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SSRN 2024年

作者： Shoby, Abin Francis, Jerome Raju, Jini Pillai, Manu J. John, Ansamma Valakkada, Jineesh Amma, Ushadevi C. Rajan, Ginu Rajaram, Priyatharshini Department of Computer Science and Engineering Thangal Kunju Musaliar College of Engineering APJ Abdul Kalam Technological University Kerala India Department of Imaging Sciences and Interventional Radiology Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum India Department of Electronics and Communications Engineering Amrita Vishwa Vidyapeetham Amritapuri Campus Kerala India Centre for Engineering Research in Intelligent Sensors and Systems Cardiff School of Technologies Cardiff Metropolitan University Wales United Kingdom

IntraVoxel Incoherent Motion Imaging is preferred to conventional MRI technique as it brings out more information by the expense of scan time due to the need of multiple b-values, resulting in patient discomfort and quantification errors. The careful reduction of b-values without compromising the quality of MRI scans is the main challenge in the field of IVIM studies. To address this challenge, a novel approach is proposed to generate the unknown b-value images from minimum number of acquired b-value images by analyzing the haemodynamic properties of the tissue using a Bi-Directional Long Short Term Memory network which can explore the sequential nature and the bi-exponential property of IVIM signals. The experiment is carried out on simulated IVIM signals of brain tissue by successively reducing the b-value count in the input b-value sequence from 8 down to 3 and generating the signals corresponding to the required b-values. The results are validated using clinical images in the DIPY dataset and evaluated by comparing the generated images with their reference images in terms of performance metrics like Structural Similarity metric and radiology score, and is comparable to the image generated from the real IVIM environment. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Evacuation Problem Under the Nuclear Leakage Accident

Evacuation Problem Under the Nuclear Leakage Accident

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第40届中国控制会议

作者： Canqi Yao Shibo Chen Zaiyue Yang School of Mechatronics Engineering Harbin Institute of Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineeringand the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in UniversitiesSouthern University of Science and Technology

To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node（bus pickup stop）,4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming（MINLP）problem. Then, to eliminate the difficulties of choosing a proper M value in Big-M method, a Big-M free method is employed to linearize the nonlinear terms of the MINLP problem. Finally, the resultant mixed integer linear program（MILP） problem is solvable with efficient commercial solvers such as CPLEX or Gurobi, which guarantees the optimal evacuation plan *** evaluate the effectiveness of proposed evacuation model, we test our model on two different scenarios（a random one and a practical scenario）. For both scenarios, our model attains executable evacuation plan within given 3600 seconds computation time.

关键词： Nuclear leakage accident bus evacuation problem mixed integer nonlinear programming

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Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

arXiv

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arXiv 2024年

作者： Mattamala, Matias Frey, Jonas Libera, Piotr Chebrolu, Nived Martius, Georg Cadena, Cesar Hutter, Marco Fallon, Maurice Dynamic Robot Systems Group University of Oxford 23 Banbury Road Oxford OxfordshireOX2 6NN United Kingdom Robotic Systems Lab ETH Zurich Leonhardstrasse 21 Zurich Zurich8092 Switzerland Autonomous Learning Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Baden-Württemberg72076 Germany Computer Science Department University of Tüüingen Maria-von-Linden-Strasse 6 Tübingen Baden-Württemberg72076 Germany

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. © 2024, CC BY.

关键词： Parks

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Next-generation smart homes: CO₂ monitoring with Matter protocol to support indoor air quality

Internet of Things (The Netherlands)

引用

Internet of Things (The Netherlands) 2025年 32卷

作者： Mota, Afonso Serôdio, Carlos Briga-Sá, Ana Valente, Antonio Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro (UTAD) Vila Real 5000-801 Portugal Center ALGORITMI Universidade do Minho Campus de Azurém Guimarães 4800-058 Portugal CQ-VR (Center of Chemistry of Vila Real) Universidade de Trás-os-Montes e Alto Douro Vila Real 5000-801 Portugal CRIIS (Centre for Robotics in Industry and Intelligent Systems) INESC-TEC—Institute for Systems and Computer Engineering Technology and Science Campus da FEUP Porto 4200-465 Portugal

Humans spend most of their time indoors, where air quality and comfort are crucial to health and well-being. Elevated CO2 levels in buildings can reduce cognitive function, discomfort, and health issues. Indoor CO2 mo... 详细信息

Humans spend most of their time indoors, where air quality and comfort are crucial to health and well-being. Elevated CO₂ levels in buildings can reduce cognitive function, discomfort, and health issues. Indoor CO₂ monitoring has emerged as a key focus in the literature, particularly in residential buildings, as it can play a vital role in helping to maintain adequate ventilation rates. The growing smart home market demands seamless integration and control, which are essential for implementing IAQ sensing devices. However, interoperability barriers between platforms and devices continue to hinder smart home adoption. To address these challenges, Matter protocol is starting to appear in the market. In this work, a wireless CO₂ sensor is developed based on ESP32-C6 and SCD40 and integrated into a created Matter-enabled ecosystem formed with the Home Assistant open-source platform. The utilized hardware and software enable the usage of two different wireless communication technologies, WiFi and Thread, enhancing compatibility. The study highlights the rapid and seamless onboarding of the developed CO₂ monitoring device into smart home ecosystems using the Matter protocol. As a result, once the device is successfully added to the ecosystem, the measurements can be accessed and analyzed through a mobile application, forming an IoT environment. © 2025

关键词： CO2 Espressif Indoor air quality Interoperability IoT Matter Smart home

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Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap

arXiv

引用

arXiv 2023年

作者： Zuo, Shiyuan Fan, Rongfei Hu, Han Zhang, Ning Gong, Shimin The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518055 China

In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks. At each user, model parameter is firstly updated by multiple steps, which is adjustable over iterations, and then pushed to the aggregation center directly. This decreases the number of interactions between the aggregation center and users, allows each user to set training parameter in a flexible way, and reduces computation burden compared with existing works that need to combine multiple historical model parameters. At the aggregation center, geometric median is leveraged to combine the received model parameters from each user. Rigorous proof shows that zero optimality gap is achieved by our proposed method with linear convergence, as long as the fraction of Byzantine attackers is below half. Numerical results verify the effectiveness of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces

引用

IEEE Internet of Things Journal 2025年

作者： Mujtaba, Ghulam Khowaja, Sunder Ali Dev, Kapal Dublin City University School of Computing Faculty of Engineering and Computing Ireland University of Sindh Faculty of Engineering and Technology Pakistan Munster Technological University Department of Computer Science Cork Ireland University of Johannesburg Department of Institute of Intelligent Systems Auckland Park 2006 South Africa Chitkara University Chitkara University Institute of Engineering and Technology Rajpura 140401 India Anderson College of Business and Computing Regis University Denver CO United States

Social media has become integral to minors’ daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional content moderation techniques have proven ineffective against exploiters’ evolving tactics. To address these growing challenges, we propose the EdgeAIGuard content moderation approach that is designed to protect minors from online grooming and various forms of digital exploitation. The proposed method comprises a multi-agent architecture deployed strategically at the network edge to enable rapid detection with low latency and prevent harmful content targeting minors. The experimental results show the proposed method is significantly more effective than the existing approaches. © 2014 IEEE.

关键词： Agentic AI Agentic LLMs Minor Protection Multi-agent architecture Online Grooming

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Look closer to segment better: Boundary patch refinement for instance segmentation

arXiv

引用

arXiv 2021年

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin State Key Laboratory of Intelligent Technology and Systems THU-Bosch JCML Center BNRist Institute for AI Department of Computer Science and Technology Tsinghua University China Institute of Automation CAS University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI CAS China

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR. Copyright © 2021, The Authors. All rights reserved.

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Ontology-based data interestingness: A state-of-the-art review

Natural Language Processing Journal

引用

Natural Language Processing Journal 2023年 4卷

作者： Abhilash C.B. Kavi Mahesh Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad India

In recent years, there has been significant growth in the use of ontology-based methods to enhance data interestingness. These methods play a crucial role in knowledge management by providing explicit, shareable, and reusable knowledge descriptions. However, limited research has been conducted to explore the impact and practical benefits of employing ontologies to increase data interestingness. A comprehensive analysis of the current state-of-the-art approaches in ontology-based data interestingness is essential to identify future advancements and directions in this field. The main objective of this study is to present a clear roadmap showcasing how ontologies contribute to data interestingness and to evaluate their effectiveness in the existing literature. The findings of this research offer an integrated and comprehensive understanding of the following aspects: (1) interestingness metrics in data mining, (2) association rule mining to identify interesting patterns, (3) ontology-based methods to improve data interestingness, and (4) techniques that use ontologies to derive interestingness in data. The paper concludes with a summary of ontology-based methods for data interestingness and outlines potential avenues for future research.

关键词： Ontology Data interestingness Knowledge management Semantics Association rule mining Data mining Interestingness metrics Ontology-based methods

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Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical systems

arXiv

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arXiv 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave School of Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Technology ABB Corporate Research Sweden Västerås72226 Sweden School of Information Technology Deakin University GeelongVIC3220 Australia Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm10044 Sweden Corporate Research Robert Bosch GmbH Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 2025, CC BY-SA.

关键词： computer control

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High-Precision 3D Printing by Deploying Expandable Microspheres

SSRN

引用

SSRN 2024年

作者： Xu, Yang Wang, Fucheng Zhang, Pei Lu, Chengbang Liu, Weixuan Guo, Yueying Ni, Zhipeng Wang, Jinqing Lu, Baoyang Lou, Qin Liang, Xiangyu Liu, Ji Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Department of Metrology and Measurement Engineering China Jiliang University Hangzhou310018 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518120 China Institute of Bast Fiber Crops Center of Southern Economic Crops Chinese Academy of Agricultural Sciences Changsha410205 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Yiwu Saiens Polymer Materials Co. Ltd Jinhua322099 China Jiangxi Key Lab of Flexible Electronics Flexible Electronics Innovation Institute Jiangxi Science & Technology Normal University Nanchang330013 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Stereolithography-based 3D printing (3DP) technology is widely employed in various industries, including manufacturing, healthcare, energy, biomedical, art and other fields. However, the precision issues, such as dimensional shrinkage and structural warping, significantly hinder its wide applications. In this study, we present a straightforward, efficient yet general strategy to engineer the photosensitive resins with thermally expandable microspheres, thus enabling significant improvement in structural fidelity of the 3DP constructs. Our findings demonstrate that addition of expandable microspheres substantially mitigates the volume shrinkage below 3.98%, in comparison to over 10% for commercially-available photosensitive resins. An exceptionally high precision of 0.035% in dimensional deviation could be achieved, in comparison to over 0.1% for commercial one. Furthermore, due to the low filling ratio, the improvement in 3DP precision does not sacrifice the mechanical properties, thus it does not affect the applications where those photosensitive resins are originally targeting. Our method represents an effective strategy to improve the 3D printing resolution of photosensitive resins, thus opening new directions for high precision 3D printing technology. © 2024, The Authors. All rights reserved.

关键词： Shrinkage

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