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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Ming Dun Xu Zhang Huawei Cao Yuan Zhang Junying Huang Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of InforSuperbahn Nanjing China

Sparse Deep Neural Networks (SpDNNs) has gained great popularity and been widely applied in various machine learning area. Compared to traditional dense DNNs, the unpre-dictable irregularity and sparsity in the sparse weight matrices of SpDNNs make them difficult to be efficiently parallelled. Moreover, most of the recent advanced efforts to optimize SpDNNs are based on high-end GPUs like NVIDIA V100, which may not be affordable to individuals and smaller re-search groups. However, migrating the SpDNNs to those cost-efficient but resource-constrained GPUs confronts enormous challenges, including limitations in both memory and computing resources, as well as the tiresome hyper-parameter tuning in batch parallelism. In this paper, we accelerate SpDNNs on GPUs with more restricted resources through exploiting the memory and computing resources. On one hand, we design the adaptive memory-aware data partition scheme to reduce memory consumption automatically. On the other hand, we propose the Tensor core/CUDA core fusion mechanism to efficiently utilize the hetergeneous computing resources on modern GPU architecture. To the best of our knowledge, we are the first to improve the performance on SpDNNs through adaptive memory tuning and utilizing hetergeneous computing core concurrency. We compare our implementation with the state-of-art previous champions, and the results demonstrate that our work achieves the highest speedup of 1.35 x and 1.39x compared to 2022 champion S&Z on single and multiple NVIDIA Tesla T4 GPUs, respectively. What's more, our work can reach similar or even better throughput compared to 2020 champion H&P on 6 V100 GPUs with only 4 T4 GPUs.

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Improving finite-difference time-domain memory bandwidth by using block floating-point arithmetic

Improving finite-difference time-domain memory bandwidth by ...

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Telecommunications Forum (TELFOR)

作者： Stefan Pijetlovic Ivan Kastelan Milos Subotic Nebojsa Pjevalica RT-RK Institute for computer based systems Novi Sad Serbia Department of Computing and control engineering (Faculty of Technical Sciences) University of Novi Sad Novi Sad Serbia

Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage since multiple memory accesses are required per single computation. In other words, memory, or more precisely its speed, is a limiting factor for the overall performance. Existing solutions focus either on structures with great parallelism such as graphical processing units or custom designs. The former have issues with underutilized resources, while the latter require significant time and effort to achieve similar or slightly better performances. The approach taken during this research was to target the memory itself and find a way to improve the efficiency of memory usage across multiple platforms. The main contribution of the paper is using block floating-point in a novel way regarding the finite-difference time-domain method. The presented solution is a verification of earlier research, proving that the performance of this algorithm can indeed be improved using the suggested methods.

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Deep Learning based Defect Detection Algorithm for Solar Panels 5

Deep Learning based Defect Detection Algorithm for Solar Pan...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Li, Jiaqi Li, Hongxu Wu, Yifan Zhou, Hailiang Manfredi, Luigi Li, Peng Zhang, Hong Hebei Province Yanshan University Department of Key Lab of Industrial Computer Control Engineering Qinhuangdao066004 China University of Alberta Department of Computing Science EdmontonABT6G 2R3 Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. China United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350307320

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots. © 2023 IEEE.

关键词： Solar panels

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One solution of a Distributed System intended for communication between ADAS and IVI Systems

One solution of a Distributed System intended for communicat...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Vuk Čabrilo Dušan Kenjić Radomir Zlatković Marija Antić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia RT-RK Institute of Computer Based Systems Novi Sad Serbia

Current trends of digitalization and automation in the automotive industry have increased the complexity of computer clusters in different domains of vehicular ecosystems, which are required to support new features. This has led to redundancy in resource usage in specific use cases, resulting in increased production costs and manufacturer suggested retail price (MSRP) markups for the finalized products. The average retail price of a new vehicle in the United States increased by 13.4% between 2015 and 2020. Considering the global chip shortage of 2020 and current global inflation, the average price of a new vehicle in the United States reached $49,500 in 2023 [1]. With the electrification of current automotive platforms underway, as well as the move to centralized system architectures, new approaches to inter-domain communication are expected. This paper proposes a solution of a distributed AUTOSAR Adaptive system that can establish communication with the In-Vehicle Infotainment (IVI) domain. The proof of concept implemented in a controlled environment proves viable for inter-domain communication, as it provides good scalability and adaptability to different scenarios. The use of a distributed approach facilitates the applicability of distributed system principles, allowing for the implementation of a stable system.

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Learning to Learn Transferable Generative Attack for Person Re-Identification

arXiv

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arXiv 2024年

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan The College of Electrical and Information Engineering Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha China The College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha China

Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based cross-model&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 21.5% and 11.3% on mean mAP drop rate, respectively. The code of MTGA will be released after the paper is accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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Decentralized Goal Function-Based control of DGSs in Microgrid Applications

Decentralized Goal Function-Based Control of DGSs in Microgr...

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Power Tech Conference

作者： Ivana Isakov Milan Rapaić Marko Vekić Ivan Todorović Department of Power Electronic and Telecommunication Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

control structures governing distributed generation sources (DGSs), utilized in microgrids, have to provide several important functionalities and generally secure stable network operation. The large majority of these control structures are based either on droop (or droop-derived) or other, more advanced, control paradigms. The former are overly simplistic and necessitate control layerization and many extensions for the overall control structure to be applicable in the general case. The latter are either too complex or application-specific, rendering them unfit to be adopted as a general tool for controlling DGSs in microgrids. The control scheme derived in this paper is based on a goal function that simultaneously secures safe and stable operation, respecting voltage amplitude and frequency limits, and precise active power exchange. As such, the proposed control scheme integrates what is traditionally called primary and secondary control of microgrids and enables reliable peer-to-peer power exchange. The control scheme was tested using simulation tools and together with the simulation results, a performance analysis is provided.

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FREPA: An Automated and Formal Approach to Requirement Modeling and Analysis in Aircraft control Domain

arXiv

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arXiv 2023年

作者： Feng, Jincao Miao, Weikai Zheng, Hanyue Huang, Yihao Li, Jianwen Wang, Zheng Su, Ting Gu, Bin Pu, Geguang Yang, Mengfei He, Jifeng East China Normal University China Shanghai Key Lab of Trustworthy Computing China Beijing Institute of Control Engineering China Shanghai Trusted Industrial Control Platform Co. Ltd China China Academy of Space Technology China

Formal methods are promising for modeling and analyzing system requirements. However, applying formal methods to large-scale industrial projects is a remaining challenge. The industrial engineers are suffering from the lack of automated engineering methodologies to effectively conduct precise requirement models, and rigorously validate and verify (V&V) the generated models. To tackle this challenge, in this paper, we present a systematic engineering approach, named Formal Requirement engineering Platform in Aircraft (FREPA), for formal requirement modeling and V&V in the aerospace and aviation control domains. FREPA is an outcome of the seamless collaboration between the academy and industry over the last eight years. The main contributions of this paper include 1) an automated and systematic engineering approach FREPA to construct requirement models, validate and verify systems in the aerospace and aviation control domain, 2) a domain-specific modeling language AASRDL to describe the formal specification, and 3) a practical FREPA-based tool AeroReq which has been used by our industry partners. We have successfully adopted FREPA to seven real aerospace gesture control and two aviation engine control systems. The experimental results show that FREPA and the corresponding tool AeroReq significantly facilitate formal modeling and V&V in the industry. Moreover, we also discuss the experiences and lessons gained from using FREPA in aerospace and aviation projects. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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Modality Unified Attack for Omni-Modality Person Re-Identification

arXiv

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arXiv 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan the College of Electrical and Information Engineering Hu nan University National Engineering Research Center of Robot Vi sual Perception and Control Technology Hunan Changsha China the College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intel ligent Computing and Language Information Processing Hunan Changsha China

Deep learning based person re-identification (reid) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality reid models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omni-modality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Intelligent Autonomous-Robot control for Medical Applications

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Computers, Materials & Continua 2021年第8期68卷 2189-2203页

作者： Rihem Farkh Haykel Marouani Khaled Al Jaloud Saad Alhuwaimel Mohammad Tabrez Quasim Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a *** has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected *** COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a *** artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic *** officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent *** propose an advanced controller for a service robot to be used in *** type of robot is deployed to deliver food and dispense medications to individual *** autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its *** criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

关键词： Autonomous medic robots PID control neural network control system real-time implementation navigation environment differential drive system

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