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2nd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2020

作者： El Fouas, C. El Manssouri, O. Hajji, B. Tina, G.M. Gagliano, A. Laboratory of Renewable Energy Embedded System and Information Processing National School of Applied Sciences Mohammed First University Oujda60000 Morocco Departments of Electric Electronics and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811562587

Hydrogen production from water electrolysis process is used worldwide as an alternative to conventional fuels. Using photovoltaic thermal system (PV/T) for electrolysis energy supply could be an attractive and innovative option. The PV/T systems are a genuine power production technology, providing simultaneously electricity and heat. In this paper, an attempt is made to investigate the possibility of hydrogen production from PV/T solar plant. Assessment of electrical and thermal performances of this plant is performed based on thermo–electrical dynamic model. Such model was developed taking into account energy balance equations and validated through an experimental data from PV/T power plant installed at the University of Catania, Italy. The results clearly show a good fitting between simulated and experimental results (RMSE% less than 7.00%, and R-squared more than 0.99). At 12 pm, the mean maximum electrical and thermal energy provided by the PV/T solar plant is found about 0.84 kWh/m2 and 0.548 kWh/m2 respectively. In terms of hydrogen production, a considerable amount of hydrogen about 60 ml/min can be provided. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Hydrogen production

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Camouflaged Object Tracking: A Benchmark

arXiv

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arXiv 2024年

作者： Guo, Xiaoyu Zhong, Pengzhi Zhang, Hao Huang, Defeng Shao, Huikai Zhao, Qijun Li, Shuiwang The College of Computer Science and Engineering Guilin University of Technology 541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology 541004 China The School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The College of Computer Science Sichuan University Sichuan610065 China

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://***/openat25/ HIPTrack-MLS. © 2024, CC BY-NC-ND.

关键词： Object detection

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Formal Verification of Interrupt Isolation for the TrustZone-based TEE

Formal Verification of Interrupt Isolation for the TrustZone...

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Asia-Pacific Conference on Software Engineering

作者： Leping Zhang Qianying Zhang Xinyue Wang Ximeng Li Guohui Wang Zhiping Shi Yong Guan College of Information Engineering Capital Normal University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center of High Reliable Embedded System Beijing China Beijing Key Laboratory of Electronic System Reliability Technology Beijing China

ARM TrustZone is a hardware security technology commonly used to implement the Trusted Execution Environment (TEE), which is a hardware-based isolated execution environment in which security-critical software can execute without interference and is widely applied to embedded systems. Isolation of interrupts in the two security domains of TrustZone is an important security mechanism that plays a vital role in protecting the execution of the TEE. This paper introduces a formal modeling and verification approach for the interrupt isolation mechanism of the TEE based on TrustZone. We propose a model formalizing the TrustZone-based TEE system at the instruction level, which focuses on modeling system behaviors of interrupt handling and captures crucial instructions related to interrupt isolation. We formally define three types of properties for the model: correctness, safety, and information flow security. The verification results in the theorem prover Isabelle/HOL show that the model satisfies the above properties, which indicates that the interrupt isolation mechanism of TrustZone correctly enforces the isolation of interrupts and protects the TEE from being interfered with non-secure interrupts.

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Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

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IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

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Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

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SSRN 2024年

作者： Zang, Di Wei, Chenguang Zhao, Jiayi Zhang, Junqi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai200092 China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai200092 China

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established on the shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, USA, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes. © 2024, The Authors. All

关键词： Diffusion

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Risk Contagion in Interbank Lending Networks: A Multi-Agent-Based Modeling and Simulation Perspective

SSRN

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SSRN 2024年

作者： Ding, Zhijun Yan, Huanlan Chen, Yijun The Key Laboratory of Embedded System and Service Computing Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China The Department of Computer Science and Technology Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China Shanghai Artificial Intelligence Laboratory No.701 Yunjin Road Shanghai200232 China

The interbank lending network, while facilitating transactions, poses risk contagion in the banking system. To address challenges in existing research, this paper models the interbank lending network using a multi-agent approach and generates a dynamic network through a bidirectional matching mechanism(BiMM). Additionally, We divide banks into three categories based on realistic bank balance sheet data. Given the fact that banks of different categories exhibit different degrees of contagion, we propose the weighted DebtRank (W-DebtRank). Then, comprehensive simulation analysis is conducted on the risk contagion of heterogeneous banks in the dynamic network using W-DebtRank. The proposed method addresses four issues: the heterogeneity of banks, the dynamic nature of networks, the practicality of lending strategies, and the comprehensiveness of risk analysis. The model is validated using the SRISK metric and the Monte Carlo-based SYMBOL simulation model. Through multiple perspectives in simulation experiments, the risk contagion process among banks is deeply investigated in our paper. © 2024, The Authors. All rights reserved.

关键词： Computational methods

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Learning Adaptive and View-Invariant Vision Transformer with Multi-Teacher Knowledge Distillation for Real-Time UAV Tracking

arXiv

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arXiv 2024年

作者： Wu, You Li, Yongxin Liu, Mengyuan Wang, Xucheng Yang, Xiangyang Ye, Hengzhou Zeng, Dan Zhao, Qijun Li, Shuiwang College of Computer Science and Engineering Guilin University of Technology Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006 China School of Computer Science Fudan University Shanghai200082 China School of Artificial Intelligence Sun Yat-sen University Zhuhai510275 China College of Computer Science Sichuan University Chengdu610017 China

Visual tracking has made significant strides due to the adoption of transformer-based models. Most state-of-the-art trackers struggle to meet real-time processing demands on mobile platforms with constrained computing resources, particularly for real-time unmanned aerial vehicle (UAV) tracking. To achieve a better balance between performance and efficiency, we introduce AVTrack, an adaptive computation framework designed to selectively activate transformer blocks for real-time UAV tracking. The proposed Activation Module (AM) dynamically optimizes the ViT architecture by selectively engaging relevant components, thereby enhancing inference efficiency without significant compromise to tracking performance. Furthermore, to tackle the challenges posed by extreme changes in viewing angles often encountered in UAV tracking, the proposed method enhances ViTs’ effectiveness by learning view-invariant representations through mutual information (MI) maximization. Two effective design principles are proposed in the AVTrack. Building on it, we propose an improved tracker, dubbed AVTrack-MD, which introduces the novel MI maximization-based multi-teacher knowledge distillation (MD) framework. It harnesses the benefits of multiple teachers, specifically the off-the-shelf tracking models from the AVTrack, by integrating and refining their outputs, thereby guiding the learning process of the compact student network. Specifically, we maximize the MI between the softened feature representations from the multi-teacher models and the student model, leading to improved generalization and performance of the student model, particularly in noisy conditions. Extensive experiments on multiple UAV tracking benchmarks demonstrate that AVTrack-MD not only achieves performance comparable to the AVTrack baseline but also reduces model complexity, resulting in a significant 17% increase in average tracking speed. Codes is be available at https://***/wuyou3474/AVTrack. Copyright © 2024, The

关键词： Teaching

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Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

引用

SSRN 2024年

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcom

关键词： Diffusion

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

引用

Security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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PseudoSC: A Binary Approximation to Stochastic Computing within Latent Operation-Space for Ultra-Lightweight on-Edge DNNs 23

PseudoSC: A Binary Approximation to Stochastic Computing wit...

引用

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jihe Wang Zhaoqing Wang Danghui Wang School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350323481

Recently, stochastic computing (SC) is increasingly popular in constructing MAC for on-edge DNNs benefiting from its outstanding energy-efficiency, including its adequate precision and gatelevel operation. However, current SC-DNN systems always include a lot of costly SNGs/APCs for inevitably switches between binary and stochastic domains, which mortgages incongruous resources to pay the "bill" of the domain-switches and impedes highly-concurrent deployments. In this work, PseudoSC, a binary approximation to low-discrepancy SC, is proposed to totally remove the domain-switch for SNG/APC-free SC-DNNs. Its basic idea is to virtually re-arrange a couple of stochastic operands into a 2-D latent op-space, in which, original Monte Carlo sampling can be partitioned into three sub-ops, i.e., two fixed binary-ops and a fractal recursion. In theory, the recursion forms an isomorphic partition of the sampling repeated in smaller scales until the binary base-case achieved, as a result, a SC-op is well approximated only with binary-ops. Based on above theory, a multi-lane micro-architecture is designed to unroll the recursion within a few cycles and its advantages on hardware saving is verified under popular DNNs. The evaluation shows that the DNN-models with our schemes achieve 98.7% accuracy of the fixed-point implementations, which significantly outperform other SOTA methods. In addition, its reduced structure improves the power efficiency by 3.67 times on average.

关键词： stochastic computing

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