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An efficient data aggregation scheme with local differential privacy in smart grid

Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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Fire Detection Based on Improved Feature Fusion 13

Fire Detection Based on Improved Feature Fusion

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Cao, Xinrong Wu, Jincai Li, Zuoyong College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Sichuang Technology Co. Ltd. Fuzhou350100 China

ISBN: (纸本)9798350319156

Fire is a serious disaster that endangers life and property worldwide. Accurate and prompt fire detection can provide early warning information for firefighting efforts. Deep learning-based object detection algorithms have proven to be useful in the field of fire detection. However, fire detection is a particularly challenging task, due to the low-resolution of fire images and the presence of blurring and deformation in the targets. Existing fire detection methods are limited by poor detection accuracy and slow speed in complex fire scenarios. To address these challenges, we propose a fire detection algorithm based on improved YOLOv5s. Firstly, the attention module is embedded into the network structure. We introduce this attention module into the last layer of the backbone to enhance feature extraction ability. Secondly, we modify the Neck structure to fully fuse different feature maps. Through fusing extra low-level features from the backbone, it utilizes various receptive fields to enhance the expression of feature. Thirdly, SIoU is introduced to achieve faster convergence during the training. SIoU take into account the direction of the mismatch to redefine penalty indicators. The experimental results show that our proposed algorithm outperforms commonly used models. Even compared to specific fire detectors, our model exhibits certain advantages. © 2023 IEEE.

关键词： Fires

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Modeling and Analysis of Induction Machine Considering the Effects of Saturation

Modeling and Analysis of Induction Machine Considering the E...

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Circuit, Systems and Communication (ICCSC), International Conference on

作者： Es-Semyhy Mustapha Ba-Razzouk Abdellfattah El Haroussi Mustapha Department of Applied Physics Mathematics Computer and Engineering Sciences Laboratory (MISI) Systems Analysis and Information Processing Team (ASTI) Settat Morocco

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

This paper focuses on the modeling of an induction machine, with a specific emphasis on saturation of the main path. The model, formulated in a two-axis system, considers the saturation effect to accurately represent the behavior of the machine. Two scenarios are studied namely nominal and high-level saturation cases. All parameters of the machine are treated as variables, and a comprehensive discussion is provided regarding the effects of certain parameters, shedding light on their influence on the induction machine’s dynamic performance. Simulation work is carried out using the MATLAB/Simulink environment, providing valuable insights into the system’s dynamic behavior under different saturation conditions.

关键词： Analytical models Torque Stators Observers Motors Mathematical models Induction machines

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Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning 13

Predicting the State of Crane Spreader for Steel Bar Deliver...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Zibin Xu, Zhezhuang Chen, Jian Cui, Chen Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou University Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd Fujian Sanming353000 China

ISBN: (纸本)9798350315196

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery. © 2023 IEEE.

关键词： Forecasting

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Offline Reinforcement Learning via Sequence Modeling for Vision-Based Robotic Grasping 13

Offline Reinforcement Learning via Sequence Modeling for Vis...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Zeng, Sheng Sun, Xiao Chen, Kang Huang, Weibing Chen, Yi Chen, Dan Xu, Zhezhuang College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350315196

High cost of environmental interaction and low data efficiency limit the development of reinforcement learning in robotic grasping. This paper proposes an end-to-end robotic grasping method based on offline reinforcement learning via sequence modeling. It considers the most recent n-step history to assist the agent in making decisions, where a predictive model learns to directly predict actions from raw image inputs. The experimental results show that our method can achieve higher grasping success rate with less training data than traditional reinforcement learning algorithms in offline setting. © 2023 IEEE.

关键词： Reinforcement learning

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Communications Based on DRL 100

UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Commu...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hu, Jiawei Ju, Ying Wang, Haoyu Liu, Lei Pei, Qingqi Shee, Y.G. Zhu, Xiaojie Wu, Celimuge University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xi'an University of Posts Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang43000 Malaysia King Abdullah University of Science and Technology Department of Computer Electrical and Mathematical Sciences and Engineering Thuwal239556900 Saudi Arabia The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798331517786

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks. © 2024 IEEE.

关键词： active reconfigurable intelligent surface deep reinforcement learning quality of service UAV trajectory

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Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video

Disentangling Spatio-Temporal Knowledge for Weakly Supervise...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Guiqiu Liao Matjaz Jogan Sai Koushik Eric Eaton Daniel A. Hashimoto Department of Surgery PCASO Laboratory University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania Department of Computer and Information Science University of Pennsylvania

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net.

关键词： Accuracy Weak supervision Surgery Object segmentation Object detection Streaming media Real-time systems Timing Object recognition Transient analysis

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Video-Based Jump Height Estimation in Athletic Performance Testing

Video-Based Jump Height Estimation in Athletic Performance T...

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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Filtering Balun Design using Hairpin-Ring Resonator and SSPP Metamaterial with Wide Notched band and Wide Stopband

Filtering Balun Design using Hairpin-Ring Resonator and SSPP...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Qi, Huanhuan Liu, Haiwen Huang, Mo School of Information and Communications Engineering Xi' An Jiaotong University Xi'an China Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350336535

In this paper, a novel filtering balun based on microstrip-to-slotline transition is proposed. Utilizing the inherent out-of-phase electric field distributions on the adjacent grounds of slotline, the balun function is realized. In order to realize the wide notched-band and stopband performance for radio astronomical applications, spoof surface plasmon polariton (SSPP) metamaterial and multi-mode hairpin ring resonators (HRRs) are adopted. With these novel structures loaded, the proposed filtering balun exhibits a wide band from 1GHz to 6 GHz. The notched band is from 3.2 GHz to 4.4 GHz, within the notched band, the rejection level is better than 15 dB. Besides, the out-of-band suppression is below 35 dB in the wide frequency range from 6 GHz to 8 GHz. © 2023 IEEE.

关键词： Metamaterials

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