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International Conference on Control, Automation and Systems ( ICCAS)

作者： Jin-Ung Ha Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do Republic of Korea

In this paper, we propose a method to improve the recognition rate of ArUco markers under photon-starved conditions by using preprocessing such as photon counting algorithm and histogram equalization to estimate the exact three-dimensional(3D) location of improved objects. In the conventional ArUco marker recognition technology, the recognition rate of ArUco markers decreases under photon-starved conditions, and the exact 3D location of the object cannot be estimated. In the conventional ArUco marker recognition technology, the photon counting algorithm can be used to estimate the 3D location of objects by recognizing ArUco markers under photon-starved conditions. However, the photon counting algorithm has the problem that the computation speed is slowed down when there are many photons to estimate, and the recognition rate of the ArUco marker is reduced when there are few photons to estimate. To solve these problems of the photon counting algorithm and improve the recognition rate of ArUco markers, we apply histogram equalization to ArUco markers with the photon counting algorithm. To verify the feasibility of the proposed method, we implement simulations using the Open Manipulator-X platform and show performance metrics such as Peak Signal-to-Noise Ratio (PSNR).

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GraphKAN: An Efficient Graph Kolmogorov Arnold Networks for Traffic Forecasting

GraphKAN: An Efficient Graph Kolmogorov Arnold Networks for ...

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2024 IEEE International Conference on Smart City (SmartCity)

作者： Wenzhu Zhao Guan Yuan Rui Bing Xiao Liu Guixian Zhang School of Computer Science and Technology China University of Mining and Technology Xuzhou China Engineering Research Center of Mine Digitalization China University of Mining and Technology Xuzhou China

ISBN: (数字)9798331540487

ISBN: (纸本)9798331540494

Traffic forecasting is an essential task for the progression of smart cities. Graph Neural Networks (GNNs) based traffic forecasting models can effectively model the complex spatio-temporal dependencies in traffic data and have become the most widely used traffic forecasting method in recent years. However, these methods suffer from high computational complexity, cumbersome structures, and optimization difficulties. To address the above issues, we propose an Efficient Graph Kolmogorov Arnold Networks for Traffic Forecasting (GraphKAN). Specifically, we first design patch processing to delineate the original traffic sequences to reduce the computational overhead of the model. Then we propose an adaptively generated dynamic graph network to capture the dynamic spatial dependence in the traffic data efficiently. We utilize Kolmogorov Arnold Networks with adaptive activation functions to capture complex relationships and nonlinear patterns in time series, which replace traditional linear weights with spline-parametrized univariate functions, significantly reducing the number of parameters and improving interpretability. Finally, we validate the effectiveness and efficiency of GraphKAN via various experiments. The results show that our GraphKAN is advanced in terms of computational speed, number of model parameters, and prediction performance.

关键词： Correlation Smart cities Computational modeling Time series analysis Predictive models Graph neural networks Data models Forecasting Splines (mathematics) Optimization

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Threat Detection in VANET Communications

Threat Detection in VANET Communications

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Akash Kandakatla Sudha Anbalagan School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Smart Grid Technologies SCOPE Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Communication plays a vital role in autonomous vehicles networks. Detecting the security threats in communication still acts as an open challenge. Leveraging the simulation environments such as OMNeT++ for network simulation and SUMO for traffic mobility visualization, we can show a realistic vehicular environment where vehicles communicate with each other and interact dynamically. The integration of Snort, an open-source intrusion detection system, allows for the detection and representation of various malicious attacks that commonly afflict Vehicular Adhoc Networks (VANETs). This work addresses the critical security challenges faced by VANETS, including the protection of communication integrity, privacy preservation, and resilience against malicious entities. By simulating realistic traffic scenarios and diverse attack vectors, the effectiveness of Snort in detecting and mitigating threats are assessed including black hole attacks, denial-of-service (DoS) attacks, and so forth. Through visualization techniques, we provide insights into the dynamics of VANETs and the impact of malicious activities on network performance and reliability. Real-time monitoring and alerting mechanisms are implemented to notify network administrators of detected threats, enabling timely response and mitigation actions. The outcomes of this research contribute to the development of robust security solutions for VANETs, enhancing the safety and trustworthiness of connected vehicle systems. By combining simulation environments with intrusion detection capabilities, the proactive defense mechanisms against evolving cyber threats in the context of intelligent transportation systems are addressed.

关键词： Visualization Renewable energy sources Vehicular ad hoc networks Intrusion detection Vectors Threat assessment Security

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Deep Cut-informed Graph Embedding and Clustering

arXiv

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arXiv 2025年

作者： Ning, Zhiyuan Wang, Zaitian Zhang, Ran Xu, Ping Liu, Kunpeng Wang, Pengyang Ju, Wei Wang, Pengfei Zhou, Yuanchun Cambria, Erik Chen, Chong Computer Network Information Center CAS China University of Chinese Academy of Sciences China Department of Computer Science Portland State University United States Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau China College of Computer Science Sichuan University China School of Computer Science and Engineering Nanyang Technological University Singapore Future City Lab Terminus Group Singapore

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there is a common issue of representation collapse in existing GNN-based deep graph clustering algorithms. We attribute two main reasons for such issues: (i) the inductive bias of GNN models: GNNs tend to generate similar representations for proximal nodes. Since graphs often contain a non-negligible amount of inter-cluster links, the bias results in error message passing and leads to biased clustering;(ii) the clustering guided loss function: most traditional approaches strive to make all samples closer to pre-learned cluster centers, which causes a degenerate solution assigning all data points to a single label thus making all samples similar and less discriminative. To address these challenges, we investigate graph clustering from a graph cut perspective and propose an innovative and non-GNN-based Deep Cut-informed Graph embedding and Clustering framework, namely DCGC. This framework includes two modules: (i) cut-informed graph encoding;(ii) self-supervised graph clustering via optimal transport. For the encoding module, we derive a cut-informed graph embedding objective to fuse graph structure and attributes by minimizing their joint normalized cut. For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments, which can balance the guidance of "proximity to the pre-learned cluster center". With the above two tailored designs, DCGC is more suitable for the graph clustering task, which can effectively alleviate the problem of representation collapse and achieve better performance. We conduct extensive experiments to demonstrate that our method is simple but effective compared with benchmarks. © 2025, CC BY.

关键词： Graph embeddings

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An Autonomic Architecture for Multi-Agent Self-Maintaining Robotic Systems

An Autonomic Architecture for Multi-Agent Self-Maintaining R...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： K. Dillon Lewis Christopher Rouff Ali Tekeoglu Computer Science Department Whiting School of Engineering Johns Hopkins University Applied Physics Laboratory & JHU Whiting School of Engineering Johns Hopkins University Whiting School of Engineering Leidos Innovations Center & Johns Hopkins University

Autonomic Computing (AC) systems have four maj or characteristics: self-healing, self-configuring, self-optimizing and self-protecting, combined refer to self-CHOP. This paper examines the self-healing properties of an autonomic system and how they can be applied to a multi-agent autonomous robotic system. This work defines an architecture that incorporates the maintenance of robots into the autonomic management of multi-agent systems. Related concepts from self-healing, prognostics and health management (PHM), and non-destructive evaluation (NDE) domains are discussed and integrated into the design of the architecture. A simple simulation applying the architecture is implemented using python and preliminary results are presented.

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Cross-subject EEG-based Motor Imagery Recognition for Patient’s Rehabilitation

Cross-subject EEG-based Motor Imagery Recognition for Patien...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yalan Ye Xinxin Mu Tongjie Pan Yuxiang Li Lin Wei Xiaoli Fan Lan Wei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Civil Aviation Flight University of China China Air Force Medical Center Air Force Medical University Beijing China FutureNeuro SFI Research Centre School of Computer Science University College Dublin Dublin Ireland

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Motor imagery (MI), a kind of psychological representation without actual action, has garnered increasing attention in rehabilitation. However, the inherent differences between patients and healthy persons hinder rehabilitation by reducing the accuracy of cross-subject MI recognition. Although unsupervised domain adaptation (UDA) methods have mitigated individual differences, they still suffer from challenges in terms of selecting confusing source domains and accurately classifying MI samples at the boundary. To address these challenges, we propose a novel UDA framework with a causal graphical model and label similarity clustering. The causal graphical model is employed to estimate the similarity of EEG signals, enabling the causal selection to effectively avoid confusing healthy persons’ data. In addition, label similarity clustering mechanism is utilized to establish a distinct boundary, thereby enhancing the classification accuracy. The experimental results demonstrate that our approach outperforms baseline 10.10% and 16.27% on BCI IV-2a&2b, separately. MI is expected to aid rehabilitation through precise recognition and active support.

关键词： Degradation Image recognition Graphical models Accuracy Psychology Stroke (medical condition) Motors Electroencephalography engineering in medicine and biology

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When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability

arXiv

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arXiv 2024年

作者： Xuan, Wenjie Xu, Yufei Zhao, Shanshan Wang, Chaoyue Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China The University of Sydney Sydney Australia Nanyang Technological University Singapore

ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-expert users, the output would include unwanted artifacts. This paper first highlights the crucial role of controlling the impact of these inexplicit masks with diverse deterioration levels through in-depth analysis. Subsequently, to enhance controllability with inexplicit masks, an advanced Shape-aware ControlNet consisting of a deterioration estimator and a shape-prior modulation block is devised. The deterioration estimator assesses the deterioration factor of the provided masks. Then this factor is utilized in the modulation block to adaptively modulate the model's contour-following ability, which helps it dismiss the noise part in the inexplicit masks. Extensive experiments prove its effectiveness in encouraging ControlNet to interpret inaccurate spatial conditions robustly rather than blindly following the given contours, suitable for diverse kinds of conditions. We showcase application scenarios like modifying shape priors and composable shape-controllable generation. Codes are available at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Masks

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Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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Analysis of Accurate Focal Points and Automatic Method of Digital Holographic Microscopy (DHM) for Obtaining an Accurate 3D Profile

Analysis of Accurate Focal Points and Automatic Method of Di...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Taishi Ono Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do Republic of Korea

Currently, Digital Holographic Microscopy(DHM) is used for researching disease diagnosis or microbes. It cannot obtain the correct three-dimensional (3D) profile for a little noise because biological cells are microscopic. Then we need images of the focal point of DHM for reducing noise. However, in DHM, the depth of field becomes smaller influenced by the objective lens. In addition, focusing to obtain hologram images of biological cells in conventional DHM systems is entirely manual, because the decision of the focal point is made by the photographer's senses, which makes it even more difficult to focus on the object. These factors make it difficult for the conventional DHM system to acquire images of the focal points. Therefore, in this paper, we propose the analysis of accurate focusing points using a noise map. This eliminates the illusion or misperception that is a drawback of the manual. We improved an accurate 3D profile. 3D profiles were created at the focal point by the proposed method and at the positions before and after the focal points, and evaluated for SNR. From this, the proposed method can be used to analyze biological cells and microstructures.

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Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference

Galaxy: A Resource-Efficient Collaborative Edge AI System fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shengyuan Ye Jiangsu Du Liekang Zeng Wenzhong Ou Xiaowen Chu Yutong Lu Xu Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China IoT Thrust and Research Center for Digital World With Intelligent Things HKUST (Guangzhou) Guangzhou China Data Science and Analytics Thrust HKUST (Guangzhou) Guangzhou China Department of Computer Science and Engineering HKUST Hong Kong SAR China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induce substantial pressure on the backbone network as well as raise users’ privacy concerns. To address that, in-situ inference has been recently recognized for edge intelligence, but it still confronts significant challenges stemming from the conflict between intensive workloads and limited on-device computing resources. In this paper, we leverage our observation that many edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources and propose Galaxy, a collaborative edge AI system that breaks the resource walls across heterogeneous edge devices for efficient Transformer inference acceleration. Galaxy introduces a novel hybrid model parallelism to orchestrate collaborative inference, along with a heterogeneity-aware parallelism planning for fully exploiting the resource potential. Furthermore, Galaxy devises a tile-based fine-grained overlapping of communication and computation to mitigate the impact of tensor synchronizations on inference latency under bandwidth-constrained edge environments. Extensive evaluation based on prototype implementation demonstrates that Galaxy remarkably outperforms state-of-the-art approaches under various edge environment setups, achieving up to 2.5× end-to-end latency reduction.

关键词： Tensors Computational modeling Collaboration Prototypes Edge AI Smart homes Parallel processing

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