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Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors

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MEDICAL ENGINEERING & PHYSICS 2024年 124卷 104060-104060页

作者： Zhang, Yue Cao, Gangsheng Sun, Maoxun Zhao, Baigan Wu, Qing Xia, Chunming Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human -machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.

关键词： Mechanomyography Pattern recognition Convolutional neural network Bald eagle search Grey wolf optimization sparrow search algorithm

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TentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 9162-9193页

作者： Hou, Ke Sun, Jianping Guo, Mingcheng Pang, Ming Wang, Na Xian Shiyou Univ Sch Econ & Management Xian 710065 Peoples R China Digital Intelligence Driven Energy Technol Innovat Energy Project Management & Innovat Strategy Think Xian 710065 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430035 Peoples R China

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight;an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed;and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers.

关键词： Evaluation of cloud service providers sparrow search algorithm BP neural network Tent chaotic map Adaptive inertia weight

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Research on load excitation identification method of multi-connected air conditioning compressor based on RBF network with multi-strategy fusion SSA

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第11期15卷 5185-5198页

作者： Wang, Lu Fang, Qiansheng Gao, Lifu Sun, Yuxiang Cao, Huibin Anhui Jianzhu Univ Hefei 230601 Anhui Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

The load excitation of the multi-connected air conditioning compressor is the major source of vibration in the entire air conditioning pipeline system, which imposes a direct impact on the vibration noise and reliability of the overall system. Accurate identification of the compressor's load excitation is a primary condition to ensure its normal operation and is an essential part of the comprehensive testing and evaluation of the system's dynamic performance. In this paper, aiming at the problem of the inability to directly identify the time-varying load excitation of the multi-circuit air conditioning compressor, a predictive model is built on the basis of the Radial Basis Function (RBF) Neural Network. Meanwhile, to ameliorate the overall network model's identification performance, a Multi-Strategy Fused sparrow search algorithm (ISSA) is adopted for optimization regarding the center, the basic functions' variance, and the weights from the hidden layer to the input one in RBF. This algorithm not only improves the joiners, discoverers, and scouts positioning in the sparrow search algorithm (SSA) but also adds the Firefly algorithm (FA) to disturb the positions of all sparrows, thereby preventing the algorithm from being trapped in local optima and enhancing the global search ability. The results of practical testing and simulation experiments show that the Firefly algorithm improved Multi-Strategy Fused sparrow search algorithm-Radial Basis Function (FAISSA-RBF) algorithm reduces the convergence to below 10-2, and the R2 is improved by approximately 12.7% compared to the SSA-RBF. This ensures the normal and stable operation of the air conditioning compressor system under time-varying conditions and the effective identification of load excitation.

关键词： Load excitation identification Multi-connected air conditioning compressor Radial basis function neural network Multi-strategy fusion sparrow search algorithm

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Short-term power load forecasting based on SKDR hybrid model

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ELECTRICAL ENGINEERING 2024年 1-17页

作者： Yuan, Yongliang Yang, Qingkang Ren, Jianji Mu, Xiaokai Wang, Zhenxi Shen, Qianlong Li, Yanan Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Peoples R China Henan Polytech Univ Sch Software Jiaozuo 454003 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Jilin Univ Sch Commun Engn Changchun 130012 Peoples R China

Load forecasting is an important index to ensure the stable operation of power system. In recent years, load forecasting methods based on machine learning algorithms have received extensive attention. However, for such a complex problem, the traditional load forecasting method based on machine learning cannot solve the problem efficiently and accurately. Therefore, the ensemble learning method has gradually entered the field of view of researchers. Among them, stacking methods based on heterogeneous learners have received less attention. To that end, support vector machine (SVR), K-nearest neighbor (KNN) and decision tree (DT) are used as the base learners, and random forest (RF) is used as the meta-learner to construct a novel Stacking ensemble learning model (SKDR) in this study. Besides, due to hyperparameters are essential elements affecting the predicted result, the sparrow search optimization algorithm is introduced to obtain the optimal combination of hyperparameters. The effectiveness and advancement of SKDR is validated on a real-world dataset. Experimental results showed that compared with traditional methods, the proposed method could provide competitive prediction results, that is R2 = 0.984/0.987, RMSE = 1.315/1.253, MAPE = 0.146/0.163, this illustrates the SKDR's potential in terms of load forecasting. The performance of SKDR is also verified on the open-source dataset.

关键词： Load forecasting Stacking ensemble algorithm Sliding time window sparrow search algorithm

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Integrating analytical and machine learning approaches to simulate and predict dam foundation stress and river valley contraction in a large-scale reservoir

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第11期83卷 444页

作者： Zhou, Ziwen Zhou, Zhifang Vanapalli, Sai K. Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Nanjing Inst Environm Sci Minist Ecol & Environm Peoples Republ China Nanjing 210042 Peoples R China Univ Ottawa Dept Civil Engn Ottawa ON K1N 6N5 Canada

The safety of several large-scale reservoirs all over the world has been of concern due to dam foundation stress (DFS) that gradually changes following impoundment inducing the river valley contraction (RVC). Presently, there are limited approaches for the prediction of DFS and RVC based on complex hydro-geomechanics principles. However, these approaches require extensive information that is cumbersome and time-consuming to gather and hence expensive. In this paper, five machine learning models (MLMs) for DFS and RVC prediction were established by merging innovative analytical, BP neural networks and optimized algorithm approaches. Three key influencing factors;namely: seepage, temperature, and creep are used as input information in these models. The developed MLMs were validated using well-documented case study results over nine years for Xiluodu reservoir in China. The trend-fitting effect and statistical indicators of the proposed MLMs demonstrated strong predictive ability (R-2 > 0.9). Among the MLMs, Generic algorithm-BP and sparrow search algorithm-BP methods were found to be comprehensive. The predicted RVC and DFS using MLMs are consistent with the coupled multi-field analytical method from the literature and provide reliable predictions using limited information. This study serves as a valuable reference for predicting DFS and RVC of large reservoirs for ensuring long-term safety.

关键词： Dam foundation stress River valley contraction Multi-field evolution Aquifer hydraulic response Machine learning sparrow search algorithm

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Optimal Measurement Poses Using LSSA for Robot Kinematics-Flexibility Calibration

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 4974-4981页

作者： Ma, Shoudong Lu, Yong Deng, Kenan Guan, Qinghe Xu, Xu Harbin Inst Technol Sch Mech Engn Harbin 150001 Heilongjiang Peoples R China Hangzhou Ying Ming Cryogen Vacuum Engn Co Ltd Hangzhou 310024 Zhejiang Peoples R China

The absolute positioning of robots is a primary factor limiting their applications. In order to enhance the efficiency and precision of robot calibration, this study introduces a method that utilizes the Levy flight and sparrow search algorithm (LSSA) to optimize the measurement pose of the robot, reducing the number of measurement poses and improving calibration accuracy. Firstly, an error model for robot positioning is established by considering the kinematic error model based on the product of exponentials (POE) and a flexible error model that accounts for the robot's own weight. Subsequently, an observability index for optimizing robot measurement poses is introduced, and LSSA is employed to optimize the most suitable subset of poses. Finally, the proposed robot measurement calibration optimization algorithm and calibration method are validated through simulations and experimental cases. The experimental results show that the proposed algorithm can effectively improve the absolute positioning accuracy of the robot, and the mean value of the end-effector position error is reduced from 0.898 mm to 0.291 mm while reducing the number of measurement poses.

关键词： Calibration industrial robots optimal measurement poses sparrow search algorithm the product of exponential

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A Model of Multi-Finger Coordination in Keystroke Movement

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SENSORS 2024年第4期24卷 1221页

作者： Lin, Jialuo Ding, Baihui Song, Zilong Li, Zheng Li, Shengchao Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China

In multi-finger coordinated keystroke actions by professional pianists, movements are precisely regulated by multiple motor neural centers, exhibiting a certain degree of coordination in finger motions. This coordination enhances the flexibility and efficiency of professional pianists' keystrokes. Research on the coordination of keystrokes in professional pianists is of great significance for guiding the movements of piano beginners and the motion planning of exoskeleton robots, among other fields. Currently, research on the coordination of multi-finger piano keystroke actions is still in its infancy. Scholars primarily focus on phenomenological analysis and theoretical description, which lack accurate and practical modeling methods. Considering that the tendon of the ring finger is closely connected to adjacent fingers, resulting in limited flexibility in its movement, this study concentrates on coordinated keystrokes involving the middle and ring fingers. A motion measurement platform is constructed, and Leap Motion is used to collect data from 12 professional pianists. A universal model applicable to multiple individuals for multi-finger coordination in keystroke actions based on the backpropagation (BP) neural network is proposed, which is optimized using a genetic algorithm (GA) and a sparrow search algorithm (SSA). The angular rotation of the ring finger's MCP joint is selected as the model output, while the individual difference information and the angular data of the middle finger's MCP joint serve as inputs. The individual difference information used in this study includes ring finger length, middle finger length, and years of piano training. The results indicate that the proposed SSA-BP neural network-based model demonstrates superior predictive accuracy, with a root mean square error of 4.8328 degrees. Based on this model, the keystroke motion of the ring finger's MCP joint can be accurately predicted from the middle finger's keystroke motion information,

关键词： multi-finger coordinated keystroke Leap Motion sensors coordination BP neural network sparrow search algorithm genetic algorithm

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Image-based visual servoing with Kalman filter and swarm intelligence optimisation algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2024年第5期238卷 820-834页

作者： Dong, Jiuxiang Li, Yang Wang, Bingsen Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

The article proposes a new Kalman depth estimation and an improved swarm intelligence optimisation algorithm for adaptive tuning of servo gain for image-based visual servo control. First, a Kalman depth estimation model is established from the principle of image-based visual servoing, and two state equations are designed for depth estimation based on the number of state quantities. Second, the improved sparrow search algorithm is proposed to tune the servo gain adaptively to improve the convergence speed and stability. To verify the effectiveness of the proposed method, the conventional image-based visual servoing and conventional Kalman estimation are reproduced and compared with the proposed method, and the simulation is completed on the Simulink simulation platform for verification. Finally, the experiments are completed in the robotic arm experimental platform. Both the simulation and experimental results show the effectiveness of the proposed method, which reduces the redundancy of the camera and shortens the convergence time.

关键词： Depth estimation sparrow search algorithm Kalman filter image-based visual servo

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Intelligent risk identification for drilling lost circulation incidents using data-driven machine learning

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 252卷

作者： Wu, Shengnan Hu, Yiming Zhang, Laibin Liu, Shujie Xie, Renjun Yin, Zhiming China Univ Petr Coll Safety & Ocean Engn Beijing Peoples R China Minist Emergency Management Key Lab Oil & Gas Safety & Emergency Technol Beijing Peoples R China Hainan Branch CNOOC China Co Ltd Haikou Peoples R China CNOOC Res Inst Ltd Beijing Peoples R China

In drilling operations, lost circulation (LC) poses a significant challenge, potentially leading to severe accidents such as overflow or blowouts if not accurately identified and controlled. This study introduces a method for identifying LC risk severity using data-driven machine learning and statistical algorithms, specifically demonstrated in the Ying-Qiong Basin of the South China Sea. The method begins with variance filtering to eliminate invalid data, followed by the application of the Pearson correlation coefficient to remove redundant data. This process results in the selection of 12 logging parameters out of the initial 38. Combined with indirect judgments of LC severity based on relevant prior knowledge, the data is labeled to construct the original dataset. A feature extraction and optimization method is proposed, which employs sliding time windows to extract 21 features. These features are then selected and optimized using a random forest algorithm to construct the optimal feature set. To enhance the controllability of the Extreme Learning Machine (ELM) model, the sparrow search algorithm is integrated. Additionally, several fitness functions are introduced for both regression and classification models, resulting in the development of the improved ELM (IELM) model. The dataset is split into training and test sets, and the performance of the IELM model is compared to that of the standard ELM and BP neural network models. The results indicate that the IELM model outperforms the other two, achieving an impressive test F1 score of 97.22 % for accurately identifying LC severity. Moreover, the model trained using the optimal feature set demonstrates higher accuracy and faster convergence. The proposed feature extraction and optimization method effectively increases the amount of information contained in the data, thereby enhancing the model's performance.

关键词： sparrow search algorithm Improved extreme learning machine Lost circulation Feature extraction and optimization Risk identification

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KMSSA optimization algorithm for bandwidth allocation in internet of vehicles based on edge computing

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 11869-11892页

作者： Hsieh, Chao-Hsien Yao, Xinyu Wang, Zhen Wang, Hongmei Guangzhou Coll Technol & Business GCTB NSU Joint Inst Technol Foshan 528138 Guangdong Peoples R China Qufu Normal Univ Sch Cyber Sci & Engn Qufu 273165 Shandong Peoples R China

With the development of science and technology, vehicles have gradually become smart network vehicles. However, applications on vehicles require great network and computing performance. It poses a great challenge to the existing Internet of Vehicles (IoV). Cloud computing can relieve the pressure of data processing, but the response time could take a long time. In the edge computing, edge server processing requests can effectively reduce response time. For example, during rush hour, in order to respond quickly, a large number of requests can be sent from the vehicles layer to the edge server. However, due to the limited bandwidth resources of edge servers, it is necessary to develop an effective bandwidth allocation model. The research in this paper consists of two main parts. First, this paper proposes a multi-objective bandwidth allocation optimization model for addressing this issue. The goal is to minimize average response time and maximize bandwidth utilization. Second, a multi-objective sparrow search algorithm (KMSSA) based on K-means algorithm is proposed to solve multi-objective bandwidth allocation. This paper compares KMSSA with particle swarm optimization (PSO) and sparrow search algorithm (SSA) on iFogSim platform. The results show that KMSSA has good optimization performance. The algorithm can be successfully applied to solve global multi-objective optimization problems. To compare with PSO algorithm, the performance of KMSSA is improved by 10%. To compare with SSA, the performance of KMSSA is improved by 5%. Also, KMSSA has superiority in solving practical problems. Its global optimal solution has superior performance in terms of average response time, bandwidth utilization, energy consumption, and cost.

关键词： Internet of vehicles Edge computing Static bandwidth allocation sparrow search algorithm

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