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Path planning of mobile robot based on improved DDQN

Journal of Physics: Conference Series 2021年第1期2024卷

作者： Yang yunxiao Wangjun Zhang Hualiang Dai shilong School of Computer Science Shenyang University of Chemical Technology Shenyang 110142 China. Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.

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An Analysis of the Effects of Flexibility Modification on system Frequency Regulation 2

An Analysis of the Effects of Flexibility Modification on Sy...

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2nd IEEE International Conference on Power and Energy Applications, ICPEA 2019

作者： Yuguang, Niu Ming, Du Enfu, Dong Ke, Zhou Weichun, Ge Huanhuan, Luo State Key Laboratory for Alternate Electric Power System with Renewable Energy Source North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China State Grid Liaoning Electric Power Supply CO. LTD Shenyang Liaoning Province110004 China Xi'an Thermal Power Research Institute CO. LTD Xi'an Shanxi Province710054 China

ISBN: (纸本)9781728115962

With the flexibility modification of thermal power plant, the minimum technical output of units has been reduced. Different kinds of operating modes can be chosen by operators of power grids, which will influence the frequency regulation of power grids. The mathematical model of reheater steam turbine and load frequency control (LFC) are established considering flexibility modification of thermal power plants. Two scenarios are created and simulations are carried out. The results indicate that with the increase of units investigated in power system, the frequency deviation decrease obviously in both a stand-alone control area and two interconnected control areas. The conclusions presented here provide some guidance on operation of thermal power plants in regions to reduce the amplitude of frequency oscillation. © 2019 IEEE.

关键词： Frequency response

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control-Oriented Wind Farm Wake Modeling with Near Wake Performance and LES Validation

Control-Oriented Wind Farm Wake Modeling with Near Wake Perf...

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Chinese Automation Congress (CAC)

作者： Zhen Xie Zhongwei Lin Zhenyu Chen Chuanxi Wang Qingru Cui Gengda Li Weidong Wang Daizhen Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing P. R. China Guodian New Energy Technology Research Institute Co. Ltd Beijing P. R. China CHN ENERGY (Shandong) New Energy Co. Ltd. Jinan P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

With the deepening research on wind farms, wake effects have gradually been paid attention to and used in wind farm optimization. During this development, the engineering wake models are adopted, which could be further improved in modeling accuracy. This paper adopts a control-oriented wake model to improve the modeling accuracy, especially in the near wake region. A double-Gaussian wake model is incorporated with appropriate wake deflection and combination models and a vertical wind profile function. Based on a Large-eddy simulated dataset, the proposed model is adopted and verified. The widely-used Gaussian wake model is also adopted and compared with the proposed model. Results show good agreement between the presented model and LES results, especially in the focused near wake region.

关键词： Wind turbines Computational modeling Wind farms Rotors Electron tubes Wind speed Mathematical model

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Prediction of σ54 promoters in prokaryotes based on SVM–Adaboost

Prediction of σ54 promoters in prokaryotes based on SVM–Ad...

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Chinese Automation Congress (CAC)

作者： Yongxian Fan Qingqi Zhu Chengwei Lv Xianyong Pan School of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

σ 54 promoters are responsible for transcriptional carbon and nitrogen in prokaryotes. However, it is costly and difficult by experimental identification of them, especially in the postgenomic era with avalanche of sequencing data. Thus, it is imperative to develop efficiently and rapidly computational algorithms to identify the σ 54 promoters. In this study, a novel predictor named SVM-Adaboost was developed to predict σ 54 promoters from sequences alone, it used the Adaboost algorithm as the core, and support vector machine (SVM) as weak base predictors. SVM-Adaboost integrated SVM predictors to construct a more powerful and robust ensemble predictor. In SVM-Adaboost, we used pseudo k-tuple nucleotide composition method to encode DNA sequences, and then a feature selection method was used to further select the discriminate features for subsequent classification. We strictly evaluate the SVM-Adaboost on a constructed gold-standard σ 54 promoter dataset using ten-fold cross validation 100 times, and achieved an average accuracy of 96.06%.

关键词： DNA Support vector machines Feature extraction Training Prediction algorithms Benchmark testing Computational modeling

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

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Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Hu Zhang Shuai Wang Tonglin Liu Aimin Zhou Yi Zhang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China School of Mechanical Engineering Changzhou University Changzhou China Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP.

关键词： Missiles Optimization Path planning Turning Planning Target tracking Sociology

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A Multi-label Classification Approach for ICT Fault Text Analysis

A Multi-label Classification Approach for ICT Fault Text Ana...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Qiang Zhang Xiaona Chen Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Global Energy Interconnection Research Institute Co. Ltd WeiLaiKeJiCheng Park Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728146539

ISBN: (纸本)9781728146546

The ICT customer service from State Grid has accumulated a large number of electric power fault reports and texts, which are difficult to classify and analyze because an occurrence of power fault may be triggered possibly by multiple different causes. In order to track different causes of electric power faults from historical fault texts, a multi-label based text classification approach is proposed for fault-assisted decision. Firstly, Chinese fault texts are sequentially preprocessed by word segmentation, stop words removal and feature representation. Then, we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm. The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification.

关键词： Text categorization Classification algorithms Task analysis Power systems Boosting Customer services Feature extraction

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Salient object detection with lossless feature reflection and weighted structural loss

arXiv

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arXiv 2019年

作者： Zhang, Pingping Liu, Wei Lu, Huchuan Shen, Chunhua School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Computer Science University of Adelaide AdelaideSA50005 Australia

Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Performance Comparison of Load Frequency control for Power system by ADRC Approach and PID controller

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Journal of Physics: Conference Series 2020年第1期1510卷

作者： Mohammed. A. Abdalla Guotian Yang Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing China

Load Frequency control (LFC) is the main problem of power networks which outcomes because of mismatch between real power generation and consumption as the power consumption changes every period. therefore, the frequency of the power system may not be at rated value resulting in power quality problems. Hence, the aim of LFC is to reduce the transient changes of area frequency and tie-line power transfer maintaining them at steady state. Moreover, the external disturbances and uncertainty factors, as well as parameters of the power system, causes a big problem for controller design in LFC. Therefore, this research describes a comparative consideration for Active Disturbance Rejection control (ADRC) based LFC as an operational control strategy, which requires few information from the plant model and being robust against disturbances and uncertainties. Two intelligent controllers Particle Swarm Optimization (PSOA) Algorithm, Bacterial Foraging Optimization (BFO) mechanism are used for tuning Proportional Integral Derivative (PID) controller based LFC to acquire optimal values, taking into consideration single-area power system. The simulation results exhibit that the ADRC can obtain better performance than PID tuned by BFO and PSO in terms of frequency deviation and settling time.

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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IEEE International Conference on Unmanned systems (ICUS)

作者： Kang Wang Jiajia Zhao Yang Wang Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China School of Information Engineering Nanchang University China

ISBN: (纸本)9781728137933

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection.

关键词： Object detection Detectors Head Training Feature extraction Task analysis Training data

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