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Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Yuxiao Wang Xingyuan Dai Kara Wang Hub Ali Fenghua Zhu State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Anhui University Hefei China

Learning-based autonomous vehicle trajectory planning methods have shown excellent performance in a variety of complex traffic scenarios. However, the existing imitation learning (IL) and reinforcement learning (RL) algorithms still have their limitations, such as poor safety and generalizability for IL, and low data efficiency for RL. To leverage their respective advantages and mitigate the limitations, this paper proposes a novel hybrid RL algorithm for autonomous vehicle planning, where IL is embedded in it to guide its exploration with expert knowledge. Different from existing approaches, we use multi-step trajectory prediction instead of behavior cloning as the IL method integrated with online RL. Through such design, we make a further step in the research about how expert demonstration can be helpful to RL. Moreover, we conduct parallel training and testing of the algorithm based on real-world driving data. Experimental results demonstrate that our proposed approach outperforms standalone IL and RL methods, and performs better than RL methods enhanced by behavior cloning.

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Air Quality controlling-Oriented Highly Efficient Method for Monitoring Particulate Matters

Air Quality Controlling-Oriented Highly Efficient Method for...

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第三十九届中国控制会议

作者： Yonghui Zhang Ke Gu Zhifang Xia Junfei Qiao Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System State Information Center

Since Particulate Matters（PMs） are closely related to people’s living and health, it has become one of the most important indicator of air quality monitoring around the world. But the existing sensor-based methods for PMs monitoring have remarkable disadvantages, such as low-density monitoring stations and high-requirement monitoring conditions. It is highly desired to devise a method that can obtain the PMs concentration at any location for the following air quality control in *** prior works indicate that the PMs concentration can be monitored by using ubiquitous photos. To further investigate such issue, we gathered 1,500 photos in big cities to establish a new AQPDCITY dataset. Experiments conducted to check nine stateof-the-art methods on this dataset show that the performance of those above methods perform poorly in the AQPDCITY *** address the above issue, we propose a new photo-based model for PMs monitoring. To be specific, we use the Support Vector Regression（SVR） to incorporate four types of 18 features to obtain a high-density PMs monitoring map. Experiments show that the newly proposed model has achieved superior performance than recently developed methods in the AQPDCITY dataset.

关键词： Particulate Matters（PMs） Monitoring AQPDCITY Dataset Feature Extrastion and Fusion

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Rehabilitation with lower limb exoskeleton robot joint load adaptive server control

Rehabilitation with lower limb exoskeleton robot joint load ...

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International Conference on Information and Automation (ICIA)

作者： Shaomin Zhang Can Wang Ying Hu Duxin Liu Ting Zhang Xinyu Wu Key Laboratory of Human-Machine-Intelligence Synergic Systems Chinese Academy ot Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Department of Mechanical and Automation The Chinese University of Hong Kong

ISBN: (纸本)9781509041039

In order to help spinal cord injury (SCI) patients to walk again, a medical robot exoskeleton have been developed to assist these patients to walk independently in SIAT, Shenzhen. The research of this article is exoskeleton robot single joint load adaptive servo control. Considering the volunteers, wearing an exoskeleton robot, joints have significant changes in load torque and the moment of inertia, so driving joints was designed based on fuzzy control. Load adaptive servo controller makes the robot in rows, counter rotating torque moment of inertia of change and change has a better adaptability, and improves the smoothness of joint movement. This method was tested and the test on SIAT exoskeletons, verify the feasibility of joint adaptive control algorithm.

关键词： Servomotors Legged locomotion Exoskeletons Torque DC motors

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Containment control Protocol Design for PDE Agents Subject to External Disturbances

Containment Control Protocol Design for PDE Agents Subject t...

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Chinese control Conference (CCC)

作者： Yining Chen School of Artificial Intelligence Tiangong University Tianjin P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples.

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Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health

Photo-Based Monitoring of Particulate Matter in the Campus: ...

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It is imperative for the students' future health to ensure the students in good physical levels. Recent years have witnessed the increasingly serious harm to student health caused by the continually growing concentration of Particulate Matters(PMs). Consequently, the task of preventing and controlling PM concentrations in the campus is eagerly required. A welldesigned model for the monitoring of PM(as the basis for PM prevention and control) has posed a big challenge. Prior works have revealed that photo-based methods are available for the monitoring of PM. Towards validating the effectiveness of existing methods for PM monitoring in the campus, we construct a novel dataset that involves 1,500 photos collected in the Beijing University of Technology. Results confirm that stated-of-the-art methods are far from ideal for the monitoring of PM in the campus. To solve the aforesaid issue, this paper further proposes a novel photo-based PM monitoring model by using the weighted average method solved by LASSO regression to fuse the above methods' outputs tested to infer the PM values. Results demonstrate the superiority of our proposed model as compared to state-of-theart methods on the large-scale AQPDBJUT dataset.

关键词： LASSO Photo-Based Monitoring of Particulate Matter in the Campus A New Strategy for Student Health

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Vibration and Noise Emitted by Dry-type Air-core Reactors Under Sine-wave Current Excitation

Vibration and Noise Emitted by Dry-type Air-core Reactors Un...

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International Conference on Electrical machines and systems (ICEMS)

作者： Jingsong Li Baojun Qu Keping Yu State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua University Beijing China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China Global Information and Telecommunication Institute Waseda University Tokyo Japan

ISBN: (纸本)9781728133997

According to magneto-mechanical strong coupled effect, structure mode is discussed by engaging finite element (FE) method and both natural frequency and modal shape for a dry-type air-core reactor (DAR) are obtained in this paper. On the basis of harmonic response analysis, electromagnetic forces under sine-wave current excitation are mapped with the structure mesh, the vibration spectrums are gained and the consequences represent that the whole structure vibration predominates in the axial direction, with less radial vibration. Referring to the test standard of reactor noise, the rules of emitted noise of the DAR are measured and comparatively analyzed at power frequency and chosen other frequencies match the sample resonant frequency and the methods of active vibration and noise reduction are put forward. Finally, the low acoustic noise emission of a prototype DAR is verified by simulation and measurement.

关键词： Vibrations Acoustic measurements Noise measurement Acoustic noise Magnetomechanical effects Resonant frequency Harmonic analysis

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Nonvolatile silicon photonics based on monolithic back-end-of-line integration phase-change-materials platform for optical neural networks

Nonvolatile silicon photonics based on monolithic back-end-o...

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2024 Applied Optics and Photonics China: AI in Optics and Photonics, AOPC 2024

作者： Xu, Kai Wei, Maoliang Tang, Bo Li, Junying Li, Lan Lin, Hongtao The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Institute of Microelectronics of the Chinese Academy of Sciences Beijing100029 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

ISBN: (数字)9781510687943

ISBN: (纸本)9781510687936

Benefiting from high parallelism and low latency, photonic integrated circuits (PICs) constructed from on-chip building blocks with diverse functions have emerged as a promising technology in the realm of optical neural networks (ONN). Tunable components, through the utilization of physical mechanisms such as thermo-optic effect and free-carrier plasma dispersion effect, structural motion like microelectromechanical systems (MEMS), or material properties including liquid crystal and two-dimensional materials, play a pivotal role in enabling reconfigurability within PICs. Among these reconfiguration schemes, chalcogenide phase change materials (PCMs) based photonic devices have attracted extensive attention owing to their high energy efficiency and integration density brought by huge refractive index contrasts and nonvolatility of PCMs. However, this nonvolatile modulation method meets difficulty in scalability since the process flow of integrating PCMs into silicon photonics is insupportable in the foundries. Here, we demonstrated a back-end-of-line (BEOL) integration platform for the monolithic integration of PCMs into silicon photonic devices without modification in standard process design kits (PDK). This is achieved by fabricating a low-loss oxide trench to expose the waveguide core at the functional area from the top dielectric layer, with assistance from a silicon nitride etch stop layer. On this basis, integrated photonic devices with stable switching performance and repeatable multi-bit storage capability have been developed, possessing the potential for crucial blocks of PICs in ONN applications that require infrequent reconfiguration, such as hardware error correction before training and data storage in pre-trained models. © 2024 SPIE.

关键词： Silicon nitride

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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Information Technology,Big Data and Artificial intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianzhi Qi Wenxing Fu Pei An Bingli Wu Jie Ma National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

关键词： Three-dimensional displays Laser radar Filtering Filtering algorithms Spatial resolution Task analysis Measurement by laser beam

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Statistical Feasibility Robust Optimization for Hydrogen-Data Center Microgrid Operation

SSRN

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SSRN 2024年

作者： Duan, Juntao Li, Yuanzheng Li, Yang Liu, Zhiwei Zhao, Yong School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The increasing processing demands of information technology (IT) workloads cause the booming of data centers (DCs) in recent years. The servers in the DC emit much heat when processing the IT workloads and thus the DC temperature rises. The power consumption when controlling the DC temperature using the air conditioning (A/C) of the DC is high, which leads to expensive power bills. To reduce the power cost, using renewable energy (RE) to power DC has raised much attention in recent years. However, the intermittency of RE and the uncertainties of outside temperature pose operational challenges. Therefore, in this paper, a data-driven statistical-feasibility-based robust rolling optimization framework for the operation of hydrogen-DC microgrid (H2-DCMG) is proposed to control the DC temperature considering these uncertainties and the intermittency of RE. In the framework, the hydrogen energy storage system (HESS) is introduced to deal with the intermittency of RE, and robust optimization (RO) is adopted to deal with the uncertainty of outside temperature. The statistical feasibility is introduced into the framework to decrease the conservatism of RO, through relaxing the DC temperature constraints without harming normal DC operations. Moreover, we propose the Statistical-Guarantee-based Vertex Link (SGVL) algorithm to construct a polyhedron uncertain set of outside temperatures that can ensure the results of dispatch are economical and satisfy statistical feasibility. The constructed polyhedron uncertain set is linear, which reduces computational complexity in solving. A case study based on the real-world temperature dataset verifies the superiority of the SGVL algorithm on the aspect of the economic cost. It further validates the effectiveness of the proposed robust optimization framework for DC temperature control. © 2024, The Authors. All rights reserved.

关键词： Air conditioning

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset

arXiv

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arXiv 2024年

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China The Hong Kong University of Science and Technology Guangzhou China Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications Southeast University Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. Datasets and codes are released on Github. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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