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arXiv 2024年

作者： Dai, Xiaobing Yang, Zewen Xu, Mengtian Liu, Fangzhou Hattab, Georges Hirche, Sandra TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Robert Koch Institute Berlin Germany National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology 150001 China Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system. For continuous enhancement in predictive performance of Gaussian process model, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. Furthermore, the control performance of the proposed approach is ensured via the Lyapunov theory, based on a probabilistic guarantee for prediction error bounds. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Quantitative analysis of real-time performance and hardware requirements for edge computing platform

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Data Science and Informetrics 2021年第2期1卷 47-63页

作者： Kangkang Shi Gangyong Jia Youhuizi Li Yuyu Yin Congfeng Jiang Li Zhou School of Computer Science Hangzhou Dianzi UniversityHangzhouChina Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi UniversityHangzhouChina

For real-time edge systems such as autonomous driving,not only the correctness of task functions,but also the response and processing time of tasks should be *** the hardware selection phase of a real-time system,time series analyses must be performed on the hardware platform running real-time *** present,the common method of worst-case execution time(WCET)analysis focuses mainly on analyzing the impact of hardware platform architecture or task execution process on the task running ***,different tasks in an autopilot system have different levels of urgency,and preemption between tasks is the main factor that affects the task execution *** key problem is how to quantify the time fluctuation caused by task preemption for each subtask of the autopilot system running on a fixed hardware *** paper presents a time analysis method for a real-time application based on a queuing theory and preemptive scheduling strategy,which assigns different priorities to tasks according to their time urgency and preemptive scheduling according to task *** an experimental case study,the impact of the running time of each subtask in a real-time application with task priority preemptive scheduling is analyzed,along with the impact of changes in hardware platform performance on such real-time applications.

关键词： Autonomous Driving Queuing Theory Preemptive Scheduling Time Series Analysis

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Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model

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Neural Networks 2025年 189卷 107503页

作者： Ye, Shaoxun Di, Xiaoguang Liao, Ming Li, Ximing Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primarily arises from the loss of detailed information during the decoding process from latent space and the lack of guidance from global 3D structural information in the point cloud generation process, leading to distortions and artifacts in LiDAR scene samples. In this paper, we propose a novel LiDAR Diffusion Transformer Model that integrates Channel-Spatial Parallel Attention and Dilation Fusion Network (CSPA-DFN) with a linear cross-attention post-processing module to refine the generated LiDAR scene samples. Specifically, CSPA-DFN is designed to simultaneously emphasize detailed features across different channels and spatial locations in parallel, leveraging multi-scale dilated convolutions and channel grouping to preserve and enhance these detailed features. In order to provide global 3D structural information and balance performance and efficiency, we design a post-processing module that fuses voxelized features and range images using a linear ReLU cross-attention mechanism. Our approach is evaluated on the unconditional generation task using the KITTI-360 and nuScenes datasets, achieving the state-of-the-art results in LiDAR scene's generation quality. Furthermore, by incorporating semantic labels and camera views into the latent space, in addition to enhancing the model's semantic understanding capability for LiDAR scenes, our method also demonstrates additional performance improvements compared to previous works in terms of LiDAR scene's visual quality. The code implementation has been released on https://***/HITysx/LiDAR-Scene-Generation. © 2025 Elsevier Ltd

关键词： Photointerpretation

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Reliability Assessment of LCC-HVDC links Using Protection Devices

Reliability Assessment of LCC-HVDC links Using Protection De...

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Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), International Conference on

作者： Abdelhadi Hameurlaine Larbi Boukezzi Salah Karef Haouari Sayah Laboratory of Applied Automation & Industrial Diagnostics University of Djelfa Djelfa Algeria Materials Science and Informatics Laboratory MSIL University of Djelfa Djelfa Algeria Com Laboratory of Modeling Optimization and Simulation of Real Complex Systems University of Djelfa Djelfa Algeria Intelligent Control and Electrical Power System Laboratory University of SidiBel-Abbes SidiBel-Abbes Algeria

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

the DC/AC interaction and the presence of the high nonlinearity in the HVDC links, these problems needs to examine the behavior of the system in steady-state and transient conditions in order to define the great role of protection functions and control parameters. Furthermore, Control and protection systems are a vital part of an HVDC system to guarantee the safety, reliability, and stability of its operation. This research aim is to provide the solutions for control and protection problems that can occur in HVDC link. In this paper, we study how we mitigate this serious malfunction of commutation failure process in the operation of HVDC converters. Indeed some protection functions like the commutation failure prevention. Furthermore, HVDC transmission systems are vulnerable to DC faults and its protection becomes ever more important with the fast growth in the number of installations. Finally, the dynamics of the studied power system including the HVDC link is thoroughly investigated through various simulation scenarios.

关键词： Performance evaluation HVDC transmission Power system dynamics Power system stability Stability analysis Power system reliability Steady-state

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Research on Image Stitching Methods for Cmos Grayscale Cameras in Industrial Scenarios

SSRN

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SSRN 2024年

作者： Liu, Qi Huo, Ju Tang, Xiyu Xue, Muyao School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China National Key Laboratory of Modeling and Simulation for Complex Systems Heilongjiang Harbin150000 China School of Astronautics Harbin Institute of Technology Harbin150001 China Space Propulsion Technology Research Institute Shanghai Academy of Spaceflight Technology Shanghai201109 China

Addressing the limited field of view of CMOS grayscale cameras, complex lighting conditions, and the paucity of image features in industrial applications, this paper proposes a novel image stitching method for CMOS grayscale cameras under varying lighting conditions. It extends the camera's field of view while maintaining the interpretability of image features, thus enhancing the robustness and generalizability of image stitching across diverse lighting environments and feature-sparse settings. In the feature extraction phase, a hybrid deep feature extraction network is designed. By employing a deep learning-based approach for feature extraction, the network ensures an ample quantity of features. Building upon this, a line feature selection and reconstruction method is developed to further refine feature matching accuracy, increasing the number of matching lines under extreme lighting and feature-scarce situations, thereby enriching the image features for subsequent stitching steps. The subsequent image transformation phase introduces planar feature constraints;matching feature points and lines generate planar features, addressing alterations in the collective shape of planes common in industrial image stitching. The paper concludes with quantitative evaluation metrics for planar feature-based stitching. Experimental results validate the effectiveness and feasibility of our approach for image stitching of CMOS grayscale cameras under varied lighting conditions and in feature-deficient industrial settings. It offers a viable solution to the challenges of limited imaging fields of view faced by CMOS grayscale cameras in industrial applications. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Privacy sensitive large-margin model for face de-identification 1st

Privacy sensitive large-margin model for face de-identifica...

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Guo, Zhiqiang Liu, Huigui Kuang, Zhenzhong Nakashima, Yuta Babaguchi, Noboru Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Osaka University Osaka Japan

ISBN: (纸本)9789811576690

There is an increasing concern of face privacy protection along with the wide application of big media data and social networks due to free online data release. Although some pioneering works obtained some achievements, they are not sufficient enough for sanitizing the sensitive identity information. In this paper, we propose a generative approach to de-identify face images yet preserving the non-sensitive information for data reusability. To ensure a high privacy level, we introduce a large-margin model for the synthesized new identities by keeping a safe distance with both the input identity and existing identities. Besides, we show that our face de-identification operation follows the (Formula Presented)-differential privacy rule which can provide a rigorous privacy notion in theory. We evaluate the proposed approach using the vggface dataset and compare with several state-of-the-art methods. The results show that our approach outperforms previous solutions for effective face privacy protection while preserving the major utilities. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Privacy-preserving techniques

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A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes

arXiv

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arXiv 2024年

作者： Yu, Ting Lin, Xiaojun Wang, Shuhui Sheng, Weiguo Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Three-Dimensional (3D) dense captioning is an emerging vision-language bridging task that aims to generate multiple detailed and accurate descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning, as well as complexities in data collection and processing of 3D point cloud sources. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field, which hinders its progress. In this paper, we provide a comprehensive review of 3D dense captioning, covering task definition, architecture classification, dataset analysis, evaluation metrics, and in-depth prosperity discussions. Based on a synthesis of previous literature, we refine a standard pipeline that serves as a common paradigm for existing methods. We also introduce a clear taxonomy of existing models, summarize technologies involved in different modules, and conduct detailed experiment analysis. Instead of a chronological order introduction, we categorize the methods into different classes to facilitate exploration and analysis of the differences and connections among existing techniques. We also provide a reading guideline to assist readers with different backgrounds and purposes in reading efficiently. Furthermore, we propose a series of promising future directions for 3D dense captioning by identifying challenges and aligning them with the development of related tasks, offering valuable insights and inspiring future research in this field. Our aim is to provide a comprehensive understanding of 3D dense captioning, foster further investigations, and contribute to the development of novel applications in multimedia and related domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Data handling

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Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering

arXiv

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arXiv 2024年

作者： Yu, Ting Fu, Kunhao Wang, Shuhui Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Video Question Answering (VideoQA) represents a crucial intersection between video understanding and language processing, requiring both discriminative unimodal comprehension and sophisticated cross-modal interaction for accurate inference. Despite advancements in multi-modal pre-trained models and video-language foundation models, these systems often struggle with domain-specific VideoQA due to their generalized pre-training objectives. Addressing this gap necessitates bridging the divide between broad cross-modal knowledge and the specific inference demands of VideoQA tasks. To this end, we introduce HeurVidQA, a framework that leverages domain-specific entity-action heuristics to refine pre-trained video-language foundation models. Our approach treats these models as implicit knowledge engines, employing domain-specific entity-action prompters to direct the model’s focus toward precise cues that enhance reasoning. By delivering fine-grained heuristics, we improve the model’s ability to identify and interpret key entities and actions, thereby enhancing its reasoning capabilities. Extensive evaluations across multiple VideoQA datasets demonstrate that our method significantly outperforms existing models, underscoring the importance of integrating domain-specific knowledge into video-language models for more accurate and context-aware VideoQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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A new general formulation of a transfer-function matrices of 2D digital systems

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Australian Journal of Electrical and Electronics Engineering 2019年第2期16卷 81-85页

作者： Bouhamla, Lakhdar Adamou-Mitiche, Amel Baha Houda Mitiche, Lahcène Laboratory of Research Modeling Simulation and Optimization of Real Complex Systems University Ziane Achour Djelfa Algeria

The contribution of our paper is a new simple method for transfer-function matrices derivation in two-dimensional digital systems from Roesser’s model is given. The main idea of this method is changing the form of the transfer-function, from state space to a fraction with a polynomial numerator and denominator. Then by identity, we can derive matrices of numerator and denominator. Numerical examples are given to prove the effectiveness and the simplicity of the proposed method. © 2019, © 2019 Engineers Australia.

关键词： Transfer functions

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