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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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Speech-Based Alzheimer's Disease Classification System with Noise-Resilient Features Optimization

Speech-Based Alzheimer's Disease Classification System with ...

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Artificial Intelligence and Cognitive Science (AICS), Irish Conference on

作者： Virender Kadyan Puneet Bawa Mohd Mujtaba Akhtar Muskaan Singh Speech and Language Research Centre University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Cognitive Analytics Research Lab Intelligent Systems Research Centre School of Computing Ulster University UK

Alzheimer's disease is a severe neurological disorder having a major influence on a substantial portion of the popu-lation. The prompt detection of this condition is crucial, and speech analysis may play a crucial role in facilitating efficient treatment and care. The main aim of this research has been to investigate the significance of timely identification of speech signal abnormalities associated with Alzheimer's disease in order to provide effective therapy interventions and improve disease man-agement. The study used the Mel Frequency Cepstral Coefficients (MFCC) framework, a well recognized technique for feature extraction known for its versatility across several domains. This research introduces an innovative approach that utilizes both individuals diagnosed with dementia and control participants to detect two unique types of cognitive impairment via the analysis of speech signals. The approach used in this work involves the extraction of acoustic properties from pre-processed speech data obtained from the Pitt Corpus of Dementia Bank. This is achieved by using several feature sets, which include a combination of MFCC, prosodic features, and statistical features. This study examines the attributes of optimum feature optimization in actual and noise-enhanced speech environments using machine learning techniques. The integration of MFCC,Statistical and prosodic features has shown remarkable outcomes, exhibiting a superior accuracy rate of 98.3%. This surpasses the performance of other feature combinations when using the Random Forest classifier.

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Implicit smoothed particle hydrodynamics model for simulating incompressible fluid-elastic coupling

Implicit smoothed particle hydrodynamics model for simulatin...

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作者： Wang, Xiaokun Wang, Tiancheng Wang, Jiamin Xu, Yanrui Ban, Xiaojuan Huang, Houbin Zhu, Zhihong Chang, Jian Zhang, Jian Jun School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing China National Centre for Computer Animation Bournemouth University Poole United Kingdom Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Chinese PLA General Hospital Beijing China Hainan Hospital of Chinese PLA General Hospital Hainan Sanya China

Fluid simulation has been one of the most critical topics in computer graphics for its capacity to produce visually realistic effects. The intricacy of fluid simulation manifests most with interacting dynamic elements. The coupling for such scenarios has always been challenging to manage due to the numerical instability arising from the coupling boundary between different elements. Therefore, we propose an implicit smoothed particle hydrodynamics fluid-elastic coupling approach to reduce the instability issue for fluid-fluid, fluid-elastic, and elastic-elastic coupling circumstances. By deriving the relationship between the universal pressure field with the incompressible attribute of the fluid, we apply the number density scheme to solve the pressure Poisson equation for both fluid and elastic material to avoid the density error for multi-material coupling and conserve the non-penetration condition for elastic objects interacting with fluid particles. Experiments show that our method can effectively handle the multiphase fluids simulation with elastic objects under various physical properties. © 2023 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Poisson equation

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An Improved Task Planning Method for Unmanned Swarms with Coupled Physical and Logical Constraints

An Improved Task Planning Method for Unmanned Swarms with Co...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Xiubin Chen Lei Zhang Fang Xu Weiran Yao School of Astronautics Harbin Institude of Technology Harbin China Wuhan 2nd Ship Design and Research Institute Wuhan China Marine Military Intelligence Innovation Institute CSSC Systems Engineering Research Institute Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path planning. Conflict-based search method is used to address path planning with physical constraints. A genetic algorithm is employed to solve multi-traveling salesman allocation problem. A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic algorithm. The experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps.

关键词： Dictionaries Search methods Estimation Path planning Data models Planning Resource management Optimization Genetic algorithms Convergence

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