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作者： Xiang, Fu Huaimin, Wang Peichang, Shi Xue, Ouyang Xunhui, Zhang School of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex System Changsha China

The blockchain is a distributed ledger that records all transactions and operations in a shared manner. Public blockchains such as Bitcoin realize decentralization at the cost of mining overhead, which is not suitable for real-life scenarios requiring high throughput. Techniques such as the consortium blockchain improve efficiency through partial decentralization. However, the consensus algorithms used in the existing state-of-the-art consortium blockchains face many challenges when dealing with commercial applications. For example, the high communication overhead hinders the scalability of PBFT-based consensus algorithms even though they are efficient at small scale. Hashgraph, one of the most popular Directed Acyclic Graph-based (DAG-based) consensus algorithms, achieves good performance in scalability;however, it does not allow users' dynamic participation. To deal with these challenges, we propose Jointgraph, a Byzantine fault-tolerance consensus algorithm for consortium blockchains based on DAG. In Jointgraph, transactions are packed into events and validated by no less than 2/3 of all members. A supervisor is introduced in our design, who monitors member behaviors and improves consensus efficiency. Simulation results demonstrate that Jointgraph outperforms Hashgraph in both throughput and latency. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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Autonomous Navigation and Obstacle Avoidance for Mobile Robots in a Tax Environment 13

Autonomous Navigation and Obstacle Avoidance for Mobile Robo...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Yu, Lianyi Wu, Zhengxing Jiang, Yang Tan, Min Yu, Junzhi Institute of Automation Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Northeastern University Shenyang110000 China BIC-ESAT State Key Laboratory for Turbulence and Complex System College of Engineering Peking University Department of Mechanics and Engineering Science Beijing100871 China

ISBN: (纸本)9798350315196

This paper focuses on the navigation and obstacle avoidance problems of intelligent mobile robots in a tax environment. Through combining the improved jump point search (JPS) and dynamic window approach (DWA) algorithms, a fusion path planning method is proposed for the mobile robots in tax services. Specially, a bi-directional JPS algorithm with an adaptive heuristic function is provided to decrease the search complexity and improve search flexibility in the global path planning. Meanwhile, considering the features of the tax environment, several evaluation subfunctions are particularly designed to leverage the global information and optimize the local path planning. Finally, various simulations and experiments in a Turtlebot3 robot validate the feasibility and effectiveness of the proposed method. The obtained results provide valuable guidance for the autonomous navigation research of the tax service robots. © 2023 IEEE.

关键词： Taxation

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Modeling the dynamic alignment of business and information systems via the lens of human-centered architecture evolution

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Journal of systems engineering and Electronics 2021年第4期32卷 881-888页

作者： ZHANG Mengmeng YI Shuanghui CHEN Honghui LUO Aimin LIU Junxian ZHANG Xiaoxue Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410072China National Key Laboratory for Complex System Simulation Military Academy of SciencesBeijing 100080China

The complexity of business and information systems(IS)alignment is a growing concern for researchers and practitioners *** extant research on alignment architecture fails to consider the human viewpoint,which makes it difficult to embrace emergent *** paper contributes to the extant literature in the following ***,we combine an enterprise architecture(EA)framework with a human viewpoint to address alignment issues in the architecture design phase;second,we describe a dynamic alignment model by developing a humancentered meta-model that explains first-and second-order changes and their effects on alignment *** paper provides better support for the theoretical research and the practical application of dynamic alignment.

关键词： business and information systems(IS)alignment human viewpoint enterprise architecture(EA) meta-model dynamic alignment

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis 25

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

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25th IEEE China Conference on system Simulation Technology and its Application, CCSSTA 2024

作者： Huang, Jiahui Zheng, Xiangxiang Lv, Guodong Li, Xiaojing Wu, Guohua Xu, Liang Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China The First Affiliated Hospital of Xinjiang Medical University State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia Urumqi China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350366600

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached 95.7%. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and 1372 cm-1 might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application. © 2024 IEEE.

关键词： Raman scattering

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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BPH-YOLOv5: Improved yolov5 based on biformer prediction head for small target cigatette detection

BPH-YOLOv5: Improved yolov5 based on biformer prediction hea...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Zheng, Xinyi Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9781839539848

Small object detection in the cigarette detection field is a recent popular task. As cigarette targets in surveillance are tiny, the object scale leads to a great challenge of object detection. To solve the issue mentioned above, we propose BPH-YOLOv5 based on YOLOv5. We add one more prediction head to detect different-scale objects, which is more sensitive to tiny objects using multi-scale features. Then we replace the original prediction heads with BiFormer Prediction Heads (BPH) to explore the prediction potential with an attention mechanism. Experiments showed that the mean average precision (mAP) of BPHYOLOv5 in the cigarette dataset reached 90.34%, which was 6.94% higher than that of YOLOv5-s prototype, 7.20% higher than that of YOLOv7, which are encouraging and competitive. Tests on the actual scenarios verified that the accuracy of smallscale target detection was significantly improved. © The Institution of engineering & Technology 2023.

关键词： Forecasting

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SegSdetr: Harnessing supervisions in SAM with detection label for efficient smoke detection

SegSdetr: Harnessing supervisions in SAM with detection labe...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Li, Dan Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Ministry of Education Nanjing210096 China

ISBN: (纸本)9781839539848

In recent years, forest fires often result in significant environmental and economic losses, spurred researchers to investigate early forest fire detection methods. Embedded devices with the object detection task can efficiently alarm early fire including smoke from a large field of view along with providing additional information such as location of the fire. But it's difficult to deploy large DNN in the chipsets in embedded devices. To satisfied the requirements for lightweight, this paper contributes to the existing research by proposing an efficient smoke detection model (SegSdetr) harnessing supervisions in Segment Anything Model (SAM) combined detection label. We lightweight the structure of model's backbone by solving linear redundancy observed in feature maps within models. And to compensate the drop accuracy from the lightweight, we have proposed a method harnessing the reference of SAM model. SegSdetr exhibits significantly superior precision and a 30% reduction in parameter efficiency compared to RT-DETR with the original backbone model. After incorporating our method, there is a further enhancement in precision. Additionally, our model outperforms the YOLOv8 family where a 3% increase in mAP50 with yolov8-n with the same Flops. © The Institution of engineering & Technology 2023.

关键词： Smoke

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CCMOP: A Runtime Verification Tool for C/C++ Programs 23rd

CCMOP: A Runtime Verification Tool for C/C++ Programs

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23rd International Conference on Runtime Verification, RV 2023

作者： Xing, Yongchao Chen, Zhenbang Xu, Shibo Zhang, Yufeng College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9783031442667

Runtime verification (RV) is an effective lightweight formal method for improving software’s reliability at runtime. There exist no RV tools specially designed for C++ programs. This paper introduces the first one, i.e., CCMOP, which implements an AOP-based RV approach and supports the RV of general properties for C/C++ program. CCMOP provides an AOP language specially designed for C++ program to define the events in RV. The instrumentation of RV monitor is done at AST-level, which improves the efficiency of compilation and the accuracy of RV. CCMOP is implemented based on JavaMOP and an industrial-strength compiler. The results of extensive experiments on 100 real-world C/C++ programs (5584.3K LOCs in total) indicate that CCMOP is robust and supports the RV of real-world C/C++ programs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： C++ (programming language)

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F-IKOS: An Abstract Interpretation-based Static Analyzer for Fortran Programs 12

F-IKOS: An Abstract Interpretation-based Static Analyzer for...

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12th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2024

作者： Zou, Sheng Chen, Liqian Fan, Guangsheng Huang, Renjie Yin, Banghu College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The Fortran programming language is widely utilized in numerical computation and scientific computing. Fortran programs are prone to potential runtime errors related to numerical properties due to the large number of numerical operations. In this paper, we present F-IKOS, an abstract interpretation-based static analyzer for Fortran programs on top of IKOS, which soundly handles floating-point types in Fortran programs. Firstly, we translate Fortran programs to LLVM IR using compiler front-end Flang. After that, we extend IKOS to support sound floating-point analysis and then employ it to analyze the translated LLVM IR. Particularly, when analyzing floating-point types in programs, we first abstract floating-point expressions into real-number expressions with interval coefficients, and then linearize these expressions into real-number expressions with scalar coefficients. These linear expressions are subsequently handled by abstract domains originally designed for real-number types to produce sound analysis results. We have conducted experiments on representative Fortran programs to show the efficiency and effectiveness of F-IKOS. The experimental results are encouraging: F-IKOS soundly analyzes runtime errors in complex programs, outperforming other analyzers. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： FORTRAN (programming language)

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Prompting Continual Person Search 24

Prompting Continual Person Search

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Ning, Xin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Computing Macquarie University Sydney Australia Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increasing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a Prompt-based Continual Person Search (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projections and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code is available at https://***/PatrickZad/PoPS. © 2024 ACM.

关键词： Contrastive Learning

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