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8th Renewable Power Generation Conference, RPG 2019

作者： Hu, You Wang, Jiajun Liu, Bin Li, Zhen Liu, Xiangdong School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781839531255

The grid-connected three-phase inverter is an indispensable part of the microgrid. The droop control is widely used in parallel inverters for high reliability and flexibility. The output impedance of droop controlled inverters plays an vital role in its stability. However, the sequence impedance model of grid-connected inverter is currently based on the grid side converter with a single current loop control exdluding the low-frequency dynamics similar in doubly fed induction generators. Both the droop control and voltage loop control greatly affect the stability of inverter, specifically in the low frequency response. This paper proposes a sequence impedance model of inverter, which considers the droop control and voltage and current dual closed-loop control using the harmonic linearization method in the frequency domain. The accuracy of the model is verified through comparing the measurement impedance value in simulation with the impedance value calculated in the sequence impedance model. The influence of control method on system impedance is verified by showing the difference between the single loop controlled inverter and droop control inverter. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Electric inverters

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A CNN-ELM Compound Fault Diagnosis Method Based on Joint Distribution Modification

A CNN-ELM Compound Fault Diagnosis Method Based on Joint Dis...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Jiechao Dong Liping Yan Yuanqing Xia Jinhui Zhang Xianghua Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems Shandong China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

In the industrial field, compound faults often occur on rolling bearings and it's difficult to diagnose them correctly. To solve this problem, this article proposes a CNN-ELM compound fault diagnosis method based on joint distribution modification. Firstly, considering the complementarity and coupling of data from multiple sensors, a data input trick of multi-sensor data connected in parallel is designed. Secondly, due to the discrepancy of distribution between the compound fault data features and the single fault data features, the marginal distribution matrix and the posterior distribution matrix are used to modify the CNN-ELM network, so that the network can extract more reliable data features for fault diagnosis. Finally, referring to the categories and criteria of bearing damage proposed by Paderborn University, the label code is defined. The corresponding data set is used to verify the proposed algorithm. Experimental results show that the algorithm can accurately obtain detailed fault information such as fault location, fault type, and fault severity.

关键词： Fault diagnosis Rolling bearings Materials reliability Feature extraction Sensor systems Sensors Compounds

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A Unified Framework for Finite-/Fixed-Time Synchronization of Memristor Chaotic systems and Its Application in Image Encryption

Research Square

引用

Research Square 2021年

作者： Wang, Leimin Jiang, Shan Ge, Ming-Feng Hu, Junhao China University of Geosciences China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China College of Mathematics and Statistics South-Central University for Nationalities Wuhan430074 China

This paper proposes a sliding-mode-based unified control framework to solve the synchronization problem of memristor chaotic systems. Both finite- and fixed-time synchronization of the memristor chaotic systems can be obtained in the uniform framework. According to the Lyapunov stability and finite-time stability theories, we demonstrate that the trajectories of error system reach the presented sliding-mode surface and converge to the origin along the surface in a finite/fixed time. Moreover, an image encryption algorithm is developed based on the presented control framework. Finally, the numerical simulations and the statistical performance analyses are discussed to illustrate the correctness of synchronization results, the effectiveness of the proposed encryption algorithm, and its potential applications in the scope of secure communication. © 2021, CC BY.

关键词： Chaotic systems

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FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion

arXiv

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arXiv 2023年

作者： Teng, Siyu Li, Luxi Li, Yuchen Hu, Xuemin Li, Lingxi Ai, Yunfeng Chen, Long Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computer Science BNU-HKBU United International College Zhuhai China The School of Artificial Intelligence Hubei University Wuhan China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research. Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Federated meta-learning for fraudulent credit card detection 29

Federated meta-learning for fraudulent credit card detection

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zheng, Wenbo Yan, Lan Gou, Chao Wang, Fei-Yue School of Software Engineering Xi'an Jiaotong University China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China School of Intelligent Systems Engineering Sun Yat-sen University China School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as Federated Meta-Learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locally-computed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Crime

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Image retrieval based on convolutional neural network and linear discriminate analysis 2

Image retrieval based on convolutional neural network and li...

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2019 2nd International Conference on Advanced Materials, intelligent Manufacturing and automation, AMIMA 2019

作者： Xia, Wei Hu, Cong Cheng, Xianghui Chen, Peng Jin, Xing School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval. © Published under licence by IOP Publishing Ltd.

关键词： Search engines

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A threshold segmentation method for non-uniform illumination image based on brightness equalization 2

A threshold segmentation method for non-uniform illumination...

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2019 2nd International Conference on Advanced Materials, intelligent Manufacturing and automation, AMIMA 2019

作者： Guo, Hui Chen, Peng Huang, Shun Hu, Cong Zhang, Lijun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

In the process of image acquisition, non-uniform illumination images are common due to poor lighting, surface reflection, or a combination of these two factors. In order to improve the quality of image segmentation, an image threshold segmentation method is proposed for non-uniform illumination images. First, the brightness of different regions in the image is compensated to make the brightness background of the whole image consistent. Second, the gradation histogram of the processed image is obtained and simulated into Gaussian distribution curve, then the inflection points of the curve are calculated. Finally, the two inflection points are set as thresholds to segment the image after brightness equalization. This method eliminates the influence of non-uniform illumination to a certain extent and gets a better segmentation effect. Experiments were carried out on images containing several different types of non-uniform illumination. The results demonstrate that the proposed method outperforms the compared enhancement algorithms in threshold segmentation. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Dynamic Modeling for Dielectric Elastomer Actuators Based on LSTM Deep Neural Network

Dynamic Modeling for Dielectric Elastomer Actuators Based on...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Huai Xiao Jundong Wu Wenjun Ye Yawu Wang School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan Hubei China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

This paper proposes a dynamic model for dielectric elastomer actuators (DEAs) based on the long-short term memory (LSTM) deep neural network. The fabrication of the DEA and the framework of the experimental platform are introduced firstly. The behaviors of the DEA are analyzed through several sets of experiments, which shows the DEA has obvious memory behavior (i.e., the hysteresis behavior and creep behavior), where the hysteresis behavior is a symmetry and rate-dependence. Considering that the traditional neural network is difficult to describe the memory property, the LSTM deep neural network is constructed as the dynamic model of the DEA. Then, such neural network is trained according to the experimental data. Finally, the comparation results of the experimental data and the model output verify the effectiveness as well as the generalization ability of the dynamic model.

关键词： Mathematical model Neural networks Hysteresis Actuators Logic gates Soft robotics

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An Easy-implemented Optimization Method of Trajectory Planning Based on Polynomial Interpolation

An Easy-implemented Optimization Method of Trajectory Planni...

引用

第40届中国控制会议

作者： Guangrong Chen Ming Ma Sheng Guo Bowen Hou Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Firstly,the forward kinematics and inverse kinematics of a five axis manipulator are ***,an easyimplemented optimization method of trajectory planning based on seventh order polynomial interpolation is *** proposed optimization method can capture the time optimal trajectory,while the actuators specifications including speed,acceleration of motor can be guaranteed as ***,simulation results validate the effectiveness and efficiency of proposed optimization *** research provides an insight for the application of trajectory optimization on the controller with low computing capability and real-time requirement.

关键词： Trajectory planning Optimization method Polynomial interpolation Manipulator

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