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Enhanced Fire Detection for Industrial and Environmental Safety using IoT-Integrated Vision Sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks 3

Enhanced Fire Detection for Industrial and Environmental Saf...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Christy Sujatha, D. Singh, Priyanshu Vallikannu, C. Varman, Ravi Skanda, M.G. Gavaskar, T. Department of Software Engineering Periyar Maniammai Institute of Science & Technology Tamil Nadu Thanjavur India Department of Computer Science and Engineering R.V college of engineering Karnataka Bangalore India Department of Humanities and Sciences Rajalakshmi Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Tamil Nadu Coimbatore India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Karnataka Mysore India Department of Mechanical Engineering St. Joseph's College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. By incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%. © 2025 IEEE.

关键词： Sprinkler systems (fire fighting)

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ROGPL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

arXiv

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arXiv 2024年

作者： Zhang, Qin Li, Xiaowei Lu, Jiexin Qiu, Liping Pan, Shirui Chen, Xiaojun Chen, Junyang College of Computer Science and Software Engineering Shenzhen University China School of Information and Communication Technology Griffith University Australia

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROGPL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROGPL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROGPL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROGPL on several benchmark graph datasets demonstrate that it has good performance. © 2024, CC BY.

关键词： Iterative methods

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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A Virtual Reality Educational Tool for Complex Brain Anatomy

A Virtual Reality Educational Tool for Complex Brain Anatomy

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International Conference on Information and Communication Systems (ICICS)

作者： Shaikhah Alhmoudi Sumyah Alkatheri Shamma Alghafri Abdel Rahman Idrais Rafat Damseh Deparment of Computer and Software Engineering United Arab Emirates University Al Ain UAE Deparment of Electrical and Computer Engineering Concordia University Montreal Canada

Brain anatomy presents a unique pedagogical challenge in medical education due to its complex structures and the detailed intricacies inherent to various brain regions. Advanced imaging techniques such as computed tomography (CT), T1/T2 magnetic resonance imaging (MRI), and diffusion imaging provide 3D representations of these anatomical details, which require suitable tools and visualization setups to be taught efficiently to medical students. Machine learning and virtual reality have emerged as promising avenues for refining the visualization and reconstruction processes of these details. This paper delves into the integration of cutting-edge deep learning (DL) algorithms designed for the reconstruction of brain vascular trees and neural fiber tracts within an immersive virtual reality (VR) framework. Our proposed solution has the potential to improve brain anatomy education by serving as tool for illustrating subject-specific complex structures. The ensuing results underscore the viability and efficacy of our solution.

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Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism

Underwater Image Enhancement Method Based on CGAN and Parall...

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Neural Networks, Information and Communication engineering (NNICE), International Conference on

作者： Chi Ma Le Yang Hui Hu Yuenai Chen Anxu Bu The School of Computer Science and Engineering Huizhou University Huizhou China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study.

关键词： Image color analysis Semantics Diversity reception Scattering Generative adversarial networks Distortion Generators

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NUMERICAL INVESTIGATION ON AERODYNAMIC PERFORMANCE OF HOVERING ROTOR IN GROUND EFFECT 34th

NUMERICAL INVESTIGATION ON AERODYNAMIC PERFORMANCE OF HOVERI...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Li, Xin-Long Xu, Jian-Hua Song, Wen-Ping Han, Shao-Qiang Han, Zhong-Hua School of Aeronautics Northwestern Polytechnical University Xi’an710071 China National Key Laboratory of Aircraft Configuration Design Xi’an710072 China Tianfu Engineering-oriented Numerical and Software Innovation Center College of Computer Science Sichuan University Chengdu610065 China

As the helicopter hovers near the ground, the interactions among the rotor, the wake, and the ground complicate the flow field, altering the rotor’s aerodynamic forces significantly. To investigate the influence of ground effect on the rotor aerodynamic characteristics, the flow around the rotor in hover is numerically simulated by using the in-house Computational Fluid Dynamics (CFD) codes with a structured dynamic overset grid system. A novel 5th-order WENO-K scheme based on nonlinear reconfiguration is employed for spatial discretization, effectively reducing the numerical dissipation and achieving high-accuracy capture of the tip vortices. In this study, the aerodynamic forces and flow fields of the S-76 rotor in ground effect and out ground effect are compared. The influence and mechanisms of ground effect on rotor aerodynamic characteristics are analyzed in detail. Results show that the ground effect causes a strong upwash flow in the inboard region of the blade and compresses the tip vortices, result in a significant increase in thrust at the blade root and a slight decrease in thrust at the blade tip, respectively. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Computational fluid dynamics

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Super-exponential quantum advantage for finding the center of a sphere

arXiv

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arXiv 2024年

作者： Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

This article considers the geometric problem of finding the center of a sphere in vector space over finite fields, given samples of random points on the sphere. We propose a quantum algorithm based on continuous-time quantum walks that needs only a constant number of samples to find the center. We also prove that any classical algorithm for the same task requires approximately as many samples as the dimension of the vector space, by a reduction to an old and basic algebraic result—Warning’s second theorem. Thus, a super-exponential quantum advantage is revealed for the first time for a natural and intuitive geometric problem. © 2024, CC BY.

关键词： Spheres

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RFSNU: Reliable Forwarding Strategy in NDN based UWSN

RFSNU: Reliable Forwarding Strategy in NDN based UWSN

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Ahmed Khalid Rana Asif Rehman Byung-Seo Kim Department of Computer Science National University of Computer & Emerging Sciences Pakistan Dept. of Software and Communications Engineering Hongik University Sejong-si South Korea

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Underwater wireless sensor network (UWSN) is a communication paradigm that works in underwater environment such as in oceans, rivers, lakes, and seas. Multiple sensors are deployed in underwater that collect information according to user demand and forwards the data towards the Underwater Autonomous Vehicle (UAV) that send the data towards the ship or user. UWSN is based on Internet Protocol (IP). There are several limitations in existing IP based architecture such as data redundancy, location dependency, mobility. Therefore, there is need for a new network that enhance the network efficiently. Named Data Network (NDN) is a new communication paradigm that provides several built-in benefits such as in-network caching, mobility, avoid data duplication, and fast content retrieval time. In case of NDN based UWSN (NUWSN), network traffic increases due to bandwidth and large propagation delay. Due to dynamic nature of water the ratio of traffic load, packet duplication, packet retransmission increases. When delay is large then more packets can loss and more number of packet retrans-mission, and data duplication occurs. Therefore, there is need for efficient forwarding mechanism in NUWSN that minimize data duplication and packet retransmission. In this paper, we proposed a forwarding mechanism named as Reliable Forwarding Strategy in NDN based UWSN (RFSNU). RFSNU improves the forwarding process in underwater environment using depth based forwarding. Our scheme improves packets retransmission rate, and packet duplication that are main problems in NUWSN.

关键词： Wireless communication Wireless sensor networks Protocols Redundancy Telecommunication traffic Sensors Rivers

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Experiment Design to Evaluate MVDC Cable Pole to Ground Arc Fault Models

Experiment Design to Evaluate MVDC Cable Pole to Ground Arc ...

引用

IEEE General Meeting Power& Energy Society

作者： Andre N. Cuppen Nirmal-Kumar C Nair Powerco New Plymouth New Zealand Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Arcing faults in medium voltage DC (MVDC) cables are foreseen to more severely exert cables and network equipment, as well as have a higher potential to harm people and property by arc-flash and fire than their MV AC equivalent. Understanding the dynamics of the fault transients and detailed analysis of the fault current under different types of faults is essential for designing robust protection logic as well as choosing the optimal grounding scheme. Such analysis requires MVDC arc fault models, suitable to be included in EMT simulations at circuit level, with the focus on MVDC cables, since such models are not available for MVDC cables and components. This paper discusses available models to simulate the static and dynamic behavior of MVDC cable arc faults. The behavior of several MV AC cable faults is qualitatively analyzed. Electrical characteristics of arcing faults in MV cable, relevant models, experiments and field measurements results are critically analyzed before recommending experiments designed to arrive at an empirical and realistic MVDC cable arc model, suitable for EMT and circuit simulations.

关键词： Analytical models Insulation Medium voltage Data models Circuit faults Logic Integrated circuit modeling Transient analysis Protection Cables

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EmoTracer: A User-independent Wearable Emotion Tracer with Multi-source Physiological Sensors Based on Few-shot Learning

EmoTracer: A User-independent Wearable Emotion Tracer with M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenting Kuang Shuo Jin Danyang Wang Yuda Zheng Yongpan Zou Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Information Hub Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The rising prevalence of mood disorders, including depression and anxiety, underscores the critical state of physical and mental health issues in contemporary society. Automatic emotion recognition technology emerges as a potential tool for monitoring mental health disorders, and offering valuable guidance through human-computer interfaces. However, existing technologies grapple with limitations, such as emotional concealment, potential privacy leakage, and specific device location restrictions. To overcome these limitations, we propose a mobile wearable emotion recognition system called EmoTracer incorporating ubiquitous multi-source sensors to measure physiology signals, and address challenges such as the intricate signal-emotion relationship, sparse data processing, and notable disparities among different subjects. We implement a real-time prototype and carry out comprehensive experiments to evaluate its performance. For six basic emotions, the results show that EmoTracer can achieve 95.6% accuracy in intra-subject emotion classification and 80.7% accuracy with 5 shots in cross-subject emotion classification.

关键词： Emotion recognition Privacy Accuracy Prototypes Mental health Sensor systems Real-time systems Biomedical monitoring Wearable sensors Monitoring

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