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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Yin, Jiye Wang, Yu Lv, Jiaqi Ma, Jin School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

Simultaneous localization and mapping (SLAM) is important for autonomous underwater vehicles (AUVs). An AUV can be equipped with corresponding function sensors to obtain the information of underwater environment and its own position. Affected by many factors of water, many sensors that can be used normally on the ground perform poor in the marine scenario. This paper presents a review of the underwater SLAM implemented by different sensors: monocular camera, stereo camera, single-beam sonar, multi-beam sonar, side-scan sonar and laser. © 2021 IEEE.

关键词： Autonomous underwater vehicles

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Indication Detection of Electric Energy Meter Based on YOLOv5

Indication Detection of Electric Energy Meter Based on YOLOv...

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作者： Bao-hua, Yang Kang-ming, Huang Yu, Sun Hong-jun, Guo School of Information Ensgineering Changzhou Vocational Institute of Mechatronic Technology Changzhou213164 China Department of Computer Engineering Asian University Suwon443-749 Korea Republic of Jiangsu Xinhuayu Measurement and Control Technology Co. Ltd Changzhou213164 China Jiangsu Xiangguo Automation Technology Co. Ltd Changzhou213164 China

With the continuous popularization of instruments, the inspection and testing of instruments have received more and more attention. This method is mainly divided into two links: target detection and information recognition. First, the instrument is located by OpenCV findcircuits function, and the instrument area is extracted. Then, the relevant methods of digital image processing are used for preprocessing, extracting the number information of the instrument panel, and establishing the data set. By using OpenCV to realize image edge detection, image segmentation and target area recognition, and then the deep learning methods are used are YOLOv3, YOLOv4 and YOLOv5 algorithms to realize the recognition of indications, the results show that the recall, precision, AP and F1 indicators of YOLOv5 were 95.26% and 95.89%, respectively, 96.29% and 95.77%, the above results of YOLOv5 are significantly improved compared with YOLOv3 and YOLOv4. The use of bottleneckcsp1 and bottleneckcsp2 in the neck can improve the network speed while ensuring accuracy, which shows that YOLOv5 is a very suitable real-time test method for instrument readings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Orientation Measurement for Objects with Planar Surface Based on Monocular Microscopic Vision

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International Journal of automation and computing 2020年第2期17卷 247-256页

作者： Ying Li Xi-Long Liu De Xu Da-Peng Zhang Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Control Engineering University of Chinses Academy of SciencesBeijing 101408China

Orientation measurement of objects is vital in micro *** this paper,we present a novel method based on monocular microscopic vision for 3-D orientation measurement of objects with planar *** proposed methods aim to measure the orientation of the object,which does not require calibrating the intrinsic parameters of microscopic *** our methods,the orientation of the object is firstly measured with analytical computation based on feature *** results of the analytical computation are coarse because the information about feature points is not fully *** order to improve the precision,the orientation measurement is converted into an optimization process base on the relationship between deviations in image space and in Cartesian space under microscopic *** results of the analytical computation are used as the initial values of the optimization *** optimized variables are the three rotational angles of the object and the pixel equivalent *** objective of the optimization process is to minimize the coordinates differences of the feature points on the *** precision of the orientation measurement is boosted *** and comparative results validate the effectiveness of the proposed methods.

关键词： Microscopic vision micro assembly orientation measurement optimization analytical computation

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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A Unified Planning Model for Microgrids Considering Typical Scenarios and Operational Simulations

A Unified Planning Model for Microgrids Considering Typical ...

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Power and Electrical Technology Conference (APET), Asia

作者： Mengmeng Su Jun Peng Yong Zhang Min Li Zhenwei Li Mingfei Ban North China Branch of State GRID Corporation of China Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The structure of microgrids and the models considered have a significant impact on the planning and operational outcomes of micro grids. Additionally, micro grids encompass various models of primary equipment, including wind, solar, diesel, and storage, while the demand-side response mechanisms are continually being refined. This necessitates the construction of diverse load types, thereby increasing the complexity of micro grid planning and highlighting the need for further research into microgrid planning models. This paper presents various typical mathematical models for microgrids, employs spectral clustering algorithms to obtain representative scenarios, and constructs a unified planning model for microgrids. Furthermore, it elucidates the model adjustment methods under different planning objectives and conducts case analyses of microgrid planning issues.

关键词： Analytical models Costs Asia Clustering algorithms Microgrids Mathematical models Planning Complexity theory Optimization Load modeling

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Enhancing local representation learning through global–local integration with functional connectivity for EEG-based emotion recognition

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computers in Biology and Medicine 2024年 179卷 108857-108857页

作者： Fu, Baole Yu, Xiangkun Jiang, Guijie Sun, Ninghao Liu, Yinhua School of Automation Qingdao University Qingdao266071 China College of Computer Science & Technology Qingdao University Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China Institute for Future Qingdao University Qingdao266071 China

Emotion recognition based on electroencephalogram (EEG) signals is crucial in understanding human affective states. Current research has limitations in extracting local features. The representation capabilities of local features are limited, making it difficult to comprehensively capture emotional information. In this study, a novel approach is proposed to enhance local representation learning through global–local integration with functional connectivity for EEG-based emotion recognition. By leveraging the functional connectivity of brain regions, EEG signals are divided into global embeddings that represent comprehensive brain connectivity patterns throughout the entire process and local embeddings that reflect dynamic interactions within specific brain functional networks at particular moments. Firstly, a convolutional feature extraction branch based on the residual network is designed to extract local features from the global embedding. To further improve the representation ability and accuracy of local features, a multidimensional collaborative attention (MCA) module is introduced. Secondly, the local features and patch embedded local embeddings are integrated into the feature coupling module (FCM), which utilizes hierarchical connections and enhanced cross-attention to couple region-level features, thereby enhancing local representation learning. Experimental results on three public datasets show that compared with other methods, this method improves accuracy by 4.92% on the DEAP, by 1.11% on the SEED, and by 7.76% on the SEED-IV, demonstrating its superior performance in emotion recognition tasks. © 2024 Elsevier Ltd

关键词： Emotion Recognition

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Event-Triggered Sampled-Data Iterative Learning control for Linear Time-Varying Switched Systems

Event-Triggered Sampled-Data Iterative Learning Control for ...

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Chinese automation Congress (CAC)

作者： Xin Ma Xisheng Dai Rusheng Zhou School of Automation Guangxi University of Science and Technology Liuzhou China School of Automation Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

Consider a class of continues linear time-varying switched systems and study its sampled-data iterative learning control output tracking problem under event triggering. Firstly, in order to reduce the update batch of the control input in the direction of the iteration axis, an event-triggered sampled-data iterative learning control law is designed and the event-triggered conditions are derived by the Lyapunov function method. Secondly, a fixed threshold method is proposed to accelerate the convergence of the system. Then, according to the activation intervals of the switched subsystem,triggered and untriggered are analyzed for convergence, and the sufficient conditions for consistent convergence of the system are derived by the compression mapping method. Finally, the effectiveness of the algorithm is verified by the numerical simulation.

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