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11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2024

作者： Li, Jianwei Tian, Hongxin Liu, Shengyu Sun, Xueqing Zhou, Ronghui Zhang, Yuzhen Dembele, Bezoun Deborah Shen, Xiong Tianjin Key Lab of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin300072 China

ISBN: (纸本)9798400707063

Water condensation and thermal destruction in the powerhouse may cause a harmful effect on the equipment and the safety of workers in the pumped storage power station. To design a sound thermal and humid environment, the ventilation system of the underground powerhouse of a pumped storage power station was simulated using the airflow network model. Besides, the dynamic control of the ventilation system of the powerhouse and the consequent thermal and humid environment were simulated. We applied VENTSIM software to simulate the air flow organization, heat and humidity transport. We put forward optimization suggestions through the simulation to ensure a safe thermal and humid environment. This study implies the necessity of using a network model in the ventilation system design in pumped storage power stations. © 2024 Copyright held by the owner/author(s).

关键词： Ventilation

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Approximately Invertible Neural Network for Learned Image Compression

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IEEE Transactions on Image Processing 2025年 34卷 3041-3055页

作者： Gao, Yanbo Li, Shuai Fu, Meng Lv, Chong Yang, Zhiyuan Cai, Xun Yuan, Hui Ye, Mao Shandong University School of Software Jinan250100 China Shandong University-WeiHai Research Institute of Industrial Technology Shandong University Weihai264209 China Shandong University School of Control Science and Engineering Jinan250100 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250100 China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Learned image compression has attracted considerable interests in recent years. An analysis transform and a synthesis transform, which can be regarded as coupled transforms, are used to encode an image to latent feature and decode the feature after quantization to reconstruct the image. Inspired by the success of invertible neural networks in generative modeling, invertible modules can be used to construct the coupled analysis and synthesis transforms. Considering the noise introduced in the feature quantization invalidates the invertible process, this paper proposes an Approximately Invertible Neural Network (A-INN) framework for learned image compression. It formulates the rate-distortion optimization in lossy image compression when using INN with quantization, which differentiates from using INN for generative modelling. Generally speaking, A-INN can be used as the theoretical foundation for any INN based lossy compression method. Based on this formulation, A-INN with a progressive denoising module (PDM) is developed to effectively reduce the quantization noise in the decoding. Moreover, a Cascaded Feature Recovery Module (CFRM) is designed to learn high-dimensional feature recovery from low-dimensional ones to further reduce the noise in feature channel compression. In addition, a Frequency-enhanced Decomposition and Synthesis Module (FDSM) is developed by explicitly enhancing the high-frequency components in an image to address the loss of high-frequency information inherent in neural network based image compression, thereby enhancing the reconstructed image quality. Extensive experiments demonstrate that the proposed A-INN framework achieves better or comparable compression efficiency than the conventional image compression approach and state-of-the-art learned image compression methods. © 1992-2012 IEEE.

关键词： Image coding

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Facile low-temperature supercritical carbonization method to prepare high-loading nickel single atom catalysts for efficient photodegradation of tetracycline

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Journal of Environmental sciences 2024年第4期138卷 373-384页

作者： Han Qiao Lirong Zheng Shiwen Hu Gang Tang Hongri Suo Chongxuan Liu School of Environment Harbin Institute of TechnologyHarbin 150090China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Beijing Synchrotron Radiation Facility(BSRF) Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China

Environmental photocatalysis is a promising technology for treating antibiotics in *** this study,a supercritical carbonization method was developed to synthesize a single-atom photocatalyst with a high loading of Ni(above 5 wt.%)anchored on a carbonnitrogen-silicate substrate for the efficient photodegradation of a ubiquitous environmental contaminant of tetracycline(TC).The photocatalyst was prepared from an easily obtained metal-biopolymer-inorganic supramolecular hydrogel,followed by supercritical drying and carbonization *** low-temperature(300℃)supercritical ethanol treatment prevents the excessive structural degradation of hydrogel and greatly reduces the metal clustering and aggregation,which contributed to the high Ni *** characterizations confirmed that Ni was present at isolated sites and stabilized by Ni-N and Ni-O bonds in a Ni-(N/O)_(6)-C/SiC configuration.A 5%Ni-C-Si catalyst,which performed the best among the studied catalysts,exhibited a wide visible light response with a narrow bandgap of 1.45 eV that could efficiently and repeatedly catalyze the oxidation of TC with a conversion rate of almost 100% within 40 *** reactive species trapping experiments and electron spin resonance(ESR)tests demonstrated that the h+,and·O_(2)-were mainly responsible for TC *** TC degradation mechanism and possible reaction pathways were provided ***,this study proposed a novel strategy to synthesize a high metal loading singleatom photocatalyst that can efficiently remove TC with high concentrations,and this strategy might be extended for synthesis of other carbon-based single-atom catalysts with valuable properties.

关键词： Carbon-based single-atom catalyst Nickel Photocatalytic degradation Tetracycline Visible light

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer 6

BOAformer: Object Detection Network Based on Background-Obje...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Yang, Yong Liang, Chenbin Huang, Shuying Wang, Xiaozheng School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (纸本)9798350353174

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. Then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. The experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background. © 2024 IEEE.

关键词： Prediction models

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Distributed Event-Triggered Time-Varying Formation control of High-Order Multiagent Systems with Multiplicative Noise 43

Distributed Event-Triggered Time-Varying Formation Control o...

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43rd Chinese control Conference, CCC 2024

作者： Jia, Ruru Ni, Yuan-Hua Wang, Guangchen Nankai University College of Artificial Intelligence Tianjin300350 China Shandong University School of Control Science and Engineering Jinan250061 China

ISBN: (纸本)9789887581581

This paper discusses the distributed event-triggered time-varying formation (TVF) problem for high-order multiagent systems (MASs) with multiplicative noise on the undirected graph. Based on relative state measurements among agents, a distributed TVF protocol with multiplicative noise for MASs is proposed via event-triggered control. Then, a specific algorithm design is given and corresponding sufficient conditions are established, which shows that all agents can asymptotically converge to the target formation in the mean square sense. Finally, a numerical example is presented to prove the availability of the designed algorithm. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Undirected graphs

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Dynamic Movement Primitives Based Robot Skills Learning

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Machine Intelligence Research 2023年第3期20卷 396-407页

作者： Ling-Huan Kong Wei He Wen-Shi Chen Hui Zhang Yao-Nan Wang School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China School of Robotics and the National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint *** DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion *** addition,motions are categorized into different goals and *** is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning *** experiment test on the Baxter robot verifies the effectiveness of the proposed method.

关键词： Dynamic movement primitives(DMPs) trajectory tracking control robot learning from demonstrations neural networks(NNs) adaptive control

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Frequency Agile Strategy Design of Radar Network for Wideband Sweep Jamming via Coalition Formation Game

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 7906-7919页

作者： Liao, Maosen Xiong, Kui Zhang, Long Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100089 China School of Information and Communication Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Chengdu611731 China

This article proposes an intelligent frequency agile design strategy for radar network using coalition game theory to counter wideband sweep jamming, which is formulated as a two-stage decision-making problem. In the first stage, the entire frequency agile range is divided into several non-overlapping agile subrange, and each radar selects a frequency agile subrange to join, which is solved by the coalition formation algorithm. Then, after achieving a stable coalition partition, a distributed multi-agent reinforcement learning algorithm is developed to address the specific carrier frequency selection problem within each coalition. By combining these two stages of the frequency agile design strategy, a joint coalition formation game and distributed Q-learning algorithm (CFDQ) is proposed. Finally, numerical simulations are conducted to illustrate the superiority of the proposed algorithm over several typical frequency agile strategies. © 1965-2011 IEEE.

关键词： Jamming

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Some Remarks on Fault-Tolerant Dynamic control Allocation for Weakly Redundant Systems 32

Some Remarks on Fault-Tolerant Dynamic Control Allocation fo...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Cristofaro, Andrea Faleschini, Michelangelo Sapienza University of Rome Department of Computer Control and Management Engineering Italy School of Science and Technology Mathematics Division University of Camerino Italy

ISBN: (纸本)9798350395440

We consider the problem of control allocation for weakly redundant systems subject to actuator faults. In particular, the design of a suitable allocator will be devised with the aim of compensating for the fault effects while, at the same time, keeping the control burden as low as possible. To this goal, two approaches can be followed for the synthesis of the allocation servomechanism: direct allocation using orthogonal projection and optimization-based allocation. Some numerical examples illustrate and highlight advantages, disadvantages and limitations of both strategies. © 2024 IEEE.

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A novel Na_(1−x)K_(x)TaO_(3)perovskite microwave dielectric ceramic with high permittivity and high positive temperature coefficient

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Journal of Advanced Ceramics 2023年第11期12卷 2053-2061页

作者： Liu Tang Hongcheng Yang Enzhu Li Chaowei Zhong State Key Laboratory of Electronic Thin Films and Integrated Devices School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 610054China National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of ChinaChengdu 611731China School of New Energy and Materials Southwest Petroleum UniversityChengdu 610500China

A novel Na_(1−x)K_(x)TaO_(3)(x=0,0.025,0.05,0.075,0.1,and 0.15)ceramic with high permittivity and high positive temperature coefficient was synthesized via the conventional solid-state *** samples were determined to be pure phase orthorhombic NaTaO3 structure of space group Pmcn,and larger grain and lower porosity were observed after adding an appropriate amount of K+*** Q×f value is majored by the packing fraction and grain size,while the value ofτf is influenced by Ta–O bond *** Na_(0.95)K_(0.05)TaO_(3) ceramic possesses excellent dielectric properties ofεr=164.29,Q×f=9091 GHz(f=3.15 GHz),tanδ=3.46×10^(–4),τf=+809.52 ppm/℃,sintered at 1550℃.Compared with NaTaO_(3) ceramics,the Na_(1−x)K_(x)TaO_(3)ceramics prepared in this study demonstrate higher dielectric constants and higher positive temperature coefficients,which are promising for device miniaturization andτf compensators.

关键词： Na_(1−x)K_(x)TaO_(3)ceramics high permittivity high positive temperature coefficient perovskite

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A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Zhang, Yongquan Shang, Aomen Li, Siwei Su, Zhenzhen Ji, Hongbing School of Electronic Engineering Xidian University Xi’an710071 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

Multi-sensor multi-target tracking (MTT) with dense group clutter is facing great challenges in data fusion. One is the high computational complexity caused by the dense group clutter;another is that existing fusion algorithms do not have uniform fusion guidelines. To handle these problems, we present a novel multi-sensor MTT algorithm in this paper, which is based on the generalized labeled multi-Bernoulli (GLMB) filter and distributed deep neural networks (DNN) track fusion under the multi-sensor (MS) system, named the MS-GLMB-DNN algorithm. Firstly, a clutter pre-processing is elaborated, it is based on a CFDP (cluster by finding density peaks) algorithm to reduce the effect of the dense group clutter. Then, the GLMB filter is applied to the DNN track fusion framework for effectively fusing various local tracks. Experimental results indicate that, compared with the commonly used covariance intersection (CI) fusion algorithm, the proposed algorithm can effectively eliminate the effect of clutter on target tracking results, reduce computational complexity, and improve tracking accuracy. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Deep neural networks

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