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Using proposed optimization algorithm for solving inverse kinematics of human upper limb applying in rehabilitation robotic

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 679-705页

作者： Nguyen, Trung Bui, Tam Pham, Ha Hanoi Univ Sci & Technol HUST Sch Mech Engn Hanoi Vietnam Shibaura Inst Technol Dept Machinery & Control Syst Tokyo Japan Hanoi Univ Ind Fac Informat Technol Hanoi Vietnam

The requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field, especially for redundant driven systems, often requires the application of a lot of techniques. The redundancy in Degree of Freedom (DoF), the nonlinearity of the system leads to solve inverse kinematics problem more challenge. In this study, we proposed to apply the self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE) to solve the problem for the human upper limb, which is a very typical redundancy model in nature. First of all, the angles of the joints were measured by a proposed Exoskeleton type Human Motion Capture System (E-HMCS) when the wearer performs some Activities of Daily Living (ADL) and athletic activities. The values of these measured angles joints then were put into the forward kinematics model to find the end effector trajectories. After having these orbits, they were re-fed into the proposed Pro-ISADE algorithm mentioned above to process the IK problem and obtain the predicted joints angular values. The experimental results showed that the predicted joints' values closely follow the measured joints' values. That demonstrates the ability to apply the Pro-ISADE algorithm to solve the problem of Inverse Kinetics of the human upper limb as well as the upper limb rehabilitation robot arm.

关键词： optimization algorithm Inverse kinematics (IK) Exoskeleton type motion capture system (E-HMCS) Activities of daily living (ADL)

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A Complete Continuous Target Coverage Model for Emerging Applications of Wireless Sensor Network Using Termite Flies optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期127卷 1479-1501页

作者： Subramanian, K. Shanmugavel, S. Natl Engn Coll Dept ECE Kovilpatti India

In everyday life, the Wireless Sensor Network has attained high demand increasingly since it provides more network structure to create various kinds of innovative real-time applications. One of the essential applications of WSN is target coverage. Forest, agriculture, underwater, terrorism, and other applications have used the target coverage model following its nature. Existing target coverage models are not efficient and continuous, and the application performance is poor. The above-said problem has taken into account, and various earlier research works proposed a different target coverage model, not up to the application requirement. This paper focused on providing an efficient target coverage model for various real-time applications. Thus, a complete, continuous, target coverage model is created for environmental monitoring applications using a novel Termite Flies optimization (TFO) algorithm. Based on the termite fly's movement, distance, targets are covered by optimal sensor nodes. From the experiment, it is found that the proposed TFO algorithm outperforms the existing approaches.

关键词： Target coverage WSN optimization algorithm Termite flies optimization

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A novel optimization algorithm for assemblable region boundary points search in high-dimensional posture space of parts assembly

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第9-10期128卷 4615-4633页

作者： Li, Yue Qie, Yifan Huang, Zhicheng Qiao, Lihong Anwer, Nabil Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Univ Paris Saclay ENS Paris Saclay LURPA F-91190 Gif Sur Yvette France

Surface deviations caused by manufacturing errors have an essential influence on the assemblability of mechanical parts. The concept of assemblable region in the posture space has been introduced as an effective tool to express the posture feasible adjustment range of non-ideal parts in the assembly process. It can reflect the complexity of posture control through ergonomic principles and thus indicates the difficulty and feasibility of assembly. The assemblability considering surface deviations can be evaluated based on the geometric and topological (G & T) properties of the assemblable region. However, one necessary precondition for realizing the analysis of G & T properties is to obtain the boundary of assemblable region in high-dimensional posture space while guaranteeing the computational efficiency. In this paper, a novel optimization algorithm is proposed for the boundary calculation of the assemblable region. The high-dimensional irregular boundary calculation problem is firstly transformed into a boundary points search problem. Then, a new heuristic boundary points diffusion search (BDS) algorithm is developed. Two main parts are designed within the proposed algorithm, including the diffusion search rules as well as the collaborative search process. By optimizing the location and quantity of points in searching group, the algorithm can efficiently achieve the boundary points search goal and realize the boundary calculation of assemblable region. Finally, two typical cases are carried out to verify the feasibility of the proposed algorithm. The comparison with an improved genetic algorithm in application also highlights the efficiency and stability of the proposed algorithm.

关键词： Assemblability evaluation Posture space Assemblable region Boundary points search optimization algorithm

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A semantic and intelligent focused crawler based on semantic vector space model and membrane computing optimization algorithm

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APPLIED INTELLIGENCE 2023年第7期53卷 7390-7407页

作者： Liu, Wenjun Gan, Zurui Xi, Tiejun Du, Yajun Wu, Jing He, Yu Jiang, Pengjun Liu, Xing Lai, Xia XiHua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China XiHua Univ Xihua Honors Coll Chengdu 610039 Peoples R China

The focused crawler downloads web pages related to the given topic from the Internet. In many research studies, most of focused crawler predict the priority values of unvisited hyperlinks by integrating the topic similarities based on the text similarity model and equivalent weighted factors based on the manual method. However, in these focused crawlers, there are flaws in the text similarity models, and weighted factors are arbitrarily determined for calculating priorities of unvisited URLs. To solve these problems, this paper proposes a semantic and intelligent focused crawler based on the Semantic Vector Space Model (SVSM) and the Membrane Computing optimization algorithm (MCOA). Firstly, the SVSM method is used to calculate topic similarities between texts and the given topic. Secondly, the MCOA method is used to optimize four weighted factors based on the evolution rules and the communication rule. Finally, this proposed focused crawler predicts the priority of each unvisited hyperlink by integrating the topic similarities of four texts and the optimal four weighted factors. The experiment results indicate that the proposed SVSM-MCOA Crawler improve the evaluation indicators compared with the other four focused crawlers. In conclusion, the proposed SVSM and MCOA method promotes the focused crawler to have semantic understanding and intelligent learning ability.

关键词： Focused Crawler Semantic Vector Space Model Membrane Computing Semantic Similarity optimization algorithm

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Chaotic enhanced colliding bodies optimization algorithm for structural reliability analysis

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ADVANCES IN STRUCTURAL ENGINEERING 2020年第3期23卷 438-453页

作者： Cheng, Jiaming Zhao, Wei Jinan Univ Sch Mech & Construct Engn MOE Key Lab Disaster Forecast & Control Engn Guangzhou 510632 Guangdong Peoples R China

It is of extreme importance to assess the failure probability and safety level of structural system in structural design. Nowadays, many researchers presented several approaches for structural reliability analysis, such as the first-order reliability method, Monte Carlo simulation, and the meta-heuristic algorithm. The meta-heuristic algorithm is not only efficient to solve global optimization problems but also shown to be an effective tool for structural reliability analysis. A recent meta-heuristic optimization approach, enhanced colliding bodies optimization, has emerged as a relatively simple implementation with a fast convergence speed. Chaos theory is characterized by its ergodicity, pseudo-randomness, and irregularity. This article thus presents a novel approach introducing chaotic maps into the enhanced colliding bodies optimization algorithm to promote the performance of convergence, named as chaotic enhanced colliding bodies optimization algorithm. The proposed algorithm uses chaotic maps to change the generation pattern of particles and improve convergence characteristics. A procedure based on the effective use of the represented chaotic enhanced colliding bodies optimization is then applied in structural reliability analysis. A variety of numerical and structural problems are tested in this article to demonstrate that the given method actually improves the performance of enhanced colliding bodies optimization in convergence as well as the accuracy for reliability analysis compared with the other methods existing in the literature.

关键词： chaos enhanced colliding bodies optimization meta-heuristic algorithm optimization algorithm structural reliability

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Improvement of optimization algorithm in active gas/odor sensing system

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SENSORS AND ACTUATORS A-PHYSICAL 1995年第3期50卷 191-196页

作者： Nakamoto, T Matsushita, H Okazaki, N Faculty of Engineering Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152 Japan

In the field of gas or odor sensing, it is difficult to quantify the composition of a mixture of aromas accurately. We proposed earlier the method of active sensing for this purpose and quantification was successfully performed in principle. Here, an optimization algorithm such as the gradient descent method for quantifying the mixture composition is improved by the introduction of least-squares and singular-value decomposition methods so that the stability of the system can be improved. Better convergence is achieved after the improvement.

关键词： gas sensing odor sensing optimization algorithm

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A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability

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NEUROCOMPUTING 2024年 592卷

作者： Zhang, Ye Zhu, Yutong Li, Haoyu Wang, Jingyu Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

The paper aims to solve the problem of multi -agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm.

关键词： Trajectory planning optimization algorithm Differential evolution Convergence in probability

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An optimization algorithm of Energy Management for HEB Based on Pontryagin's Minimum Principle

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WIRELESS PERSONAL COMMUNICATIONS 2018年第1期103卷 1011-1023页

作者： Wu, Zhen Yin, Guodong Wang, Yeqin Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China Huaiyin Inst Technol Fac Automat Huaian Peoples R China

Fuel economy is pursued by hybrid electric bus (HEB). However, a key challenge for a hybrid bus is to achieve near-optimality while keeping the energy management strategy (EMS) simple. In this paper, an EMS based on the Pontryagin's minimum principle (PMP) is developed and implemented in ADVISOR. The designed EMS can successfully calculate the fuel consumption of the parallel HEB under the specific driving cycle. The simulation result suggests that the fuel economy of the hybrid electric bus with PMP-based control strategy is close to the benchmarking optimal solution calculated through DP, which is much better than the fuel economy of the parallel HEB with the default control strategy in ADVISOR. To sum up, the EMS based on PMP can improve the fuel economy by adjusting the engine operating point to reduce the exhaust emissions. Simultaneously, it can also extend the battery life by maintaining the battery state of charge at an appropriate level.

关键词： Hybrid electric bus Pontryagin's minimum principle Fuel economy SOC optimization algorithm

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Sensitivity analysis and optimization algorithm-based on nonlinear programming

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第9期E91A卷 2426-2434页

作者： Oda, Masayoshi Yamagami, Yoshihiro Kawata, Junji Nishio, Yoshifumi Ushida, Akio Univ Tokushima Fac Engn Dept Elect & Elect Engn Tokushima 7708506 Japan Tokushima Bunri Univ Fac Engn Dept Mech & Elect Engn Sanuki 7692193 Japan

We propose here a fully Spice-oriented design algorithm of op-amps for attaining the maximum gains under low power consumptions and assigned slew-rates. Our optimization algorithm is based on a well-known steepest descent method combining with nonlinear programming. The algorithm is realized by equivalent RC circuits with ABMs (analog behavior models) of Spice. The gradient direction is decided by the analysis of sensitivity circuits. The optimum parameters can be found at the equilibrium point in the transient response of the RC circuit. Although the optimization time is much faster than the other design tools, the results might be rough because of the simple transistor models. If much better parameter values are required, they can be improved with Spice simulator and/or other tools.

关键词： sensitivity analysis steepest descent method optimization algorithm spice-oriented

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An effective metaheuristic technology of people duality psychological tendency and feedback mechanism-based Inherited optimization algorithm for solving engineering applications

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Wang, Kaiguang Guo, Min Dai, Cai Li, Zhiqiang Wu, Chengwei Li, Jiahang Shaanxi Normal Univ Sch Comp Sci Key Lab Modern Teaching Technol Minist Educ Xian 710119 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

Nature- and society -inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism -based Inherited optimization algorithm(IOA), which is inspired by people showing positive -negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency.

关键词： Metaheuristics Exploration and exploitation Constrained engineering optimization Benchmark problem optimization algorithm

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