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An implementation, evaluation and validation of a dynamic fire and conflagration risk indicator for wooden homes

Procedia computer science 2024年 238卷 49-56页

作者： R.D. Strand L.M. Kristensen Department of Safety Chemistry and Biomedical laboratory sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution of this paper is the design, performance evaluation, and validation of a software implementation of model-based dynamic fire risk prediction for wooden homes using local weather data. A key feature of the implementation is that the software architecture has been designed to support the use of different underlying cloud-based weather data services, and the integration as a service in third-party smart systems embedding fire risk predictions. The performed evaluation shows that the implementation is efficient, as weather data can be retrieved, preprocessed and fire risk predictions computed within seconds using only in the order of Kb’s of memory; and accurate considering our sets of data, as the fire risks computed by the implementation have been validated against a set of in-situ measurements.

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SkyRover: A Modular Simulator for Cross-Domain Pathfinding

arXiv

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arXiv 2025年

作者： Ma, Wenhui Li, Wenhao Jin, Bo Lu, Changhong Wang, Xiangfeng School of Computer Science and Technology East China Normal University China School of Computer Science and Technology Tongji University China School of Mathematical Sciences East China Normal University China Key Laboratory of Mathematics and Engineering Applications MoE China

Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. However, existing simulators often focus on a single domain, limiting cross-domain study. This paper presents the SkyRover, a modular simulator for UAV-AGV multi-agent pathfinding (MAPF). SkyRover supports realistic agent dynamics, configurable 3D environments, and convenient APIs for external solvers and learning methods. By unifying ground and aerial operations, it facilitates cross-domain algorithm design, testing, and benchmarking. Experiments highlight SkyRover’s capacity for efficient pathfinding and high-fidelity simulations in UAV-AGV coordination. Project is available at https://***/view/mapf3d/home. © 2025, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 3

Distributed Deep Learning System for Efficient Face Recognit...

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3rd International Conference on Advanced Information science and System, AISS 2021

作者： Liu, Jinjin Chen, Zhifeng Li, Xiaonan Wei, Tongxin School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process. © 2021 ACM.

关键词： Distillation

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Uniform Coherence in Amalgamated Algebra Along an Ideal

Uniform Coherence in Amalgamated Algebra Along an Ideal

引用

International Conference on Algebra and its Applications, ICAA 2023

作者： Ismaili, Karima Alaoui Kim, Hwankoo Mahdou, Najib Department of Mathematics Faculty of Sciences of Rabat Mohammed V University in Rabat B.P. 1014 Rabat Morocco Division of Computer Engineering Hoseo University Dongnam-gu Korea Republic of Modelling and Mathematical Structures Laboratory Department of Mathematics Faculty of Science and Technology of Fez University S.M. Ben Abdellah Fez Fez city2202 Morocco

ISBN: (纸本)9789819767977

The concept of uniform coherence was first introduced by Soublin and has since been extensively explored by numerous researchers. A ring A is defined as uniformly coherent if there exists a function : N → N, where N represents the set of natural numbers, such that for every n ∈ N and each nonzero homomorphism f: An → A, the kernel ker( f ) can be generated by (n) elements. In this paper, we investigate how the property of uniform coherence is transferred to commuta-tive ring extensions. We establish necessary and sufficient conditions for rings such as A ⋈f J, A ⋈ I, and A ∝ E to possess uniform coherence, covering various classes of ideals and A-modules. Our study contributes to the development of new classes of rings that meet this criterion, and we also introduce new families of rings that demonstrate the distinctiveness of the categories of Noetherian and uniformly coherent rings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Amalgamated algebra Coherent Noetherian Trivial ring extension Uniformly coherent

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RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection 39

RaCMC: Residual-Aware Compensation Network with Multi-Granul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Xinquan Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Event-Triggered-Based Privacy-Preserving Finite-Time Flocking Control for Networked Multi-Agent Systems 63

Event-Triggered-Based Privacy-Preserving Finite-Time Flockin...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yue, Jiangfeng Qin, Kaiyu Shao, Jinliang Shi, Mengji Li, Weihao Zheng, Wei Xing University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

Drawing inspiration from the dynamics of biological groups, flocking behavior has captivated interest due to its adaptive, self-organizing, and resilient characteristics. However, the presence of numerous agents in an open network environment raises concerns regarding the potential for information leakage during flocking movements. To address this issue, we present a decentralized output mapping function, executed independently by each individual agent, to ensure the maintenance of flocking behavior, including velocity alignment, cohesion and collision avoidance. Additionally, to enhance resource efficiency and convergence speed, an event-triggered mechanism is employed to mitigate the excessive consumption of bandwidth and computation resources in traditional flocking control algorithms. Moreover, the designed finite-time controller facilitates rapid convergence of flocking behavior. Finally, the efficacy of our proposed approach is corroborated through numerical simulation experiments. © 2024 IEEE.

关键词： Multi agent systems

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Joint Beamforming and Power Allocation Design for Wirelessly Charged Unmanned Inspecting Vehicles in Power Transmission Systems

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Huang, Yingjie Abdelhady, Amr M. Wang, Qianggang Ye, Jia Chi, Yuan Eltawil, Ahmed M. El-Sherif, Amr A. ElBatt, Tamer Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Province Makkah23955-6900 Saudi Arabia Electronics and Communications Department Egypt University of Informatics Cairo Governorate Egypt Department of Computer Science and Engineering The American University Cairo Governorate Cairo Egypt

The rapid development of the smart grid has intensified research efforts towards automating power transmission system inspections using unmanned intelligent vehicles (UIVs). A major challenge hindering their large-scale deployment is the short operational endurance of these vehicles. This paper addresses this limitation by proposing the use of an energy transmitter (ET) and a reconfigurable intelligent surface for wirelessly charging the vehicles through radio frequency (RF) signals. Additionally, we tackle the nonlinearity of RF-to-direct current (DC) conversion, which shows efficiency saturation at high power levels, by introducing a novel power-splitting (PS) energy harvesting (EH) receiver architecture in the inspection vehicles. The inspection tasks are divided into multiple time slots, and a power transfer efficiency (PTE) maximization problem is formulated to jointly optimize the beamforming design at both the transmitter and the RIS, as well as the transmit power allocation and receiving power splitter settings for each time slot. This approach aims to meet the energy requirements of the unmanned inspection vehicles while adhering to the total available power constraint at the ET. To solve the non-convex PTE maximization problem, we propose an efficient solution utilizing alternating optimization, the Dinkelbach’s method, and successive convex approximation, considering the unique properties of channel structure and finite phase resolution. Numerical results demonstrate that our proposed system and design significantly extend the lifespan of inspection vehicles and achieve high energy efficiency in a low-complexity manner. © 2017 IEEE.

关键词： Inductive power transmission

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ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect

arXiv

引用

arXiv 2024年

作者： Li, Dachong Li, Li Chen, Zhuangzhuang Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Mathematical Sciences Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology China

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that 3 × 3 convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://***/lidc54/shift-wiseConv. © 2024, CC BY-NC-SA.

关键词： Convolution

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Community Search Based on Containment Control of Multi-Agent System with Opinion Leaders

Community Search Based on Containment Control of Multi-Agent...

引用

第43届中国控制会议

作者： Siyang Liu Tong Li Jinliang Shao Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Laboratory of Electromagnetic Space Cognition and Intelligent Control School of Computer Data and Mathematical SciencesWestern Sydney University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Community search is the problem of identifying the community in which a given node *** from traditional community search methods based on specific topological structures,this paper proposes a community search algorithm based on containment control of multi-agent system by studying the influence of opinion leaders with competitive relationships in community ***,the Breadth-First Search method is used to locally sample the ***,containment control of multi-agent system is applied to obtain the final state of each node for local community ***,grounded in practical considerations,an effective leadership transfer mechanism is proposed to update the results and enhance search *** experiments on real-world networks and synthetic networks in comparison with three local community detection algorithms,the effectiveness and rationality of our algorithm are demonstrated.

关键词： Community Search Opinion Leaders Multi-Agent System Containment Control

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