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Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO systems

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tugfe Ai, Bo School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Department of Computer Science KTH Royal Institute of Technology KistaSE-16440 Sweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems are characterized by having many more access points (APs) than user equipments (UEs). A key challenge is to determine which APs should serve which UEs. Previous work has tackled this combinatorial problem heuristically. This paper proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to jointly optimize the association and LSFD. We formulate a group sparsity problem and then solve it using a proximal algorithm with block-coordinate descent. Numerical results show that sparse LSFD achieves almost the same spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency since the processing and signaling are reduced. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

Distributed event-triggered aggregative optimization with applications to price-based energy management

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arXiv 2023年

作者： Cai, Xin Xiao, Feng Wei, Bo Wang, Aiping State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical Engineering Xinjiang University Urumqi830017 China School of Mathematics and Physics North China Electric Power University Beijing102206 China

This paper studies a distributed continuous-time aggregative optimization problem, which is a fundamental problem in the price-based energy management. The objective of the distributed aggregative optimization is to minimize the sum of local objective functions, which have a specific expression that relies on agents’ own decisions and the aggregation of all agents’ decisions. To solve the problem, a novel distributed continuous-time algorithm is proposed by combining gradient dynamics with a dynamic average consensus estimator in a two-time scale. The exponential convergence of the proposed algorithm is established under the assumption of a convex global cost function by virtue of the stability theory of singular perturbation systems. Motivated by practical applications, the implementation of the continuous-time algorithm with event-triggered communication is investigated. Simulations on the price-based energy management of distributed energy resources are given to illustrate the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy management

Data-Driven Modeling & Analysis of Dynamic Wake for Wind Farm Control: A Comparison Study

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Data-Driven Modeling & Analysis of Dynamic Wake for Wind Far...

Chinese Automation Congress (CAC)

作者： Zhenyu Chen Bart M Doekemeijer Zhongwei Lin Zhen Xie Zongming Si Jizhen Liu Jan-Willem van Wingerden State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing China Delft Center for Systems and Control (DCSC) Faculty of Mechanical Maritime and Materials Engineering (3mE) Delft University of Technology The Netherlands CHN ENERGY (Shandong) New Energy Co. Ltd Jinan PR China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective flows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics' consistency is maintained.

关键词： Wind turbines Wind farms Analytical models Mathematical model Computational modeling Solid modeling Wind speed

Risk constrained battery energy storage planning in active distribution networks

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Risk constrained battery energy storage planning in active d...

International Conference on Power system Technology (POWERCON)

作者： Yongxi Zhang Fengji Luo Ke Meng Zhao Yang Dong Hongming Yang K.P. Wong School of Electrical and Information Engineering The University of Sydney Sydney NSW Australia State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University China School of Civil Engineering University of Sydney Sydney NSW Australia International College Changsha University of Science and Technology Changsha China School of Electrical Electronics and Computer Engineering University of Western Australia Perth WA Australia

This paper proposes a stochastic planning framework for the battery energy storage system (BESS) with abundant distributed renewable energy sources. The uncertainties of wind power, solar power, and load demand variations are modeled through the scenario analysis. The risks of the planner in the uncertain environment is also considered. The conditional value at risk (CVaR) theory is introduced into the objective model to model the risk. A series of case studies are conducted to validate the efficiency of the proposed method.

关键词： Stochastic processes Renewable energy sources Batteries Planning Optimization Mathematical model

Nonlinear Virtual Inertia Control of WTGs for Enhancing Primary Frequency Response and Suppressing Drive-Train Torsional Oscillations

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arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

Comparison of Duty Cycle Measurement Techniques of 802.11b/g in the Frequency and Time Domain

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Comparison of Duty Cycle Measurement Techniques of 802.11b/g...

IEEE International Instrumentation and Measurement Technology Conference

作者： Nickolas J. LaSorte Dan Bloom Samer Rajab Sina Asadallahi Hazem H. Refai Ruiqiang Zhang Wei He Electrical and Computer Engineering University of Oklahoma Tulsa OK USA State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing 400030 China

ISBN: (纸本)9781467346214

Medical device manufacturers are evermore including wireless communication in their medical devices. Because most utilize common wireless standards such as ZigBee and Bluetooth that operate in the unlicensed 2.4 GHz industrial, scientific, and medical (ISM) band, the Food and Drug Administration (FDA) is asking medical device manufacturers to test for wireless coexistence. Coexistence among wireless devices is dependent on three factors, namely frequency, space, and time. Specific to time, the probability of coexistence in the 2.4 GHz ISM depends on channel occupancy (or duty cycle) of the interfering wireless network--the main source being WiFi (802.11b/g) for ZigBee and Bluetooth. This paper reports on the implementation of two duty-cycle measurement systems for 802.11b/g utilizing a National Instruments 5663E vector signal analyzer. Measurement techniques were designed in the frequency and time domain, and then compared. Simulations of 802.11b/g duty cycles were also executed and compared with experimental measurements.

关键词： Coexistence 802.11b 802.11g Duty Cycle Channel Characterization Typical Environment

Analysis of the Importance of Fresh Air system to Indoor Environment of Green Building and Healthy Building

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IOP Conference Series: Earth and Environmental Science 2020年第3期546卷

作者： Chen Xiaoyi Niu Runping Liu Hua Yu Kai Beijing University of Civil Engineering and Architecture Beijing 100044 China State Key Laboratory of Air-conditioning Equipment and System Energy Conservation Guangdong Key Laboratory of Refrigeration Equipment and Energy Conservation Technology Zhuhai Guangdong 519070 China

As an important place for people to live, study and work, the building indoor environment has gradually become the focus of people's attention. The fresh air system plays an important role in improving indoor environmental health problems. This paper discusses the control requirements of green buildings and healthy buildings on the indoor environment. Through the real-time detection of the indoor environment temperature, relative humidity, CO2 concentration, PM2.5 concentration, air flow speed and fresh air volume of the building, the operation process of the fresh air system is analyzed. The impact on the indoor environment, expounds the importance of the fresh air system to the indoor environment, and provides a reference for the improvement of the indoor environment of green buildings and healthy buildings.

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers