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Time-Optimal Asymmetric S-Curve Trajectory Planning of Redundant Manipulators under Kinematic Constraints

SENSORS 2023年第6期23卷 3074-3074页

作者： Liu, Tianyu Cui, Jingkai Li, Yanhui Gao, Siyuan Zhu, Mingchao Chen, Liheng Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys CAS Key Lab On Orbit Mfg & Integrat Space Opt Syst Changchun 130033 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.

关键词： asymmetric S-curve velocity scheduling trajectory planning redundant manipulators whale optimization algorithm kinematic constraints

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A high-accuracy and low-energy range-free localization algorithm for wireless sensor networks

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2023年第1期2023卷 37页

作者： Cui, Huanqing Wang, Sen Zhou, Chuanai Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Qingdao Binhai Univ Coll Business Qingdao Peoples R China

As the backbone of the Internet of Things, wireless sensor networks are widely applied to perceive the physical world. Most applications need to associate perception information with a position to generate physical significance. This paper proposes WRCDV-Hop, which has quadruple improvements of the well-known DV-Hop. First, the hop count between a pair of sensor nodes is measured as a continuous value rather than a discrete value. Second, the unknown nodes calculate the average distance per hop by the weighted method. Third, each sensor node only records and relays limited but sufficient beacons of the anchors. Fourth, the unknown nodes apply the whale optimization algorithm to estimate positions. The first two improvements ensure that the distance estimation between a pair of sensor nodes is highly accurate, and the third improvement reduces the energy consumption. The last improvement makes the position estimation more precise. The simulation results show that WRCDV-Hop performs well in terms of localization accuracy and energy consumption.

关键词： Wireless sensor network localization Range-free whale optimization algorithm Hop count

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whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis

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JOURNAL OF BIOMEDICAL INFORMATICS 2019年 92卷 103124-000页

作者： Zhao, Dandan Liu, Hong Zheng, Yuanjie He, Yanlin Lu, Dianjie Lyu, Chen Shandong Normal Univ Sch Informat Sci & Engn 88 Wenhua East Rd Jinan Peoples R China Shandong Prov Key Lab Novel Distributed Comp Soft Jinan Peoples R China

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the whale optimization algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.

关键词： Colorectal cancer Feature selection whale optimization algorithm Mixed kernel function Support vector machine

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Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion

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DEVELOPMENTS IN THE BUILT ENVIRONMENT 2023年 14卷

作者： Yu, Yang Li, Jiantao Li, Jianchun Xia, Yong Ding, Zhenghao Samali, Bijan Univ New South Wales Ctr Infrastructure Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia Zhejiang Univ Technol Sch Civil Engn Hangzhou 310000 Zhejiang Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Western Sydney Univ Ctr Infrastruct Engn Penrith NSW 2751 Australia

A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.

关键词： Structural damage diagnosis Deep stacked autoencoders Multi -sensor fusion whale optimization algorithm

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A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination

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ENERGY 2024年 287卷

作者： Zheng, Xidong Bai, Feifei Zeng, Ziyang Jin, Tao Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Griffith Univ Sch Engn & Built Environm Gold Coast Qld 4222 Australia Fujian Prov Univ Engn Res Ctr Smart Distribut Grid Equipment Fuzhou 350108 Peoples R China

The proper integration of wind power-driven grids relies heavily on a reliable balance between electricity production and demand. Therefore, accurate prediction is essential for planning and efficient operation of wind power systems to ensure their continuous supply. However, increasingly severe power quality disturbance (PQD) constantly disturbs this equilibrium, which affects the accuracy of wind power prediction to a large extent. For this purpose, this paper developed a novel optimization methodology to improve wind power prediction accuracy considering micro PQD dimension reduction and elimination for wind-storage integrated systems. A novel idea has been presented in this optimization methodology to eliminate the barrier of PQD and wind power prediction. In the micro aspect, a PQD dimension reduction and elimination strategy based on dynamic mode decomposition (DMD) and Wiener Filter (WF) is proposed to eliminate the autonomy of PQD. This elimination of autonomy allows the WF to get a higher signal-to-noise ratio (SNR). In the macro aspect, this paper takes PQD of different complexity into full consideration, and compares their effects on improving wind power prediction accuracy based on deep learning-based approaches. Through the experimental verification, it is confirmed that the proposed DMD-WF optimization method has demonstrated an effective dimension reduction and elimination of PQD. Moreover, it is found that the proposed PQD optimization method contributes to improve the deep learning-based prediction accuracy when PQD is more complex. The proposed methodology creates a novel perspective to improve the short-term wind power prediction accuracy, which provides a theoretical and methodological guidance for future development of large-scale integrated wind-storage systems.

关键词： Power quality disturbance Dynamic mode decomposition Wiener filter whale optimization algorithm Attention mechanism Long short-term memory

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PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOA-BPNN algorithm

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FRONTIERS IN HEAT AND MASS TRANSFER 2022年 18卷

作者： Xiao, Rongge Zhuang, Qi Jin, Shuaishuai Jin, Wenbo Xian Shiyou Univ Coll Petr Engn Shaanxi Key Lab Adv Stimulat Technol Oil & Gas Re Xian 710065 Shaanxi Peoples R China

A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using whale optimization algorithm (WOA) to optimize BPNN model (WOA-BPNN). Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area as an example, the simulation calculation is carried out in MATLAB, and the Genetic algorithm optimized BPNN(GA-BPNN) and the non Optimized BP neural network are used as comparative models for comparative analysis. The results show that the Mean Relative Error (MRE) of WOA-BPNN model in predicting wax deposition rate is 2.72% and the coefficient of determination(R-2) is 0.9966, which are better than those of BPNN and GA-BPNN models. It is proved that WOA-BPNN model has higher accuracy and robustness in predicting wax deposition rate.

关键词： whale optimization algorithm Wax deposition rate BP Neural Network Prediction Model

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Classifying Heart Disease in Medical Data Using Deep Learning Methods

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Journal of Computer and Communications 2021年第1期9卷 66-79页

作者： T. Velmurugan U. Latha PG and Research Department of Computer Science D. G. Vaishnav College Chennai India

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and whale optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

关键词： Named Entity Recognition algorithm Neural Network Methods whale optimization algorithm F-Measure Recall Precision

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基于元启发式算法的船舶动力装置随机建模与性能优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 751-761页

作者： Monika Saini Bhavan Lal Patel Ashish Kumar Department of Mathematics&Statistics Manipal University JaipurJaipur-303007India

For the successful operation of any industry or plant continuous availability of power supply is *** of the large-scale plants established their power generation *** power plant having two generators is also fall in this *** this study,an effort is made to derive and optimize the availability of a marine power plant having two generators,one switch board and distribution *** this purpose,a mathematical model is proposed using Markov birth death process by considering exponentially distributed failure and repair rates of all the *** availability expression of marine power plant is *** algorithms namely dragonfly algorithm(DA),bat algorithm(BA)and whale optimization(WOA)are employed to optimize the availability of marine power *** is revealed that whale optimization algorithm outperforms over dragonfly algorithm(DA),and bat algorithm(BA)in optimum availability prediction and parameter *** numerical values of the availability and estimated parameters are appended as numerical *** derived results can be utilized in development of maintenance strategies of marine power plants and to carry out design modifications.

关键词： Markov process whale optimization algorithm Dragonfly algorithm Availability Marine power plant

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired algorithms

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Procedia Computer Science 2020年 173卷 18-27页

作者： Sangeeta Gupta Gaurava Gautam Deepak Divyanshu Vats Pragya Varshney Smriti Srivastava Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi -110078 India

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms.

关键词： optimization Techniques Fractional Calculus Fractional Order Fractional Order Financial Chaotic system whale optimization algorithm Ant colony optimization Grey wolf optimization Artificial bee colony optimization

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whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system

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Energy Procedia 2019年 162卷 77-86页

作者： M.A. Ebrahim Adham Osama Khaled Mohamed Kotb Fahmy Bendary Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt National Water Research Center ELqanater Elkhayrya 13411 Egypt

Over the past decades, meta-heuristic optimization techniques have become surprisingly very popular due to their flexibility and local optima avoidance capability. This paper uses the whale optimization algorithm (WOA), a swarm-based technique to tune the Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) controllers of a grid-connected solar Photovoltaic (PV) system. The results of the PI-based Incremental Conductance (IC) MPPT technique are compared with both the conventional incremental conductance and the Perturb & Observe (P&O) MPPT techniques. Various modes of the PI controller are used. I, PI and Fractional order PI (FOPI) gain parameters are determined using WOA. Performance indices are applied to estimate the best parameters of the PI controller. This paper aims to show the effect of using PI-based MPPT controllers on enhancing the performance of a 400-kW grid-connected PV system. Simulation results show the capability of PI-based MPPT controllers on improving the performance of the PV system. It demonstrates the superiority of FOPI controllers over the other modes in enhancing system performance. The proposed work is simulated using MATLAB SIMULINK.

关键词： Maximum Power Point Tracking Incremental conductance Perturb Observe whale optimization algorithm Proportional-Integral controller Photovoltaic

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