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SSRN 2022年

作者： Shi, Qingxin Wan, Haiyang Liu, Wenxia Han, Haiteng Wang, Zhuorong Li, Fangxing State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China College of Energy & Electrical Engineering Hohai University Nanjing China Department of Electrical Engineering & Computer Science University of Tennessee KnoxvilleTN United States

Repair crews (RCs) and mobile power sources (MPSs) are emergency mobile resources for distribution system repair and restoration. In this paper, an outage management strategy (OMS) is proposed to improve the distribution system resilience against extreme weather events in both pre-disaster and post-disaster stages. The pre-disaster stage is an allocation of MPSs. Considering the uncertainty of fault location, the optimal allocation of resources is formulated as a two-stage stochastic programming. The post-disaster stage is a cooperative dispatch of RCs and MPSs, accompanied by dynamic microgrid formation. The purpose is to minimize the accumulative cost of the load reduction. The optimal repair sequence of faulted lines and scheduling of MPSs are determined in a multi-timeslot dispatch model. The proposed algorithm is validated by the case studies of the IEEE 123-bus test systems. The simulation results demonstrate that OMS can significantly enhance the distribution system resilience via an optimal utilization of mobile resources. © 2022, The Authors. All rights reserved.

关键词： Disasters

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Soybeannet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (Uav) Images

SSRN

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SSRN 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute & Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source at https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images and the labeled soybean dataset at https://***/datasets/jiajiali/uav-based-soybean-pod-images, offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, The Authors. All rights reserved.

关键词： Statistical tests

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SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images

arXiv

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arXiv 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code (https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images) and the labeled soybean dataset (https://***/datasets/jiajiali/uav-based-soybean-pod-images), offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, CC BY-NC-SA.

关键词： Statistical tests

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Microcomb-driven photonic convolutional engine

Microcomb-driven photonic convolutional engine

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Conference on Lasers and Electro-Optics (CLEO)

作者： Bowen Bai Qipeng Yang Haowen Shu Lin Chang Yichen Wu Fenghe Yang Bitao Shen Zihan Tao Weiqiang Xie Weiwei Hu John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China Zhangjiang Laboratory Shanghai China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm 2 and a record high weight co... 详细信息

ISBN: (纸本)9781957171258

关键词： Calibration Distributed feedback lasers Edge detection Optical delay lines Parallel processing Silicon modulators

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A secure blockchain-based architecture for the COVID-19 data network 5

A secure blockchain-based architecture for the COVID-19 data...

引用

5th Cyber Security in Networking Conference, CSNet 2021

作者： Al-Mohtar, Darine Daou, Amani Ramzi Madhoun, Nour El Maallawi, Rachad Lebanese University Faculty of Technology Department Communications and Computer Networks Engineering Beirut Lebanon Lebanese University Faculty of Technology Department Business Computer Beirut Lebanon EPITA Security and System Laboratory 14-16 Rue Voltaire Le Kremlin-Bicêtre94270 France Sorbonne Universite CNRS LIP6 4 place Jussieu Paris75005 France

ISBN: (纸本)9781665407229

The COVID-19 pandemic has impacted the world economy and mainly all activities where social distancing cannot be respected. In order to control this pandemic, screening tests such as PCR have become essential. For example, in the case of a trip, the traveler must carry out a PCR test within 72 hours before his departure and if he is not a carrier of the COVID-19, he can therefore travel by presenting, during check-in and boarding, the negative result sheet to the agent. The latter will then verify the presented sheet by trusting: (a) the medical biology laboratory, (b) the credibility of the traveler for not having changed the PCR result from 'positive to negative'. Therefore, this confidence and this verification are made without being based on any mechanism of security and integrity, despite the great importance of the PCR test results to control the COVID-19 pandemic. Consequently, we propose in this paper a blockchain-based decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner. © 2021 IEEE.

关键词： Smart contract

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Modeling Fine-Grained Relations in Dynamic Space-Time Graphs for Video-Based Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Huang, Changqin Jiang, Fan Han, Zhongmei Huang, Xiaodi Wang, Shijin Zhu, Yanlai Jiang, Yunliang Hu, Bin Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Jinhua321004 China Charles Sturt University School of Computing Mathematics and Engineering AlburyNSW2640 Australia State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Hangzhou Hikvision Digital Technology Co. Ltd Education Business Department Hangzhou310051 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Beijing Institute of Technology School of Medical Technology Beijing10081 China

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39 k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39 k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes. © 2010-2012 IEEE.

关键词： Graphic methods

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Cryptanalysis and Improvement of a Chaotic Map-Control-Based and the Plain Image-Related Cryptosystem

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computers, Materials & Continua 2019年第8期61卷 687-699页

作者： Bin Lu Fenlin Liu Xin Ge Zhenyu Li China National Digital Switching System Engineering Technology Research Center ZhengzhouChina Department of Computer Science University of YorkDeramor LaneHeslingtonUK

Due to the characteristics of chaotic systems,different cryptosystems based on chaos have been proposed to satisfy the security of multimedia data.A plain image-related chaotic algorithm is proposed by Luo et *** high speed and *** weaknesses of the cryptosystem are studied in this *** is found that the important secret key information is leaked because an important parameter can be obtained after an inverse operation in the last step of the cryptosystems without secret ***,the value zero is processed improperly in quantification *** on the weaknesses,chosen plaintext attack on the cryptosystem is proposed,by which,an important parameter,equivalent to secret key,can be calculated with a specific chosen plain *** the obtained parameter,the plain image of any ciphered image,encrypted by the cryptosystem,can be ***,an improvement is proposed to solve the problems after modifying the quantification *** is from the experiments that chosen plaintext attack is valid and improved algorithm possesses better performance.

关键词： Multimedia cryptosystem cryptanalysis chaos chosen plaintext attack

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Radiative forcing reduced by early twenty-first century increase in land albedo

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Nature 2025年第8065期641卷 1162-1171页

作者： Zhengyang Hou Liqiang Zhang Jingjing Peng Giovanni Forzieri Aolin Jia Zhiqiang Xiao Ying Qu Jintai Lin Duoying Ji Zidong Zhu Xin Yao Shuwen Peng Lanpu Zhao Wenjie Fan Zhaocong Wu Hao Geng Qihao Wang Chenghu Zhou Suhong Liu Liangpei Zhang State Key Laboratory of Remote Sensing and Digital Earth Faculty of Geographical Science Beijing Normal University Beijing China. State Key Laboratory of Remote Sensing and Digital Earth Faculty of Geographical Science Beijing Normal University Beijing China. zhanglq@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. zhanglq@***. Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies (CISESS) University of Maryland College Park MD USA. Department of Civil and Environmental Engineering University of Florence Florence Italy. Remote Sensing and Natural Resource Modeling Department ERIN Luxembourg Institute of Science and Technology Belvaux Luxembourg. Laboratory for Climate and Ocean-Atmosphere Studies Department of Atmospheric and Oceanic Sciences School of Physics Peking University Beijing China. linjt@***. Institute of Carbon Neutrality Peking University Beijing China. linjt@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. School of Earth and Space Sciences Peking University Beijing China. State Key Laboratory of Resources and Environmental Information System Institute of Geographical Science and Natural Resources Chinese Academy of Sciences Beijing China. zhouch@***. Aerospace Information Research Institute Henan Academy of Sciences Zhengzhou China. zlp62@***.

Surface albedo greatly affects how much energy the Earth absorbs. Intensive human activities and accelerated climate change have altered surface albedo across spatial and temporal scales, yet assessments of the effects of land use or land cover (LULC) and snow variations on land surface albedo are scarce at the global scale. As a result, the global land surface albedo dynamics over recent decades and their corresponding radiative forcing to the climate system remain poorly understood. Here we quantify the individual and combined effects of snow cover dynamics, LULC conversions and non-conversion regions on albedo variations during 2001-2020 and estimate their induced radiative forcing. We show that the negative radiative forcing induced by the global land surface albedo change was -0.142 (-0.158, -0.114) W m over the past two decades. The global snow-free land surface albedo increased by 2.2% (P < 0.001), with a negative radiative forcing of -0.164 (-0.186, -0.138) W m (P < 0.001). The magnitude of this negative forcing is sevenfold larger than the positive forcing induced by snow dynamics, and equivalent to 59.9% of that caused by CO emissions from 2011 to 2019. The global radiative forcing due to albedo changes in LULC non-conversion regions is 3.9 to 8.1 times greater than that from LULC conversions. The radiative forcing induced by albedo changes highlights the important role of land surface dynamics in modulating global warming.

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

arXiv

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arXiv 2024年

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024, CC BY.

关键词： Computation offloading

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