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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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Redundancy management strategy for electro-hydraulic actuators based on intelligent algorithms

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ADVANCES IN MECHANICAL ENGINEERING 2020年第6期12卷

作者： Li, Wending Shi, Guanglin Shanghai Jiao Tong Univ Sch Mech Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai Peoples R China Shanghai Servosyst Engn Res Ctr Shanghai Peoples R China

This paper explores three methods for electro-hydraulic actuator redundancy management strategies based on fuzzy aggregation, Mamdani's fuzzy logic rules, and fuzzy neural network theory. These intelligent management schemes and implementation strategies are studied for a designed hardware-independent redundancy loop. Fault identification and isolation and system reconstruction are performed by combining fuzzy clustering with Mamdani's fuzzy control, a fuzzy neural network, and redundancy management. Simulation analyses of the three algorithms are performed and loop simulation models are established based on the analysis algorithm. The methods proposed in this paper can solve the problem of misjudgment encountered in traditional methods and can also avoid uncertain states in the system.

关键词： Redundancy management electro-hydraulic actuator fuzzy neural network intelligent algorithm Mamdani fuzzy logic fault identification

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Parallel Implementation of Swarm intelligent algorithms in a Spark-Based Cloud Computing Environment

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2021年第16期30卷 2150291-2150291页

作者： Zhu, Jun Wang, Yushen Jiangsu Vocat Coll Finance & Econ Dept Mech & Elect Engn 8 East Meicheng St Huaian 223003 Jiangsu Peoples R China China Mobile Grp Jiangsu Co Ltd 59 Huju Rd Nanjing 210029 Jiangsu Peoples R China

Swarm intelligent algorithms can effectively tackle optimization problems that are difficult to solve by using traditional optimization algorithms. However, with the huge increase in the time and space cost for solving optimization problems, the use of swarm intelligent algorithms suffer from the limitation of overly long computation time. Based on Spark, which is the most popular open-source distributed computing framework, this paper studies specifically using swarm intelligent algorithms to solve combinatorial optimization problems. Based on the characteristics of typical swarm intelligent algorithms, we develop Spark-based parallel implementation of these algorithms to accelerate the population updating and parameter tuning procedures involved in swarm intelligence. Specifically, we first initialize the swarm and generate the initial solution, then perform the distributed iterative evolution procedure, and finally obtain the optimal solution. In addition, in order to improve solution quality, we rely on the Spark platform to perform distributed parameter tuning. The tuning strategy first generates different parameter combinations according to a given parameter list, then execute swarm intelligent algorithms with different parameter combinations in a distributed and parallel manner, and finally determine the optimal parameter combination by comparing the solutions of all algorithms. Experimental results on benchmark datasets show that the distributed algorithms can significantly enhance the computational efficiency without affecting the solution quality.

关键词： Spark parallel computing metaheuristic intelligent algorithm permutation flowshop optimization problem

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Multi-objective optimized driving strategy of dual-motor EVs using NSGA-II as a case study and comparison of various intelligent algorithms

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APPLIED SOFT COMPUTING 2021年 111卷 107684-107684页

作者： Lin, Xinyou Lin, Zhili Wei, Shenshen Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350116 Peoples R China

The driving strategy of the driver is one of the critical factors which affect the energy consumption of the vehicle. However, it is often overlooked in the researches of energy management. Moreover, there are few studies devoted to formulating specific driving strategies which consider the driving preference of the driver. This study proposes a multi-objective optimized driving strategy for dual-motor electric vehicles based on the intelligent algorithms, which mainly studies the flat road sections, the long uphill road sections, and the vehicle acceleration sections. The acceleration duration, energy consumption, and driving comfort are defined as the optimized objectives. For the economy performance, the optimized driving strategy designs the travel speed trajectory according to the given road information to guide and adjust the driving behavior, so that the vehicle can be driven economically. In the process of studying vehicle multi-objective acceleration strategy, NSGA-II, MOPSO, PESA-II and SPEA2 were adopted to solve the multi-objective optimization problem with three conflicting objectives. And the performance comparison and statistical analysis of the four algorithms were carried out. Finally, a spliced driving cycle was constructed to validate the effectiveness of the driving strategy. The results indicated that the proposed driving strategy has an excellent performance towards driving assistance. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Energy consumption Driving strategy Dual-motor electric vehicle intelligent algorithm Multi-objective optimization

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Performance evaluation for intelligent optimization algorithms in self-potential data inversion

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Journal of Central South University 2016年第10期23卷 2659-2668页

作者：崔益安朱肖雄陈志学刘嘉文柳建新 School of Geosciences and Info-Physics Central South University Changsha 410083 China Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Detection (Central South University) Changsha 410083 China Key Laboratory of Metallogenic Prediction of Nonferrous Metals Ministry of Education (Central South University) Changsha 410083 China

The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.

关键词： self-potential inversion intelligent algorithm

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The application of Coarse-Grained Parallel Genetic algorithm with Hadoop in University intelligent Course-Timetabling System

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2015年第8期10卷 11-15页

作者： Wu, Liping Shandong Agr & Engn Univ Sch Informat Sci & Engn Jinan 250100 Shandong Peoples R China

Course-timetabling problem is a NP-C problem at Universities. Many traditional methods for solving this problem show low-efficiencies in several aspects, such as a high conflict rate of teacher resources or classroom resources, low degree of satisfaction among students or teachers and so on. Thus these methods do not meet the requirements of modern university educational administration management. Parallel genetic algorithm (PGA) integrating the advantages of the traditional genetic algorithm (GA) and the computing power of parallel computing can improve effectively the quality and speed in solving problems. This paper, which is based on the cloud computing platform with Hadoop, presents an improved method fusing Coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model, to solve the university course-timetabling problem. The simulation experiment results show that, compared with the traditional genetic algorithm, CGPGA not only improves the success rate but also reduces the conflict rate in solving the university course-timetabling problem. At the same time, this paper makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm. And then the corresponding computational results are reported.

关键词： intelligent algorithm Coarse-grained parallel genetic algorithm (CGPGA) intelligent course-time-tabling system Map/Reduce

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Autonomous Task Planning of intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

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DRONES 2025年第4期9卷 272-272页

作者： Jiang, Qiang Yan, Yongzhao Dai, Yinxing Yang, Zequan Cao, Huazhen Wang, Bo Ma, Xiaoping Chinese Acad Sci Inst Engn Thermophys Natl Key Lab Sci & Technol Adv Light Duty Gas Turb Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cent South Univ Sch Automat Changsha 410075 Peoples R China Qingdao Inst Aeronaut Technol Qingdao 266400 Peoples R China

intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence.

关键词： swarm autonomous decision-making simulation environment intelligent algorithm

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NNDI decentralized evolved intelligent stabilization of large-scale systems

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SMART STRUCTURES AND SYSTEMS 2022年第1期30卷 1-15页

作者： Chen, Z. Y. Wang, Ruei-Yuan Jiang, Rong Chen, Timothy Guangdong Univ Petrochem Technol Sch Sci Maoming 525000 Peoples R China CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

关键词： intelligent algorithm large-scale system neural fuzzy control numerical modelling

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Research on intelligent clustering of male upper body

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TEXTILE RESEARCH JOURNAL 2022年第13-14期92卷 2174-2193页

作者： Cheng, Pengpeng Zeng, Xianyi Bruniaux, Pascal Wang, Jianping Chen, Daoling Gemtex Ensait Cent Lille 2 Allee Louise & Victor Champier BP 30329 F-59000 Roubaix France Donghua Univ Coll Fash & Design Shanghai Peoples R China Minjiang Univ Clothing & Design Fac Fuzhou Peoples R China

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18-25 and 26-35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18-25 and 26-35 years.

关键词： young men intelligent algorithm cuckoo search-density peak algorithm clustering

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An intelligent resilience evaluation model for the development of urban underground space with safety concern of surrounding existing built environment

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2024年 149卷

作者： Zhou, Cong Su, Yanqi He, Lei Peng, Luyuan Zhang, Yi Wu, Gang Soh, Chee Kiong Southeast Univ Sch Civil Engn Nanjing 211189 Jiangsu Peoples R China Southeast Univ State Key Lab Safety Durabil & Hlth Operat Long Sp Nanjing 211189 Jiangsu Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong 999077 Peoples R China XCMG KAIGONG Heavy Ind Nanjing CO Ltd Nanjing 211161 Jiangsu Peoples R China

The rationality and safety of Urban Underground Space (UUS) construction are critical aspects of urban development. Given the lack of safety concern of the surrounding environment in existing layout planning research for newly constructed UUS, this study proposed a novel resilience evaluation model for UUS safe development through the cross -application of geotechnical analysis, urban planning theory, and artificial intelligence (AI). By re -using geological information, the proposed model mapping algorithm enables the application of geological models to refined numerical calculations which can accurately locate the range of excavation disturbances of new UUS. Subsequently, the proposed AI algorithm is trained to learn from the existing UUS excavation cases, establishing a mathematical mapping relationship between the main construction parameters of UUS and the safety indexes of surrounding affected buildings. The mapping relationship enables the assessment of the development resilience of UUS in the planned area through the safety evaluation of surrounding affected buildings. Finally, the layout of an underground station for Nanjing Metro Line 13 is used, as an example, to apply the proposed intelligent model. This study contributes to the tools and methods for UUS layout evaluation from a safety perspective, and seeks the breakthrough for the cross -application of geotechnical science and urban planning theory.

关键词： Urban underground space (UUS) 3D geological modelling Excavation disturbance intelligent algorithm Development resilience

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