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4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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A Reinforcement Learning Approach for Robust Restoration of Generators in Power System

A Reinforcement Learning Approach for Robust Restoration of ...

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Chinese Automation Congress (CAC)

作者： Huimiao Li Xiaozhou Yang Chao Zhai School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Singapore-ETH Centre CREATE Campus Future Resilient Systems Singapore

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Q-learning Power supplies Simulation Power system protection Generators Explosions Power systems

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Experimental comparison of nonlinear control strategies for vehicle speed control

Experimental comparison of nonlinear control strategies for ...

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IEEE Conference on control technology and Applications (CCTA)

作者： K.J. Hunt J. Kalkkuhl H. Fritz T.A. Johansen T. Gottsche Centre for Systems and Control Department of Mechanical Engineering University of Glasgow Glasgow UK Intelligent Systems Group FT3 AI Daimler-Benz Research Berlin Germany Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway Driver-assistance Systems Group FT3 AA Daimler-Benz Research Stuttgart Germany

This paper describes an experimental comparison of two alternative nonlinear automotive speed control systems. The first approach is based on interpolation of multiple linear controllers designed using multiple local linear models. The second approach is based upon geometric nonlinear control theory and utilises feedback linearisation. This paper focuses on the engineering aspects and experimental comparison in a test vehicle. The controllers are tested in a range of speed-profile tracking tasks, and in a disturbance rejection task (the vehicle is driven up a 10% slope). For comparison, linear PI/PID controllers are implemented.

关键词： Velocity control control systems Testing control design control theory Three-term control Gears Vehicle dynamics Diesel engines Nonlinear control systems

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Improved Context-Sensitive Transformer Model for Inland Vessel Trajectory Prediction

Improved Context-Sensitive Transformer Model for Inland Vess...

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International Conference on intelligent Transportation

作者： Kathrin Donandt Karim Böttger Dirk Söffker Institute of Ship Technology Ocean Engineering and Transport Systems (ISMT) University of Duisburg-Essen Duisburg Germany German Federal Waterways Engineering and Research Institute Karlsruhe Germany Chair of Dynamics and Control (SRS) University of Duisburg-Essen Duisburg Germany

Physics-related and model-based vessel trajectory prediction is highly accurate but requires specific knowledge of the vessel under consideration which is not always practical. Machine learning-based trajectory prediction models do not require expert knowledge, but rely on the implicit knowledge extracted from massive amounts of data. Several deep learning (DL) methods for vessel trajectory prediction have recently been suggested. The DL models developed typically only process information about the (dis)location of vessels defined with respect to a global reference system. In the context of inland navigation, this can be problematic, since without knowledge of the limited navigable space, irrealistic trajectories are likely to be determined. If spatial constraintes are introduced, e.g., by implementing an additional submodule to process map data, however, overall complexity increases. Instead of processing the vessel displacement information on the one hand and the spatial information on the other hand, the paper proposes the merging of both information. Here, fairway-related and navigation-related displacement information are used directly. In this way, the previously proposed context-sensitive Classification Transformer (CSCT) shows an improved spatial awareness. Additionally, the CSCT is adapted to assess the model uncertainty by enabling dropout during inference. This approach is trained on different inland waterways to analyze its generalizability. As the improved CSCT obtains lower prediction errors and enables to estimate the trustworthiness of each prediction, it is more suitable for safety-critical applications in inland navigation than previously developed models.

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A new B2B platform based on cloud computing

A new B2B platform based on cloud computing

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Xiong, Gang Hou, Jiachen Dong, Xisong Nyberg, Timo R. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 China Jiang Su China Sciences Intelligent Engineering Co. Ltd. Enterprise Academician Workstation 215000 China BIT Research Centre Aalto University Finland

ISBN: (纸本)9781467324007

With the fast development of Internet and its data scale, B2B (Business to Business), whose speed and high availability advantage is based on Internet, is eroding more and more market share of traditional business. In recent years, the new data processing technologies, such as cloud computing, assure the enhancement of computer's computing capacity;it has become possible for researchers to process and analyze the massive business data, including large scale customer data, and large scale commodity information. A new B2B platform frame, based on cloud computing with less running time and better response efficiency, is proposed here to promote transaction handle efficiency. By adoption of complex network theory and cloud computing technology, the new platform has been evaluated to decrease transaction handle time, and make more benefits. © 2012 IEEE.

关键词： Complex networks

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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controlled on-chip fabrication of large-scale perovskite single crystal arrays for high-performance laser and photodetector integration

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Light(Science & Applications) 2023年第4期12卷 558-569页

作者： Zhangsheng Xu Xun Han Wenqiang Wu Fangtao Li Ru Wang Hui Lu Qiuchun Lu Binghui Ge Ningyan Cheng Xiaoyi Li Guangjie Yao Hao Hong Kaihui Liu Caofeng Pan CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Technology University of Chinese Academy of SciencesBeijing 100049China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Colllege of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Center on Nanoenergy Research School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China College of Materials Science and Engineering Ocean University of ChinaQingdao 266100China State Key Laboratory for Mesoscopic Physics Frontiers Science Centre for Nano-optoelectronicsSchool of PhysicsPeking UniversityBeijing 100871China Songshan Lake Materials Laboratory Dongguan 523808China

Metal halide perovskites possess intriguing optoelectronic properties,however,the lack of precise control of on-chip fabrication of the large-scale perovskite single crystal arrays restricts its application in integrated ***,we report a space confinement and antisolvent-assisted crystallization method for the homogeneous perovskite single crystal arrays spanning 100 square centimeter *** method enables precise control over the crystal arrays,including different array shapes and resolutions with less than 10%-pixel position variation,tunable pixel dimensions from 2 to 8 pm as well as the in-plane rotation of each *** crystal pixel could serve as a high-quality whispering gallery mode(WGM)microcavity with a quality factor of 2915 and a threshold of 4.14μJ cm^(-2).Through directly on-chip fabrication on the patterned electrodes,a vertical structured photodetector array is demonstrated with stable photoswitching behavior and the capability to image the input patterns,indicating the potential application in the integrated systems of this method.

关键词： arrays crystal precise

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Passivation of degradation path enables high performance perovskite nanoplatelet lasers with high operational stability

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Photonics research 2022年第6期10卷 1440-1452页

作者： GUOHUI LI HUIHUI PI YANFU WEI BOLIN ZHOU YA GAO RONG WEN YUYING HAO HAN ZHANG BENG S.ONG YANXIA CUI College of Physics and Optoelectronics Key Laboratory of Interface Science and Engineering in Advanced MaterialsKey Laboratory of Advanced Transducers and Intelligent Control System of Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China Collaborative Innovation Centre for Optoelectronic Science and Technology Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen Key Laboratory of Micro-Nano Photonic Information TechnologyGuangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)Shenzhen UniversityShenzhen 518060China Department of Chemistry Research Centre of Excellence for Organic ElectronicsInstitute of Advanced MaterialsHong Kong Baptist UniversityKowloon TongHong KongChina

MAPbI_(3)perovskite has attracted widespread interests for developing low-cost near infrared semiconductor gain ***,it faces the instability issue under operation conditions,which remains a critical *** is found that the instability of the MAPbI_(3)nanoplatelet laser comes from the thermal-induced degradation progressing from the surface defects towards neighboring *** using PbI_(2) passivation,the defect-initiated degradation is significantly suppressed and the nanoplatelet degrades in a layer-by-layer way,enabling the MAPbI_(3)laser to sustain for 4500 s(2.7×10^(7) pulses),which is nearly three times longer than that of the nanoplatelet laser without ***,the PbI_(2) passivated MAPbI_(3)nanoplatelet laser with the nanoplatelet cavity displays a maximum quality factor up to∼7800,the highest reported for all MAPbI_(3)nanoplatelet ***,a high stability MAPbI_(3)nanoplatelet laser that can last for 8500 s(5.1×10^(7) pulses)is demonstrated based on a dual passivation strategy,by retarding the defect-initiated degradation and surface-initiated degradation *** work provides in-depth insights for understanding the operating degradation of perovskite lasers,and the dual passivation strategy paves the way for developing high stability near infrared semiconductor laser media.

关键词： stability initiated high

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Active Compliance Variable Impedance control of Lower Supernumerary Robotic Limb*

Active Compliance Variable Impedance Control of Lower Supern...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jun Huo Xinyi Wang Bo Yang Qingyang Yan Zhaofan Yuan Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems and the 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 Wuhan China China Telecom Research Institute Guangdong China China Ship Development and Design Centre Wuhan China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In human-robot systems, ensuring user safety amidst external disturbances remains paramount. An efficient variable impedance control (VIC) technique is designed to enhance human-robot interaction, particularly in scenarios with external force disturbances. By dynamically adapting impedance parameters in response to detected changes in contact force, VIC achieves heightened system flexibility. The robust stability of the VIC system is rigorously established using the Lyapunov approach, with a parameter update law tailored for a lower limb supernumerary robotic limb robot. Through simulations and experiments, the efficacy of the system in maintaining a specific safety margin is demonstrated. Moreover, the technique offers a pragmatic solution to accommodate individual variations in walking gait and cross-user disparities in interaction. The proposed method contributes to advancing the safety and adaptability of cross-user human-robot systems.

关键词： Legged locomotion Adaptation models Mechatronics Robust stability Force Human-robot interaction Safety Impedance Robots Pragmatics

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