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18th International Forum of Digital Multimedia Communication, IFTC 2021

作者： Gong, Yafei Peng, Chenchen Liu, Jing Zhou, Chengxu Liu, Hongyan Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China Faculty of Science Beijing University of Technology Beijing China

ISBN: (纸本)9789811922657

In recent years, computer vision applied in industrial application scenarios has been attracting attention. A lot of work has been made to present approaches based on visual perception, ensuring the safety during the processes of industrial production. However, much less effort has been done to assess the perceptual quality of corrupted industrial images. In this paper, we construct an Industrial Scene Image Database (ISID), which contains 3000 distorted images generated through applying different levels of distortion types to each of the 50 source images. Then, the subjective experiment is carried out to gather the subjective scores in a well-controlled laboratory environment. Finally, we perform comparison experiments on ISID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Can Digital intelligence and Cyber-Physical-Social systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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Research on conducted emi characteristics of sic mosfet considering temperature effect

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Progress In Electromagnetics Research C 2021年 112卷 69-82页

作者： Du, Mingxing Bian, Weiguo Wang, Hongbin Dai, Qiqi Ouyang, Ziwei Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin300384 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China

—The junction temperature change of SiC MOSFET will change its switching process, and then affect the electromagnetic interference (EMI) characteristics of the system where the device is located and the safe operation of the surrounding equipment. Therefore, it is of great significance to research the temperature dependence of its EMI characteristics. In this paper, a buck converter composed of SiC MOSFET is taken as the research object to study the temperature variation characteristics of the conducted EMI spectrum during the switching process. Combined with the specific circuit connection form of the buck converter, the coupling paths of the conducted EMI are determined, and then the influence mechanisms of temperature change on the differential mode (DM) interference and common mode (CM) interference are analyzed. The theoretical analysis and experimental results show that the DM interference of the buck converter composed of SiC MOSFET increases with the increase of temperature, and the CM interference is almost unaffected by temperature. When the working temperature increases from 25◦C to 145◦C, the peak value of DM voltage increases by 6.7 dBµV, and the peak value of CM voltage changes less than 1.4 dBµV. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Temperature distribution

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Pan, Qingtao Qiao, Wenhao Lou, Jingjiao Ji, Bing Li, Shuo School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) for SSMIS. Specifically, 1) a Dual Contrastive Learning (DCL) is designed to improve cross-modal semantic consistency by capturing intrinsic representations within each modality and semantic correlations across modalities. 2) To encourage the learning of multiple semantic correspondences, a Semantic Similarity-Supervision strategy (SSS) is proposed and injected into each contrastive learning process in DCL, supervising semantic similarity via the distribution-based uncertainty levels. Furthermore, a novel VLM-based SSMIS network is designed to compensate for the quality deficiencies of pseudo-labels. It utilizes the pretrained VLM to generate text prompt guided supervision information, refining the pseudo label for better consistency regularization. Experimental results demonstrate that our DuSSS achieves outstanding performance with Dice of 82.52%, 74.61% and 78.03% on three public datasets (QaTa-COV19, BM-Seg and MoNuSeg). Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Modular design of adaptive controller for strict-feedback stochastic nonlinear systems with uncertain Wiener noise

Modular design of adaptive controller for strict-feedback st...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jun Wang Tao Cai Yu Kang Key Laboratory of Machine Vision and Intelligence Control Technology Hefei University Hefei China Department of Automation University of Science and Technology Hefei China

In this paper, a modular approach is proposed for a class of strict-feedback stochastic nonlinear systems with uncertain Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and input-to-state stable(ISS) controller of global stabilization in probability are designed to guarantee that the system states are bounded and has adaptive stabilization while the covariance of Wiener noises is uncertain. According to Swapping technique, we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed.

关键词： Programmable control Adaptive control Nonlinear control systems control systems Stochastic systems Stochastic resonance Nonlinear systems Backstepping Filters Parametric statistics

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Modular design of adaptive tracking for a class of stochastic nonlinear systems

Modular design of adaptive tracking for a class of stochasti...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

In this paper, a modular design approach of adaptive tracking is proposed for parameter-strict-feedback stochastic nonlinear systems with standard Wiener noises and constant unknown parameters. Both the adaptive backstepping procedure and input-to-state stable (ISS) controller of global stabilization in probability are designed separately to ensure the output-feedback tracking can be achieved. According to swapping technique, we develop two filters and convert dynamic parametric models into a static one to which the gradient update law is chosen. The transient performance shows the tracking error is bounded.

关键词： Stochastic systems Nonlinear systems Adaptive control control systems Nonlinear control systems Programmable control Automatic control Robust control Error correction Sliding mode control

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NONLINEAR STEAM GENERATOR FEEDWATER system IDENTIFICATION BASED ON WAVELET NEURAL NETWORK 10

NONLINEAR STEAM GENERATOR FEEDWATER SYSTEM IDENTIFICATION BA...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wang, Hongrui Yuan, Mingzhe Zhang, Pengbin Xing, Kunpeng Guangzhou Institute of Industrial Intelligence Guangzhou China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (纸本)9781837242672

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability. © The Institution of Engineering & Technology 2024.

关键词： Nuclear power plants

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Motion capture data reveal the important role of waist flexibility in human gait

Motion capture data reveal the important role of waist flexi...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Daoxiong Gong Long Fu Guoyu Zuo Yuqian Du Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China College of Electronic Information and Control Engineering

ISBN: (纸本)9781479987313

In order to realize the human-like locomotion of humanoid, it is crucially important to study the gait patterns as well as stance control strategies of human walking from the human motion capture data so as to transfer them to humanoid. In this paper, we use a specially designed wearable motion capture system, which can simultaneously capture the motion of the lower limbs in the joint space, the stance of the pelvis and the back in the Descartes space, and the forces exerted on the sole of foot, to capture the motion data of subjects; and the captured walking data reveal some interesting gait patterns of human, in which the role of waist flexibility in human gait is remarkable. Thus we suggest that we should pay attention to the study of the mechanism of waist flexibility in human gait as well as the method of transferring the waist flexibility to humanoid.

关键词： Legged locomotion Joints Pelvis Foot Humanoid robots Sensors

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Chalcogenide integrated nanophotonics and its potential for quantum photonics

Chalcogenide integrated nanophotonics and its potential for ...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Lei, Kunhao Xu, Kai Bao, Kangjian Weng, Jiawei 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 The 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

Due to the high optical nonlinearity and nonvolatile phase-changing properties, chalcogenide integrated nanophotonics are emerging for entangling photon generation and nonvolatile quantum states operation. We will rev... 详细信息

ISBN: (纸本)9798350379266

关键词： Silicon photonics

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