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arXiv 2023年

作者： Lu, Zhichao Ding, Chuntao Juefei-Xu, Felix Boddeti, Vishnu Naresh Wang, Shangguang Yang, Yun School of Software Engineering Sun Yat-sen University Zhuhai519082 China School of Computer and Information Technology Beijing Jiaotong University Beijing China Meta AI New YorkNY10001 United States New York University New YorkNY10012 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Deploying high-performance vision transformer (ViT) models on ubiquitous Internet of Things (IoT) devices to provide high-quality vision services will revolutionize the way we live, work, and interact with the world. Due to the contradiction between the limited resources of IoT devices and resource-intensive ViT models, the use of cloud servers to assist ViT model training has become mainstream. However, due to the larger number of parameters and floating-point operations (FLOPs) of the existing ViT models, the model parameters transmitted by cloud servers are large and difficult to run on resource-constrained IoT devices. To this end, this paper proposes a transmission-friendly ViT model, TFormer, for deployment on resource-constrained IoT devices with the assistance of a cloud server. The high performance and small number of model parameters and FLOPs of TFormer are attributed to the proposed hybrid layer and the proposed partially connected feed-forward network (PCS-FFN). The hybrid layer consists of nonlearnable modules and a pointwise convolution, which can obtain multitype and multiscale features with only a few parameters and FLOPs to improve the TFormer performance. The PCS-FFN adopts group convolution to reduce the number of parameters. The key idea of this paper is to propose TFormer with few model parameters and FLOPs to facilitate applications running on resource-constrained IoT devices to benefit from the high performance of the ViT models. Experimental results on the ImageNet-1K, MS COCO, and ADE20K datasets for image classification, object detection, and semantic segmentation tasks demonstrate that the proposed model outperforms other state-of-the-art models. Specifically, TFormer-S achieves 5% higher accuracy on ImageNet-1K than ResNet18 with 1.4× fewer parameters and FLOPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Real-time and ultrafast optical pulse quantization based on slicing the supercontinuum

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Chinese Optics Letters 2021年第8期19卷 69-74页

作者： Ya Guo Qiang Cai Zhiwei Jia Bingjie Xu Zhensen Gao Qianwu Zhang Ruonan Zhang Adonis Bogris K.Alan Shore Yuncai Wang Pu Li Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Science and Technology on Communication Security Laboratory Institute of Southwestern CommunicationChengdu 610041China Department of Informatics and Computer Engineering University of West AtticaAthens 12243Greece School of Electronic Engineering Bangor UniversityWales LL571UTUnited Kingdom

All-optical analog-to-digital conversion is a paramount issue in modern science. How to implement real-time and ultrafast quantization to optical pulses with different intensities in an all-optical domain is a central problem. Here, we report a realtime demonstration of an all-optical quantization scheme based on slicing the supercontinuum in a nonlinear fiber. In comparison with previous schemes through off-line analysis of the power of different optical spectral components in the supercontinuum, this, to the best of our knowledge, is the first demonstration of such functionality online in the time ***, the extinction ratio among the quantized outputs can exceed 10 d B, which further confirms the feasibility of the proposed quantization scheme. The current 3 bit resolution in the proof-of-principle experiment is limited by the current experimental condition, but it can be expected to be greatly enhanced through improving both the spectral width of the generated supercontinuum and the number of filtering channels used.

关键词： all-optical analog-to-digital conversion photonic quantization all-optical signal processing supercontinuum generation

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Applying Predictive Machine Learning Algorithms to Petroleum Refining Processes as Part of intelligent Automation

Applying Predictive Machine Learning Algorithms to Petroleum...

引用

Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ivan S. Nekrasov Vadim S. Tynchenko Vladimir B. Bukhtoyarov Vera A. Kachaeva Kirill A. Bashmur Aleksandra E. Sinitskaya Laboratory of Biofuel Composition Siberian Federal Univercity Krasnoyarsk Russian Federation dept. of Technological Machines and Equipment of Oil and Gas Complex Siberian Federal Univercity Krasnoyarsk Russian Federation Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

intelligent automation is a term that can be applied to the more complex field of workflow automation, consisting of robotic workplace automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to improve efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training datasets, technical development, infrastructure, and expertise. But the potential benefits in terms of new ideas and financial development can increase significantly. The development of mechanized oil production in recent years has been accompanied by significant achievements in the field of digitalization. Machine learning, as an important element of digitalization, can successfully solve many production problems. The paper describes the application of some machine learning algorithms for solving the problem of classifying and predicting failures of hydrocracking process equipment that occur during oil refining and diesel fuel production. The application of random forest, principal component analysis and hyperparameter tuning methods is considered. The effectiveness of their application is compared.

关键词： Radio frequency intelligent automation Machine learning algorithms Biological system modeling Oils Refining Production

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Convergence analysis of nonconvex distributed stochastic zeroth-order coordinate method

arXiv

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arXiv 2021年

作者： Zhang, Shengjun Dong, Yunlong Xie, Dong Yao, Lisha Bailey, Colleen P. Fu, Shengli OSCAR Laboratory Department of Electrical Engineering University of North Texas DentonTX76207 United States School of Artificial Intelligence and Automation The MOE Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates with an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of O(√p/√T) for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery

arXiv

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arXiv 2022年

作者： Lu, Ming Fang, Leyuan Li, Muxing Zhang, Bob Zhang, Yi Ghamisi, Pedram College of Electrical and Information Engineering Hunan University Changsha410082 China State Key Laboratory of Integrated Services Networks Xidian University Xian710126 China Peng Cheng Laboratory Shenzhen518000 China College of Engineering & Computer Science Australian National University ACT2601 Australia Department of Computer and Information Science University of Macau 999078 China College of Computer Science Sichuan University Chengdu610065 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria

The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level. Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet). Compared with pixel-level labels, point labels are much easier to obtain, but they will lose much information. In this paper, we take advantage of the similarity between the adjacent pixels of a local water-body, and propose a neighbor sampler to resample remote sensing images. Then, the sampled images are sent to the network for feature aggregation. In addition, we use an improved recursive training algorithm to further improve the extraction accuracy, making the water boundary more natural. Furthermore, our method utilizes neighboring features instead of global or local features to learn more representative features. The experimental results show that the proposed NFANet method not only outperforms other studied weakly supervised approaches, but also obtains similar results as the state-of-the-art ones. © 2022, CC BY.

关键词： Semantic Segmentation

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A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 307-315页

作者： Guangyu Xiong Xiuqin Shang Gang Xiong Timo R.Nyberg IEEE the Silk Road Business School Sanya University the Department of Industrial Engineering and Management School of ScienceAalto University the State Key Laboratory for Management and Controlof Complex Systems Institute of AutomationChinese Academy of Sciences(CAS) Qingdao Academy of Intelligent Industries the Cloud Computing Center CAS the Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationCAS the School of Science at Aalto University

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing *** on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

关键词： Lean approach non-manufacturing process optimized order handling process removing wastes value chain win-win scenario

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Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China

arXiv

引用

arXiv 2022年

作者： Sabour, Sahand Zhang, Wen Xiao, Xiyao Zhang, Yuwei Zheng, Yinhe Wen, Jiaxin Zhao, Jialu Huang, Minlie The CoAI Group Dcst Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Psychology Beijing Normal University Beijing100875 China Beijing Lingxin Intelligent Technology CO. Ltd Block D Yousheng Building Haidian District Beijing100083 China Center for Counseling Psychological Development Guidance Center Tsinghua University Beijing100084 China

The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availability and reduce the relative costs of mental health support. The provided support can be divided into two main types: cognitive and emotional support. Existing work on this topic mainly focuses on constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles. Such agents operate based on pre-defined templates and exercises to provide cognitive support. However, research on emotional support using such agents is limited. In addition, most of the constructed agents operate in English, highlighting the importance of conducting such studies in China. In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress. Emohaa is a conversational agent that provides cognitive support through CBT-based exercises and guided conversations. It also emotionally supports users by enabling them to vent their desired emotional problems. The study included 134 participants, split into three groups: Emohaa (CBT-based), Emohaa (Full), and control. Experimental results demonstrated that compared to the control group, participants who used Emohaa experienced considerably more significant improvements in symptoms of mental distress. We also found that adding the emotional support agent had a complementary effect on such improvements, mainly depression and insomnia. Based on the obtained results and participants' satisfaction with the platform, we concluded that Emohaa is a practical and effective tool for reducing mental distress. © 2022, CC BY.

关键词： Health

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state Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on state Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-state Estimation (IES-SE) and more established Electric Power System-state Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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Reputation-based Distributed Filtering Over Sensor Networks Subject to Stochastic Nonlinearity and Network-Induced Quantization

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IEEE Transactions on Network science and Engineering 2025年

作者： Jia, Chaoqing Wang, Zidong Hu, Jun Dong, Hongli Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In sensor networks, due to inevitable sensor faults, malfunctions, or deliberate attacks, sensors may transmit erroneous, inaccurate, or misleading data, thereby degrading overall system performance. To address this issue, an effective approach is to assign reputation scores to sensors based on their trustworthiness, historical performance, or reliability. In this paper, the reputation-based distributed filtering (RBDF) problem is considered for a class of stochastic nonlinear systems over sensor networks with network-induced quantization. A reputation mechanism is employed to mitigate the adverse effects caused by noisy, faulty, or malicious sensors. Specifically, reputations are allocated by each sensor to the data received from its neighbors, ensuring that abnormal data are assigned smaller reputation values and may even be discarded. For the first time, a recursive RBDF algorithm is proposed, wherein an upper bound of the filtering error covariance (UBFEC) is derived by solving two matrix equations. Subsequently, the filter gain is determined by minimizing the trace of UBFEC at each step. Furthermore, a sufficient condition is presented to ensure the uniform boundedness of the filtering error dynamics. Finally, a simulation example is provided to verify the feasibility and validity of the developed RBDF algorithm. © 2013 IEEE.

关键词： Digital arithmetic

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Effect of Near-Surface Winds on the Measurement of Soil Carbon Fluxes by Closed Air Chambers

SSRN

引用

SSRN 2022年

作者： Jiang, Junjie Hu, Junguo Xu, Xiaojun Li, Yongfu Sun, Qisong Yin, Wenjie School of Mathematics and Computer Science Zhejiang A&F University Zhejiang Hangzhou311300 China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Zhejiang A&F University Zhejiang Hangzhou311300 China Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province Zhejiang A&F University Lin’an Zhejiang 311300 China State Key Laboratory of Subtropical Silviculture Zhejiang A&F University Hangzhou311300 China School of Environmental and Resources Science Zhejiang A&F University Hangzhou311300 China

The measurement of soil carbon fluxes is important to the study of global climate change issues and current monitoring methods are still based on closed gas chambers which isolate the wind pumping effect caused by near-surface winds in the measurements, leading to biased values. Therefore, this paper will investigate the effect of near-surface winds on the monitored fluxes in the chamber. Through the designed flux reference system, the carbon flux was quantified with different concentrations of CO2, and the monitoring performance of the closed chamber was studied. Wavelet coherence was used to analyse the response relationship between near-surface winds and soil gas, and was combined with a flux calculation model to explore the relevant factors influencing the bias produced by the gas chamber measurements. The data indicate that at near-surface wind speeds greater than 0.8 m s-1, the enhancement of gas transport was significant and led to a further increase in the deviation values of the carbon fluxes monitored by the gas chamber. The monitoring error of the flow chamber(NSF) increased from 7% to 30% in soils with low carbon content, whereas the error did not vary significantly (3%~7%) in soils with high CO2 concentrations. The flux measurement bias of the non-flow chamber(NSNF) was positively correlated with the soil carbon content, with the measurement error expanding by 16% to 24% with increasing soil CO2 concentration. In addition, in a windless environment, the exponential model had an error of 9.8%(Exp), whereas the linear model reached 18.7%(Lin). The estimation errors of both models were positively correlated with the time of a single monitoring event and the wind-induced coefficient Dw. Therefore, flux calculation models should be improved in environments with wind disturbances to reduce the effect of wind on measured values. This will help improve the accuracy of ecosystem carbon budgets. © 2022, The Authors. All rights reserved.

关键词： Soils

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