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A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios

IEEE ACCESS 2023年 11卷 46144-46163页

作者： Yang, Xueying Hu, Min Zhang, Rui Huang, Gang Space Engn Univ Dept Aerosp Sci & Technol Beijing 101416 Peoples R China

Multi-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved adaptive differential evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response.

关键词： Satellites evolutionary computation Image resolution Task analysis Optimization Heuristic algorithms Statistics Multi-satellite imaging mission planning global large-scale imaging mission emergency support mission adaptive differential evolution algorithm individual weight ranking rule

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Active Disturbance Rejection Control Based on adaptive differential evolution for Two-Wheeled Self-Balancing Robot

Active Disturbance Rejection Control Based on Adaptive Diffe...

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第28届中国控制与决策会议

作者： Libiao Jiang Huachuan Qiu Zhongwei Wu Jiashou He College of Mechanical & Automotive Engineering South China University of Technology

ISBN: (纸本)9781467397155

An active disturbance rejection control(ADRC) technique based on adaptive differential evolution(ADE) algorithm and an improved PID control strategy were proposed so as to solve the problem of self-balancing control and steering control for two-wheeled self-balancing *** using Lagrange formula,the nonholonomic dynamic mathematical model was derived based on the nonholonomic dynamic Routh equation of generalized *** self-balancing control,an ADRC strategy whose parameters were adjusted by means of ADE algorithm was *** steering control,a PID control strategy combined with tracking differentiator(TD) was *** dynamic model of the whole robot was built through Adams software by applying virtual prototype ***,co-simulations using both Matlab/Simulink and Adams were carried *** co-simulation results showed that the supposed control strategy could achieve self-balancing and steering operation effectively with the performance of fast adjusting speed,high accuracy and strong robustness as well.

关键词： Two-wheeled Self-balancing Robot Mathematical Modeling Active Disturbance Rejection Control adaptive differential evolution algorithm Co-simulation

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A Model for Text Summarization

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2017年第1期13卷 67-85页

作者： Alguliyev, Rasim M. Aliguliyev, Ramiz M. Isazade, Nijat R. Abdi, Asad Idris, Norisma Azerbaijan Natl Acad Sci Inst Informat Technol Baku Azerbaijan Univ Malaya Dept Artificial Intelligence Kuala Lumpur Malaysia

Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

关键词： adaptive differential evolution algorithm Information Diversity k-Means Optimization Model Sentence Clustering Text Summarization

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Production Scheduling of Batch Processes Based on adaptive DE algorithm

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JOURNAL OF COMPUTERS 2013年第8期8卷 1968-1972页

作者： Zhang, Lieping Song, Gang Zhang, Zuqiong Guilin Univ Technol Coll Mech & Control Engn Guilin Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin Peoples R China Guilin Univ Technol Modern Educ Technol Ctr Guilin Peoples R China

Production scheduling problems and traditional differential evolution(DE) are analyzed in this paper. Taking the minimum of total completion time as the objective function of production scheduling, a production scheduling model is established for batch processes in process industry. Considering that the performance of the traditional DE algorithm is sensitive to the parameter settings, an adaptive control evolution strategy is used to control the parameter scale factor F and crossover probability CR to solve the problem of parameter settings. Taking a practical production problem as a scheduling example, the established model and adaptive DE algorithm are applied to implement the production scheduling simulation. The simulation results show that compared with the ant colony system(ACS) algorithm, the adaptive DE algorithm is effective and feasible.

关键词： batch processes adaptive differential evolution algorithm production scheduling ant colony system algorithm

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A model-based optimization of vapor compression refrigeration system for energy saving

A model-based optimization of vapor compression refrigeratio...

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Chinese Automation Congress (CAC)

作者： Kong, Dehao Yin, Xiaohong Wang, Xinli Ding, Xudong Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266042 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Prov Key Lab Intelligent Bldg Technol Jinan 250061 Peoples R China

ISBN: (纸本)9781728176871

In this paper, a model-based global optimization strategy for vapor compression refrigeration system (VCRS) is proposed. The energy models of compressor, evaporator fan and condenser fan are established, and the energy consumption under different working conditions are predicted. To optimize the energy consumption of the entire system under the premise of ensuring indoor cooling demands, heat transfer models of the evaporator and condenser are also established. An objective function and component interaction optimization problem that satisfies indoor cooling capacity constraints are proposed. Six controllable variables related to the performance of the VCRS system are chosen as the control settings. The self-adaptive differential evolution (SADE) algorithm with fast convergence speed is used to solve the optimization problem, and the optimal control settings are obtained. Simulations are carried out and the proposed optimization strategy is compared with the traditional strategy. The results show that the global optimization of the operation strategy can achieve 15.18% energy saving effect on the test day.

关键词： vapor compression refrigeration system global optimization strategy adaptive differential evolution algorithm

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Two-dimensional Photoelectric Compensation based on BP Neural Network 3

Two-dimensional Photoelectric Compensation based on BP Neura...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Yang Xiaojing Jiao Qingju Liu Xinke Anyang Normal Univ Coll Comp & Informat Engn Anyang 455000 Peoples R China

ISBN: (纸本)9781538662434

To improve the accuracy of heading angle measured by electronic compass. a new error compensation method is proposed based on adaptive differential evolution algorithm and BP neural network. In the method, the 3-layer BP neural network is used to model heading angle error, and adaptive differential evolution algorithm is adopted to train the weights of network, thus obtaining a more exact error model, and compensating the heading angle error measured by electronic compass. This method is compared with eight position least square, BP neural network, differential evolution algorithm, BP neural network optimization and other compensation *** accuracy of error compensation is improved obviously, and it has the characteristics of strong global optimization ability, fast convergence speed and good stability. The experimental results show that after compensation, the error range of heading angle is decreased from -16 degrees similar to 30.7 degrees to -0.22 degrees similar to 0.2 degrees , which satisfies the needs of higher accuracy navigation systems.

关键词： electronic compass error compensation adaptive differential evolution algorithm BP neural network least squares

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MR Image Denoising Based on Improved Multipath Matching Pursuit algorithm 5th

MR Image Denoising Based on Improved Multipath Matching Purs...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Li, Chenxi Luo, Yitong Yang, Jing Fan, Hong Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Magnetic resonance images are accompanied by random Rician noise due to the influence of uncertain factors in the process of imaging, storage, which brings a lot of inconvenience to the subsequent processing of the image and clinical diagnosis. This paper proposes an improved multipath matching pursuit algorithm based on learning Gabor pattern dictionary atom for image reconstruction and denoising. Firstly, Gabor wavelet transform based on neurophysiological constraints is used to generate dictionary atoms that match the local features of the image;Then this paper introduces adaptive differential evolution algorithm optimization to the process of solving multiple candidate atoms matching the local image features in each iteration of the multipath matching pursuit. It combines the advantages of adaptive differential evolution and multipath matching pursuit algorithm, not only avoids the genetic falling into the local optimal defect, but also obtains the best matching parameters with higher accuracy, and effectively reduces the computational complexity of the multipath matching pursuit. In the reconstruction experiment of the simulated MR images, compared with state-of-the-art denoising algorithms, our algorithm not only shows better denoising performance, but also retains more detailed information, and the running time is reduced nearly 50% than multipath matching pursuit;which fully shows the clinical application value.

关键词： Gabor pattern Multipath matching pursuit algorithm adaptive differential evolution algorithm Dictionary atom MR image denoising

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Modelling of Multistage Centrifugal Compressor

Modelling of Multistage Centrifugal Compressor

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Niu, Dapeng Shi, Aiping Chang, Yuqing Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Provin Peoples R China Binzhou Univ Dept Automat Binzhou Shandong Peoples R China

ISBN: (纸本)9781457715846

Surge is an unstable operating mode of the centrifugal compressor that occurs at mass flows below the surge line, and it results in considerable loss of performance and efficiency of the compression system. Modelling of the centrifugal compressor is the basis of getting the surge line and carrying out anti-surge control. Based on fluid mechanics, mass and energy conservation principles, the mechanism model of a CCPP gas system's multistage centrifugal compressor with three single-stage compressors in series is established. The parameter estimation problem of the model is transformed into an optimization problem, and an adaptive differential evolution algorithm is used to decide the unknown parameters in the model. Model validation results show that the model can describe pressure ratio's varying trend of the multistage compressor. It lays the foundation for calculating the surge line and is of certain value to compression system's anti-surge control.

关键词： surge modelling multistage centrifugal compressor parameter estimation adaptive differential evolution algorithm

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COSUM: Text summarization based on clustering and optimization

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EXPERT SYSTEMS 2019年第1期36卷 e12340-e12340页

作者： Alguliyev, Rasim M. Aliguliyev, Ramiz M. Isazade, Nijat R. Abdi, Asad Idris, Norisma Azerbaijan Natl Acad Sci Inst Informat Technol Dept Informat Soc Baku Azerbaijan Rhein Westfal TH Aachen Dept Comp Sci Aachen Germany Univ Teknol Malaysia UTM Big Data Ctr Skudai Johor Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur Malaysia

Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two-stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k-means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state-of-the-art methods: DPSO-EDASum;LexRank;CollabSum;UnifiedRank;0-1 non-linear;query, cluster, summarize;support vector machine;fuzzy evolutionary optimization model;conditional random fields;MA-SingleDocSum;NetSum;manifold ranking;ESDS-GHS-GLO;and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state-of-the-art methods in terms of ROUGE-1 and ROUGE-2 measures.

关键词： adaptive differential evolution algorithm content coverage harmonic mean information diversity k-means optimization model sentence clustering text summarization

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A machine learning-based adaptive heuristic for vessel scheduling problem under uncertainty via chance-constrained programming

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Li, Runfo Zhang, Xinyu Wang, Chengbo Cui, Jinlong Mu, Mengfeng Dalian Maritime Univ Nav Coll Maritime Intelligent Transportat Res Team Dalian 116026 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Dept Automat Hefei Peoples R China

Efficient vessel scheduling in port is critical for enhancing navigational efficiency. However, it faces substantial challenges due to unforeseeable events. In this context, this paper addresses the vessel scheduling problem with stochastic sailing times in port. The problem is formulated into a chance-constrained programming (CCP) model and then transformed into an equivalent deterministic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this model. In MLDE, a K-means clustering method is employed to generate initial population, aiming to enhance the population's quality and diversity while mitigating the impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is established as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting the optimal scaling factor and crossover probability parameters. Numerical experiments based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in comparison to existing metaheuristic algorithms and traditional differential evolution (DE) variants. A statistical analysis experiment is also conducted to further confirm the superiority of MLDE.

关键词： Vessel scheduling Machine learning Stochastic sailing times Chance-constrained programming adaptive differential evolution algorithm

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