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Modeling and simulation of charging characteristics of electric vehicle group under the mode of autonomous driving-shared travel

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IET INTELLIGENT TRANSPORT SYSTEMS 2024年第2期18卷 302-314页

作者： Zhu, Junliang Wu, Zhigang Chen, Chongchen Su, Entong South China Univ Technol Sch Elect Power Guangzhou Peoples R China South China Univ Technol Sch Elect Power Guangzhou 510641 Peoples R China

Compared with the traditional travel mode, the increasingly mature autonomous driving and shared travel technologies can lead to a higher driving utilization rate and lower car parc;however, at the same time, they will bring unknown impacts to the trans-energy system. As a new generation of vehicles, the behavior of electric vehicles will also be affected. This paper describes and models the behavior of electric vehicle group in the autonomous driving-shared travel mode in detail, and uses the multi-agent technology to establish a large-scale electric vehicle group simulation model. This model fully considers the constraints of traffic network and charging station and the influence of fuel vehicles, which can simulate the actual scene well and be used to study the charging behavior of electric vehicle group. Finally, the simulation model is used to obtain the charging load curve of the electric vehicle group under the new travel mode and analyze the influence of travel upgrade and network topology on charging load. This model fully considers the constraints of traffic network and charging station and the influence of fuel vehicles, which can simulate the actual scene well and be used to study the charging behavior of electric vehicle group. Finally, the simulation model is used to obtain the charging load curve of the electric vehicle group under the new travel mode and analyze the influence of travel upgrade on charging ***

关键词： electric vehicle charging electric vehicles

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Systematic and long-term technical validity of toxicity determination and early warning of heavy metal pollution based on an automatic water-toxicity-determination-system

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Frontiers of Environmental Science & Engineering 2024年第8期18卷 59-71页

作者： Yue Yi Baoguo Wang Xuemei Yi Fan Zha Haisen Lin Zhijun Zhou Yanhong Ge Hong Liu School of Life Science Beijing Institute of TechnologyBeijing 100081China Institute of Environmental Biology and Life Support Technology School of Biological Science and Medical EngineeringBeihang UniversityBeijing 100191China Infore Environment Technology Group Foshan 528000China Jiangmen Ecological Environment Monitoring Station Jiangmen 529000China Guangzhou Sub-branch of Guangdong Ecological and Environmental Monitoring Center Guangzhou 510000China

Water toxicity determination with electrochemically active bacteria(EAB)shows promise for providing early warnings for heavy metal pollution in ***,thus far,only idealized tests with a few types of heavy metals have been *** this study,an automatic water-toxicitydetermination system with high technical maturity was established,and the toxicological properties of common heavy metals were systematically *** results demonstrated that the common heavy metals linearly inhibited EAB currents in the range of 0.1 mg/L to 0.5 mg/*** toxicity ranking of the tested heavy metals was Pb^(2+)>Tl^(3+)>Cu^(2+)>Cd^(2+)>Zn^(2+)>Ni^(2+)>Hg^(2+)>As^(3+).The toxicity interaction mainly exhibited an antagonistic effect in binary heavy metal *** system can accurately determine surface water toxicity and rapidly monitor heavy metal pollution,with good repeatability and a long ***,this study demonstrates that EAB are capable of long-term(>60 d)surface water quality monitoring and on-site early warning of heavy metal pollution.

关键词： Biological early warning system Electrochemically active bacteria Water toxicity determination Biosensor Heavy metal pollution Early warning

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Using kin discrimination to construct synthetic microbial communities of Bacillus subtilis strains impacts the growth of black soldier fly larvae

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Insect Science 2024年第6期31卷 1943-1959页

作者： Jun-Hui Zhao Ping Cheng Yi Wang Xun Yan Zhi-Min Xu Dong-Hai Peng Guo-Hui Yu Ming-Wei Shao Key Laboratory of Green Prevention and Control on Fruits and Vegetables in South China Ministry of Agriculture and Rural AffairsGuangdong University Key Laboratory for Sustainable Control of Fruit and Vegetable Diseases and PestsInnovative Institute for Plant HealthCollege of Agriculture and BiologyZhongkai University of Agriculture and EngineeringGuangzhouChina State Key Laboratory of Agricultural Microbiology National Engineering Research Center of Microbial PesticidesCollege of Life Science and TechnologyHuazhong Agricultural UniversityWuhanChina Hubei Hongshan Laboratory WuhanChina College of Resources and Environment Zhongkai University of Agriculture and EngineeringGuangzhouChina

Using synthetic microbial communities to promote host growth is an effective ***,the construction of such communities lacks theoretical *** discrimination is an effective means by which strains can recognize themselves from non-self,and construct competitive microbial communities to produce more secondary ***,the construction of cooperative communities benefits from the widespread use of beneficial *** used kin discrimination to construct synthetic communities(SCs)comprising 13 Bacillus subtilis strains from the surface and gut of black soldier fly(BSF)*** assessed larval growth promotion in a pigeon manure system and found that the synthetic community comprising 4 strains(SC 4)had the most profound *** analyses of these 4 strains revealed that their complementary functional genes underpinned the robust functionality of the cooperative synthetic community,highlighting the importance of strain *** analyzing the bacterial composition of BSF larvae and the pigeon manure substrate,we observed that SC 4 altered the bacterial abundance in both the larval gut and pigeon *** also influenced microbial metabolic functions and co-occurrence network *** discrimination facilitates the rapid construction of synthetic *** positive effects of SC 4 on larval weight gain resulted from the functional redundancy and complementarity among the ***,SC 4 may enhance larval growth by inducing shifts in the bacterial composition of the larval gut and pigeon *** elucidated how the SC promoted larval growth by regulating bacterial composition and provided theoretical guidance for the construction of SCs.

关键词： community construction genomic analysis gut bacteria pigeon manure

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Unbiased Real-Time Traffic Sketching

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 2371-2383页

作者： Wu, Yuhan Jiang, Shiqi Xu, Yifei Dong, Siyuan Yang, Kaicheng Chen, Peiqing Yang, Tong Peking Univ Natl Key Lab Multimedia Informat Proc Beijing 100871 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

In network measurement, sliding window measurement has the advantage of providing recent and timely measurement results. Recently, sketches have become the most popular method of conducting flow-level network measurements due to their favorable trade-off between small memory overhead and high measurement accuracy. However, it remains a challenge that no current sketches are able to support unbiased estimation toward flow size measurement, which can improve the performance of tasks including network diagnoses, delay measurement and heavy hitter detection. In this article, we propose the first work that achieves unbiased flow size measurement in sliding windows, namely Unbiased Cleaning sketch (UC sketch). The key technique of the UC sketch is Unbiased Cleaning which can remove outdated keys from the sliding windows in a balanced way. Besides, we significantly reduce the variance of flow size by two optimization techniques, namely Linear Scaling and Column Randomizing. To prove the result, we conduct rigorous mathematical analysis and reasonable experiments.

关键词： Estimation Size measurement Task analysis Current measurement Arrays Real-time systems Cleaning Network measurement sketches sliding windows unbiased estimation

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Long-Term Adaptive VCG Auction Mechanism for Sustainable Federated Learning With Periodical Client Shifting

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 6060-6073页

作者： Wu, Leijie Guo, Song Hong, Zicong Liu, Yi Xu, Wenchao Zhan, Yufeng Hong Kong Polytech Univ Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Clear Water Bay Hong Kong Peoples R China Beijing Inst Technol Beijing 100811 Peoples R China

Federated Learning (FL) system needs to incentivize clients since they may be reluctant to participate in the resource consuming process. Existing incentive mechanisms fail to construct a sustainable environment for the long-term development of FL system: 1) They seldom focus on system economic properties (e.g., social welfare, individual rationality, and incentive compatibility) to guarantee client attraction. 2) Current online auction modeling methods divide the whole continual process into multiple independent rounds and solve them one-by-one, which breaks the correlation between each round. Besides, the inherent characteristics of FL system (model-agnostic and privacy-sensitive) also prevent it from the optimal strategy by precise mathematical analysis. 3) Current system modelings ignore the practical problem of periodical client shifting, which cannot adaptively update its strategy to handle system dynamics. To overcome the above challenges, this paper proposes a long-term adaptive Vickrey-Clarke-Groves (VCG) auction mechanism for FL system, which incorporate a multi-branch deep reinforcement learning (DRL) algorithm. First, VCG auction is the only one that can simultaneously guarantee all crucial economic properties. Second, we extend the economic properties to long-term forms and apply the experience-driven DRL algorithm to directly obtain long-term optimal strategy, without any prior system knowledge. Third, we reconstruct a multi-branch DRL network to accommodate periodical client shifting by adaptive decision head switching for different time periods. Finally, we theoretically prove he extended economic properties (i.e., IC) and conduct extensive experiments on several real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL system increases by 36% with a 37% reduction in payment. Besides, the multi-branch network can adaptively handle periodical client shifting on the timeline.

关键词： deep reinforcement learning incentive mechanism game theory Federated learning

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Visually security privacy medical data protection scheme using compressive sensing and improved duffing chaotic system

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PHYSICA SCRIPTA 2024年第9期99卷 095222-095222页

作者： Huang, Shufeng Jiang, Donghua Huang, Linqing Xiong, Xiaoming Tan, Beihai Cai, Shuting Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 511400 Peoples R China Guangdong Univ Technol Sch Adv Mfg Guangzhou 510006 Peoples R China

Health and medical data frequently contain sensitive patient information that must be protected. Existing visual security schemes for medical images exhibit limitations in the imperceptibility of cipher images and the performance of image reconstruction. This paper introduces and evaluates a novel approach called Visual Meaningful Image Encryption (VMIE) for securing medical images. The proposed VMIE scheme employs a chaotic system based on the Duffing equation for initial encryption. Medical images are processed and encrypted in a sparse domain. A Bidirectional Chaotic Magic Transformation (BCMT) algorithm is then applied to scramble the sparse medical images. The scrambled data undergoes compression and diffusion. An adaptive embedding strategy employing the Discrete Cosine Stockwell Transform (DCST) integrates confidential data into the host image. The performance of the proposed chaotic system is validated through theoretical analysis and numerical simulation. Simulation results, along with comparisons to existing schemes, demonstrate the efficacy of the VMIE method in enhancing visual security and its suitability for natural images. The VMIE approach presented in this paper offers a promising solution for securing medical images, effectively addressing the limitations of current visual security schemes.

关键词： medical data protection chaotic system compressive sensing visual security BCMT

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NoQSM-net: Combining Convolutional Neural Network With Numerical Optimization Algorithm for Quantitative Susceptibility Mapping Reconstruction

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IEEE ACCESS 2024年 12卷 33129-33139页

作者： Zhang, Qianqian Guo, Yihao Chen, Wufan Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China Guangdong Prov Key Lab Med Image Proc Guangzhou 510515 Peoples R China Hainan Med Univ Hainan Gen Hosp Dept Radiol Hainan Affiliated Hosp Haikou 570311 Peoples R China

In gradient echo MRI, quantitative susceptibility mapping (QSM) quantifies the magnetic susceptibility distributions of tissues, which has great potential in detecting brain diseases. However, QSM reconstruction is an ill-conditional inversion problem because of the zeros in the frequency domain of the dipole kernel. The intrinsic nature of the ill-posedness would affect the accuracy of quantifying tissue susceptibility. Recently, deep learning-based methods have been proposed to improve accuracy by suppressing the streaking artifacts. In this work, we proposed a hybrid architecture to enforce data consistency by involving numerical optimization blocks within convolutional neural networks (CNN), which aimed to reconstruct high-quality QSM images, referred to as NoQSM-net. The Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) QSM maps were used as labels for training. The performance of the proposed method was evaluated on two healthy volunteers and brain images of patients with diseases. Our experiments showed that the proposed method achieved good performance in terms of quantitative metrics and could effectively suppress artifacts in reconstructed QSM images, demonstrating its potential for future applications. For experiments on patients with multiple sclerosis (MS), the proposed method could better detect lesion regions in the results of NoQSM-net.

关键词： Image reconstruction Optimization Training Magnetic susceptibility Convolutional neural networks Neural networks Magnetic field measurement Multiple sclerosis Biomedical measurement Brain modeling Diseases Magnetic resonance imaging Quantitative susceptibility mapping dipole kernel inversion MRI numerical optimization convolutional neural network

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PromptFL Let Federated Participants Cooperatively Learn Prompts Instead of Models - Federated Learning in Age of Foundation Model

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5179-5194页

作者： Guo, Tao Guo, Song Wang, Junxiao Tang, Xueyang Xu, Wenchao Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China King Abdullah Univ Sci & Technol Div CEMSE Mecca 23955 Saudi Arabia SDAIA KAUST AI Ctr Mecca 23955 Saudi Arabia

Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training. Otherwise, FL may cost excessive training time for convergence and produce inaccurate models. In this paper, we propose a brand-new FL framework, PromptFL, that replaces the federated model training with the federated prompt training, i.e., let federated participants train prompts instead of a shared model, to simultaneously achieve the efficient global aggregation and local training on insufficient data by exploiting the power of foundation models (FM) in a distributed way. PromptFL ships an off-the-shelf FM, i.e., CLIP, to distributed clients who would cooperatively train shared soft prompts based on very few local data. Since PromptFL only needs to update the prompts instead of the whole model, both the local training and the global aggregation can be significantly accelerated. And FM trained over large scale data can provide strong adaptation capability to distributed users tasks with the trained soft prompts. We empirically analyze the PromptFL via extensive experiments, and show its superiority in terms of system feasibility, user privacy, and performance.

关键词： Training Data models Servers Frequency modulation Federated learning Adaptation models Computational modeling Edge computing prompt federated learning vision-language model

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Incremental Learning, Incremental Backdoor Threats

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第2期21卷 559-572页

作者： Jiang, Wenbo Zhang, Tianwei Qiu, Han Li, Hongwei Xu, Guowen Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Sichuan Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace BNRist Beijing 100190 Peoples R China

Class incremental learning from a pre-trained DNN model is gaining lots of popularity. Unfortunately, the pre-trained model also introduces a new attack vector, which enables an adversary to inject a backdoor into it and further compromise the downstream models learned from it. Prior works proposed backdoor attacks against the pre-trained models in the transfer learning scenario. However, they become less effective when the adversary does not have the knowledge of the downstream tasks or new data, which is more practical and considered in this paper. To this end, we design the first latent backdoor attacks against incremental learning. We propose two novel techniques, which can effectively and stealthily embed a backdoor into the pre-trained model. Such backdoor can only be activated when the pre-trained model is extended to a downstream model with incremental learning. It has a very high attack success rate, and is able to bypass existing backdoor detection approaches. Extensive experiments confirm the effectiveness of our attacks over different datasets and incremental learning methods, as well as strong robustness against state-of-the-art backdoor defense mechanisms including Neural Cleanse, Fine-Pruning and STRIP.

关键词： Data models Task analysis Computational modeling Training Adaptation models Predictive models Transfer learning Backdoor attack class incremental learning deep learning

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SiBrain: A Sparse Spatio-Temporal Parallel Neuromorphic Architecture for Accelerating Spiking Convolution Neural Networks With Low Latency

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第12期71卷 6482-6494页

作者： Chen, Yuehai Ye, Wujian Liu, Yijun Zhou, Huihui Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China

Currently, the performance of spiking neural networks (SNNs) under complicated tasks is gradually close to that of convolutional neural networks (CNNs). However, the existing neuromorphic computing hardware architectures (NCHAs) for accelerating SNNs lack effective sparse detection and cannot realize effective spatio-temporal parallel computing, resulting in high inference latency. In this paper, we propose an improved neuromorphic computing hardware architecture (called SiBrain) with high accuracy, low power and low latency, consisting of a sparse spatio-temporal parallel processing element ((STP)-T-2-PE) array for spike convolution and pooling computation and a fully-connected (FC) Core for spike FC computation. In the novel S2} TP-PE array, a (STP)-T-2 computation unit is designed by using time-step and channel parallel computation techniques to reduce the latency caused by multiple time-steps of SNN, and a sparse detection and response unit is presented based on channel-cache and block-multiplex to achieve the spike detection, response, and reuse. Combining the above kernel components, the SiBrain is built and implemented on Virtex-7 FPGA with 200MHz. The experimental results show that the SiBrain can effectively support the deployment of SCNN models with different sizes, and the deployed large-scale Spiking Visual-Geometry-Group (VGG) model can achieve the recognition accuracies of 90.25% and 66.97% on CIFAR-10 and CIFAR-100 with the power consumption of 1.5 W and the energy efficiency of 83 GSOPs/W. Compared with existing FPGA-based SNN accelerators, SiBrain has a maximum increase in inference speed of nearly 11 times and a maximum decrease in energy consumption of nearly 34 times, respectively.

关键词： Spiking convolution neural network sparse spike detection spatio-temporal parallel computing FPGA platform image recognition

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