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An overview on the designs of distributed observers in LTI multi-agent systems

Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Xu, Saijuan Yang, Liliang Liu, Genggeng Department of Information Engineering Fujian Business University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q-learning, the proposed global router adopts the Double Deep Q-Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router. © 2023 Saijuan Xu et al.

关键词： Reinforcement learning

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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Yb3+, Er3+ Ions Co-Doped Double Molybdate/Tungstate Composite Laser Crystals for Promising 1.5 Μm Solid State Lasers

SSRN

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SSRN 2024年

作者： Hu, Haitang Ding, Shoujun J. Wang, Miaomiao Zou, Yong Zhang, Chuancheng Ren, Hao Liu, Wenpeng School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Hefei230031 China

A series of 10 at% Yb3+, 5 at% Er3+ doped NaGd(MoxW1-xO4)2 (x = 0.5, 0.7, and 0.9) single crystals were grown by Czochralski method. The structure of the crystals was determined to be a tetragonal system with space group of I41/a, as confirmed through X-ray diffraction (XRD), Rietveld refinement and FT-IR analyses. In addition, the 1.5 μm spectral properties of the crystals were investigated, including emission, absorption and fluorescence decay characteristics. Importantly, the influence of crystal composition on the spectroscopic properties was revealed deeply. As the crystal composition changes, a significant inhomogeneous broadening of the spectral and remarkable changes in energy band Eg were observed. A maximum full width at half-maximum (FWHM) at 1535 nm was obtained to be 83 nm in (MoO4)1.0(WO4)1.0 composition crystal. However, the maximum absorption and emission cross-sections were achieved in (MoO4)1.4(WO4)0.6 component crystal with values of 1.15 × 10-20 cm2 and 1.05 × 10-20 cm2, respectively. Besides, for (MoO4)1.4(WO4)0.6 composition crystal, a positive gain cross-section is obtained at around 1535 nm for a population inversion ratio β above 0.4. The results show that double molybdate/tungstate composite crystals co-doping with Yb3+ and Er3+ are promising for 1.5 μm near-infrared lasers. © 2024, The Authors. All rights reserved.

关键词： Infrared devices

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A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

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Visible-Thermal Multiple Object Tracking: Large-scale Video dataset and Progressive Fusion Approach

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Privacy Protection Multi-copy Provable data Possession Supporting data Reliability 5th

Privacy Protection Multi-copy Provable Data Possession Suppo...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zhang, Feng Wen, Bin Yan, Yifei Zeng, Zhaowu Zhou, Wei School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819981007

With the development of technology, a massive amount of data continues to emerge, and there is still a strong demand for data storage. Outsourcing data to cloud service providers has become a common practice. Although cloud storage offers the advantage of location-independent data access and reduces burdens such as hardware and software maintenance, this service still faces some challenges in terms of privacy and data reliability. To address this issue, we present Privacy-preserving Multi-copy Provable data Possession supporting data Reliability (PMPDP-R). Our proposal ensures the integrity, reliability, and freshness of data by designing Timestamp-based Double-layer Merkle Hash Tree (TD-MHT), a novel dynamic structure for multicopy storage. Furthermore, our approach uses random masking techniques to develop unique replica blocks and safeguard privacy. We’ve also ensured data reliability by enabling PMPDP-R to locate damaged data blocks to aid recovery. The experimental analysis proves that our scheme is both practical and effective. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud storage

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Deep learning radiomics model related with genomics phenotypes for pathological complete response prediction in locally advanced rectal cancer under neoadjuvant chemoradiotherapy 14

Deep learning radiomics model related with genomics phenotyp...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Wang, Ye Pan, Zhen Lin, Lanyan Cai, Huajun Ding, Feng Tan, Chaojun Zheng, Dingyuan Xie, Yajun Guan, Guoxian The First Affiliated Hospital Fujian Medical University Department of Colorectal Surgery Fuzhou China Shengli Clinical Medical College Fujian Medical University Fujian Key Laboratory of Geriatrics Diseases Fujian Provincial Center for Geriatrics Fuzhou University Affiliated Provincial Hospital Department of Geriatric Medicine Fuzhou China Fujian Huaye Information Technology Co. Ltd China Fuzhou University of International Studies and Trade School of Big Data Key Laboratory of Data Science and Intelligent Computing Fuzhou China The First Affiliated Hospital Fujian Medical University National Regional Medical Center The First Affiliated Hospital Department of Colorectal Surgery Binhai Campus Fuzhou China

ISBN: (纸本)9798350356670

Background: Developing a treatment strategy after neoadjuvant chemoradiotherapy (nCRT) based on accurate pathological complete response (pCR) predictions is crucial for locally advanced rectal cancer (LARC).Objective: To determine the effectiveness of a radiomics model using diffusion-weighted imaging (DWI) and T2-weighted in predicting post-nCRT pCR efficacy after ***: This study enrolled 237 LARC patients who had radical surgery under nCRT between February 2020 and August 2023. Tumor tissues from 54 patients were RNA sequenced. Radiomics features from preoperative MRI data were used to develop a model for deep learning via the LASSO algorithm. ROC curves were used to assess the model performance. Following Spearman's correlation, analyses of transcriptomic data were conducted to identify the genes correlated with the DPL ***: The DPL model, incorporating 7 features and 5 machine-learning algorithms, was used to create risk assessment models. The RF model excelled in predicting pCR, achieving an AUC of 0.91, accuracy of 0.88, and F1 score of 0.89. Furthermore, catabolic processes and pathways related to immunity were associated with selected DPL ***: The modified MRI-based radiomics model shows good predictive efficacy in predicting pCR after nCRT in LARC patients. Additionally, we investigated the genomic phenotypes associated with DPL. © 2024 IEEE.

关键词： locally advanced rectal cancer machine learning neoadjuvant chemoradiotherapy pathological complete response Radiomic

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Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting

arXiv

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arXiv 2024年

作者： Zhuo, Linhai Wang, Zheng Qian, Tianwen Fu, Yuqian College of Computer and Data Science Fuzhou University Fuzhou China Zhejiang University of Technology College of Computer Science Zhejiang China Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Beihang University Zhejiang China INSAIT Sofia University Bulgaria

The source-free cross-domain few-shot learning (CD-FSL) task aims to transfer pretrained models to target domains utilizing minimal samples, eliminating the need for source domain data. Addressing this issue requires models to have robust generalization abilities and strong feature representation, aligning with the characteristics of large-scale pretrained models. However, large-scale models tend to lose representational ability in cross-domain scenarios due to limited sample diversity. Given the abundant diversity provided by semantic modality, this paper utilize prompt as "free lunch" to enhance the diversity and leverages textual modality to guide the prompt generating. Specifically, we propose the SeGD-VPT framework, which is divided into two phases. The first step aims to increase feature diversity by adding diversity prompts to each support sample, thereby generating varying input and enhancing sample diversity. Furthermore, we use diversity descriptions of classes to guide semantically meaningful learning of diversity prompts, proposing random combinations and selections of texts to increase textual diversity. Additionally, deep prompt tuning is introduced to enhance the model's transfer capability. After training of the first step, support samples with different diversity prompts are input into the CLIP backbone to generate enhanced features. After generation, the second phase trains classifiers using the generated features. Extensive experimental results across several benchmarks verify our method is comparable to SOTA source-utilized models and attain the best performance under the source-free CD-FSL setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Reconfigurable architecture for heterogeneous multi-core and many-core architecture with IoT assistance

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International Journal of High Performance Systems Architecture 2021年第3-4期10卷 162-173页

作者： Xing, Xuefeng Cao, Jing Zhou, Hongtao Song, Lei Qiu, Yanan Northeast Petroleum University Heilongjiang Daqing163318 China The Key Laboratory for Oil Big Data and Intelligent Analysis of Heilongjiang Province Heilongjiang Daqing163318 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Qinhuangdao066000 China

This paper discusses a multi-core real-time device reconfigurable and has a sequence of configurations, each lifted to a predetermined state, executing different functions performed by the process. Multiple cores enhance one's efficiency while malfunctioning and under the requirements of powerful applications and programs to run various operations simultaneously with more ease. In this paper, the reconfigurable architecture for heterogeneous multi-core architecture (RA-HMCA) framework is proposed. However, researchers intend to simplify the program by preventing redundancies from being introduced and decrease the risk of threads when satisfying all necessary real-time requirements. The normalisation process is usually used for the removal of redundancies. The suggested solution employs multi-integer linear programming (MILP) technology in the scan stage to have a viable task model. An optically reconfigurable POSIX-based program creates a procedural performance of this technology. A research paper implementation and successful assessment support and validates the findings predicted. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Internet of things

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