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Autonomous ship safe navigation using smoothing A* Algorithm

Open Cybernetics and Systemics Journal 2014年第1期8卷 72-76页

作者： Ma, Yong Gan, Langxiong Zheng, Yuanzhou Zhang, Jing School of Navigation Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Institute of Systems Engineering Huazhong University of Science and Technology Wuhan China School of Business Chutian College Huazhong Agricultural Business University Wuhan China

Using reasonable and efficient autonomous technology for path planning can effectively improve the safety of ship navigation and can reduce human errors of maritime accidents. The planned path by A* algorithm is flawed with broken lines, many turning points and numerous transitional frequencies under the environment modeled by grid method. Smoothing A* algorithm is adopted to delete the removable points which prolong the planned path length. After all the realities of ship navigation are taken into account, smoothing A* algorithm is applied to resolve safe navigation problem for the ship. With the proposed smoothing A* algorithm, simulation results show that the conflict-free path can be planned for ships under different circumstances with static and dynamic obstacles and abide by the COLREGS. © Ma et al.;Licensee Bentham Open.

关键词： Ships

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Distributed control of heterogeneous linear multi-agent systems by intermittent event-triggered control

Distributed control of heterogeneous linear multi-agent syst...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yan Lei Yan-Wu Wang Hua Chen Xiao-Kang Liu Huazhong University of Science and Technology Wuhan Hubei CN Mathematics and Physics Department Hohai Universtiy Changzhou China Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

ISBN: (纸本)9781538629024

In this paper, the leader-following output consensus problem of heterogeneous linear systems(MASs) is addressed. A distributed intermittent event-triggered protocol is designed, where the information interaction among agents only occurs under a local triggering condition within some certain time intervals. As a result, it saves the energy for communication and reduces the number of control updates. In aid of the Lyapunov functional method, some sufficient conditions are derived to achieve the leader-following consensus and the result is further extended to the formation control problem. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.

关键词： Multi-agent systems Linear systems Laplace equations Eigenvalues and eigenfunctions Riccati equations Decentralized control Automation

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Short-Term Residential Load Forecasting Via Pooling-Ensemble Model with Smoothing Clustering

SSRN

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SSRN 2023年

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this paper proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole shortterm load forecasting framework in this paper contains three steps. Specifically and firstly, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Secondly, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Thirdly, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

arXiv

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arXiv 2022年

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Tencent Shanghai China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well . In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2022, CC BY.

关键词： Deep neural networks

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An Electronic Travel Aid based on multi-sensor fusion using extended Kalman filter

An Electronic Travel Aid based on multi-sensor fusion using ...

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World Congress on intelligent control and Automation (WCICA)

作者： Yanqi Lu Jian Huang Wenxia Xu Chunjing Tao Xitai Wang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China R&D department National Research Center for Rehabilitation Technical Aids Beijing China

Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.

关键词： Acoustics Sensor systems Mercury (metals) Magnetic sensors Vectors Acceleration

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Joint Load Prediction of Multiple Buildings Using Multi-Task Learning with Selected-Shared-Private Mechanism

SSRN

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SSRN 2023年

作者： Cao, Minghui Xiao, Jiang-Wen Fang, Hongliang Li, Yuan-Zheng Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Short-term load forecasting for residential buildings is of great significance to ensure the safe and economic operation of power grid. However, most of the existing prediction methods focus on the temporal characteristics of individual buildings and ignore the importance of spatial characteristics between different buildings. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with the selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy considering the auto-correlation and time dependency is proposed to build input set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, this paper designs a novel selected-shared-private mechanism based on the cross-correlation, which can selectively utilize information from related tasks while focusing on private features to improve prediction accuracy. The proposed model is compared with other methods on the public dataset, and the results demonstrate the superiority of this model in load prediction for multiple buildings. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Automatic Monitoring of Driver's Physiological Parameters Based on Microarray Camera

Automatic Monitoring of Driver's Physiological Parameters Ba...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Jiancheng Zou Zhengzheng Li Peizhou Yan Institute of Image Processing and Pattern Recognition North China University of Technology Beijing Key Laboratory of Urban Rod Traffic Intelligent Control Technology North China University of Technology Shijingshan District Beijing China

Driver's physical and mental states are very important factors affecting the driving states. Traffic accidents are occurred by accompanying abnormal physiological parameters. So how to monitor automatically driver's physiological parameters is an important problem. A real-time driver's physiological parameters monitoring method based on microarray camera is proposed in this paper. Firstly, we use a microarray camera to acquire videos in real time without contact. Secondly, the videos are registered and fused into a high quality video. Thirdly, deep learning framework is used to capture driver's facial data quickly. Then based on the principle of imaging photoplethysmography (IPPG) algorithm, physiological parameters such as heart rate (HR) and oxygen saturation (SpO 2 ) are monitored in real time. Results of expression analysis can further support the physiological parameters monitoring results. The experimental results show that the method proposed in this paper can monitor the physiological parameters of drivers in real time and accurately, and it is hopeful to play an active role in improving traffic safety.

关键词： Biomedical monitoring Monitoring Cameras Videos Heart rate Real-time systems

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Transfer Learning for Motor imagery Based Brain-Computer Interfaces: A Complete Pipeline

arXiv

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arXiv 2020年

作者： Wu, Dongrui Jiang, Xue Peng, Ruimin Kong, Wanzeng Huang, Jian Zeng, Zhigang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou310018 China

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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A Uniform Solution to Common Algorithmic Problem by Tissue P Systems with Cell Division

A Uniform Solution to Common Algorithmic Problem by Tissue P...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Yunyun Niu Linqiang Pan Mario J. Perez-Jimenez Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Sevilla Spain

Common algorithmic problem is an optimization problem, which has the nice property that several other NP-complete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model which has two basic characters: intercellular communication and the ability of cell division. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. We here present an effective solution to the common algorithmic decision problem using a family of recognizer tissue P systems with cell division.

关键词： Manganese Polynomials Biomembranes Computer science Bismuth Radiation detectors Educational institutions

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A nonlinear sliding mode observer for the estimation of temperature distribution in the planar solid oxide fuel cell stack

A nonlinear sliding mode observer for the estimation of temp...

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第五届世界氢能技术大会(WHTC2013)

作者： Huan Cheng Xi Li Jianhua Jiang Lin Zhang Jian Li Jie Yang Department of control science and Engineering Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould Technology Huazhong University of Science and Technology Wuhan 430074 China School of Mechanical and Electronic Information China University of Geosciences Wuhan 430074 China

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