Two Modified Autonomous Development algorithms in Robot Obstacle Avoidance

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电脑学刊 2017年第3期28卷 235-250页

作者： Hong-Ge Ren Rui Yin Tao Shi Fu-Jin Li

In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on online sequential extreme learning machine (Q-OSELM algorithm) was proposed in this paper. And then, due to the random selection of weight and threshold parameters, it also proposes an extreme learning machine algorithm optimized by particle swarm (PSO-ELM algorithm) in this paper. Firstly, Q-OSELM algorithm obtains current environment and the status information of the robot through the characteristic of reinforcement learning, which combines dynamic network with supervised learning. After that, the online sequential extreme learning machine is used to approximate the function of the current status to get the rewards and punishments of the current status; Secondly, it is used to solve the problem of slow training speed by the characteristic of less parameter settings and better generalization performance. PSO-ELM algorithm is used to optimize the input weights and the hidden layer bias of the extreme learning machine which have been seen as the particle of particle swarm optimization algorithm to improve the network structure of the extreme learning machine. It could overcome inaccuracy of traditional extreme learning machine through particle swarm optimization algorithm. Finally, the performance of two learning algorithms is verified. The simulation experimental results show that the Q-OSELM learning algorithm improves the initiative of machine learning. And compared with the Q-OSELM algorithm, the PSO-ELM algorithm has better generalization ability and higher training precision. Simulation experiments are carried out to verify the stability and convergence of the two algorithms.

关键词： autonomous learning obstacle avoidance online sequential extreme learning machine particle swarm optimization algorithm reinforcement Q learning

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Mechanism of time-delay feedback control of suspension damping with an annular vibration-absorbing structure

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JOURNAL OF VIBROENGINEERING 2023年第8期25卷 1561-1582页

作者： Chen, Yajie Ma, Kehui Ren, Chuanbo Nan, Yang Zhou, Pengcheng Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

With the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system.

关键词： suspension structure vehicle vibration damping time-delay feedback control stability analysis particle swarm optimization algorithm

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The Mechanism of the Role of Big Data Knowledge Management in the Development of Enterprise Innovation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1364-1372页

作者： Yan, Guangyu Ma, Rui Chengdu Polytech Dept Sci Res Chengdu 610041 Peoples R China Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu 611137 Peoples R China

The effectiveness and efficiency of enterprise knowledge management depends on the effectiveness and efficiency of the enterprise's implementation of knowledge management. Big data technology can collect, analyse and apply the massive amount of data in an organisation to support the implementation of knowledge management. Therefore, exploring the role of big-data knowledge management in the development of enterprise innovation will help enterprises to better implement knowledge management. Based on this, the study aims to propose a model for predicting big data knowledge management and enterprise innovation development for high-tech enterprises in China. The study firstly used Principal Component Analysis (PCA) to decrease the dimensionality of the model, and then used the particle swarm algorithm to optimize BP neural network (PSO-BP). Network (PSO-BP) was used to evaluate enterprise knowledge management and enterprise innovation development. The results of the study show that the absolute values of the relative errors of the pre-processed model do not exceed the 5% threshold, and only the relative errors of some indicators are relatively large, such as X5 and X7, with values of 4.5% and -3.8%, indicating that the model has a good performance in predicting the innovation effect of enterprises.

关键词： Big data knowledge management BP neural network algorithm enterprise innovation development principal component analysis particle swarm optimization algorithm correlation analysis

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An evacuation model based on co-evolutionary multi-particle swarms optimization for pedestrian-vehicle mixed traffic flow

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2017年第12期28卷 1750142-1750142页

作者： Zong, Xinlu Wang, Chunzhi Chen, Hongwei Hubei Univ Technol Sch Comp Sci & Technol Wuhan 430068 Hubei Peoples R China

In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pedestrians and vehicles as well as the mutual effect between evacuation individuals and the environment which evacuees are situated in, will have an important impact on evacuation effects. This paper presents a pedestrian-vehicle mixed evacuation model to produce optimal evacuation plans considering both evacuation time and density degree. A co-evolutionary multi-particle swarms optimization approach is proposed to simulate the evacuation process of pedestrians and vehicles separately and the interaction between these two kinds of traffc modes. The proposed model and algorithm are effective for mixed evacuation problems. An illustrating example of a study region around a large stadium has been presented. The experimental results indicate the effective performances for evacuation problems which involve complex environments and various types of traffic modes.

关键词： Mixed evacuation pedestrian particle swarm optimization algorithm co-evolution pheromone

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Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第1期22卷 67-67页

作者： Li, Dujuan Chen, Caixia North Sichuan Med Coll Nanchong 631000 Peoples R China China West Normal Univ Nanchong 631000 Peoples R China

Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

关键词： Exercise fatigue Surface EMG signal Electrocardiogram signal Feature fusion particle swarm optimization algorithm

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Research on Load and Mutual Inductance Identification Method of WPT System Based on a LCC-S Type Compensation Network

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WORLD ELECTRIC VEHICLE JOURNAL 2021年第4期12卷 197页

作者： Xue, Ming Lu, Keyan Zhu, Lihua Tiangong Univ Tianjin Key Lab Adv Technol Elect Engn & Energy Tianjin 300387 Peoples R China

The identification of load and mutual inductance parameters of a wireless power transfer system can make the mathematical model of the system more accurate, which can effectively avoid system errors due to parameter uncertainties in the implementation of control, and provide theoretical support for system interoperability and high efficiency. This paper uses the two-port theorem and fundamental wave analysis to establish the identification model and obtain the relationship between inverter output current and load and between mutual inductance and load based on the equivalent circuit of a LCC-S magnetically coupled wireless power transfer system. To make the identification results more accurate, a particle swarm optimization algorithm with weights is introduced to transform the parameter identification problem of the system into an optimization problem, which can obtain the identification method of the system load and mutual inductance parameters. Both simulation and experimental results verify the feasibility and effectiveness of the method.

关键词： wireless power transfer (WPT) load identification mutual inductance identification LCC-S type compensation particle swarm optimization algorithm

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Mutual inductance identification of non-contact excitation system

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ADVANCES IN MECHANICAL ENGINEERING 2024年第8期16卷

作者： Li, Ke Ding, Jie Sun, Xiaodong Meng, Xuan Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Jiangsu Univ Automot Engn Res Inst Xuefu Rd 31 Zhenjiang 212013 Peoples R China

Due to the presence of brush and slip ring in the excitation method of electrically excited synchronous motors, this article proposes a new excitation method - non-contact excitation system. This method transfers electrical energy from the stator to the rotor through magnetic coupling, replacing slip ring and brush. However, the magnetic coupling coils at the primary and secondary ends of the system will deviate, which will affect motor operation quality. In order to effectively reduce the changes caused by mutual inductance, this article proposes an improved particle swarm optimization algorithm for mutual inductance identification. This improved algorithm can effectively reduce the shortcomings of low accuracy and easy to fall into local optima in particle swarm optimization. Simulation and experimental results show that the improved particle swarm optimization algorithm can improve search accuracy.

关键词： Non-contact excitation wireless power transfer mutual inductance identification particle swarm optimization algorithm

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On the Localization algorithm of Wireless Sensor Network and Its Application

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2017年第3期13卷 40-51页

作者： Jia, Honglei Zheng, Jiaxin Wang, Gang Chen, Yulong Huang, Dongyan Yuan, Hongfang Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun 130022 Peoples R China

This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and proposes the centroid particle swarm optimization localization algorithm based on RSSI by adding the RSSI and particle swarm optimization algorithm to the traditional centroid localization algorithm. Simulation experiment proves that the two algorithms have excellent effect.

关键词： DV-Hoplocalization algorithm WSN localization one-hop RSSI particle swarm optimization algorithm

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PSO-NMPC control strategy based path tracking control of mining LHD (scraper)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-28页

作者： Liu, Ya Peng, Ping-an Wang, Li-guan Wu, Jia-xi Lei, Ming-yu Zhang, Chao-wei Lei, Ru Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Minist Educ Xinjiang Inst Engn Key Lab Xinjiang Coal Resources Green Min Urumqi 830023 Peoples R China

The automation of underground articulated vehicles is a critical step in advancing digital and smart mining. Current nonlinear model predictive control (NMPC) controllers face challenges such as delays in turning on large curvature paths and correction lags during the control of underground the Load-Haul-Dump (LHD). To address these issues, this paper proposes a PSO-NMPC control strategy that integrates a particle swarm optimization algorithm (PSO) into the NMPC controller to enhance path tracking for LHDs. To verify the effectiveness of the proposed PSO-NMPC control strategy, the local path of the tunnels is selected as the simulation path, comparing it with the pure NMPC controller based on the path characteristics of the actual tunnel. The results demonstrate that the improved NMPC controller significantly enhances the trajectory tracking performance of the LHD, with maximum absolute lateral deviations for experimental paths 2, 3, and 5 improved by 89.7%, 72.2%, and 68.9%, respectively. Additionally, the improved NMPC controller exhibits superior performance in paths with large curvature compared to those with very small curvature and straight-line paths, effectively addressing the challenges of turn delay and backward lag in LHD operation, thus providing practical significance.

关键词： Mining LHD Nonlinear model predictive control particle swarm optimization algorithm PSO-NMPC control strategy Path tracking control

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