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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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Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Class-wise Adaptive Logits Distillation with Meta-Learning

Class-wise Adaptive Logits Distillation with Meta-Learning

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

作者： Xiao Huang Wu Chen Wei Zhou College of Computer and Information Science College of Software Southwest University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge distillation has become a crucial technique for transferring intricate knowledge from a teacher model to a smaller student model. While logit-based knowledge distillation has shown promise, existing methods often overlook the efficient distillation of logits. In this paper, we introduce a novel approach called Class-wise Adaptive Logits Distillation (CALD) based on meta-learning. Our method leverages a meta-network to generate class-adaptive weights, delivering both explicit and implicit knowledge adaptively. By training the meta-network to assign higher weights to specific classes crucial for the student model’s learning from the teacher model, our approach enhances the knowledge transfer process. Experimental results on CIFAR-100 and ImageNet datasets demonstrate that CALD surpasses state-of-the-art knowledge distillation methods, achieving enhanced accuracy and efficiency in transferring knowledge from teacher to student models.

关键词： Metalearning Training Adaptation models Accuracy Signal processing Acoustics Speech processing Knowledge transfer

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LV-Mamba: Integrating Denoising Mechanism with Mamba for Improved Segmentation of the Pediatric Echocardiographic Left Ventricle 5th

LV-Mamba: Integrating Denoising Mechanism with Mamba for Imp...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Chen, Tianxiang Chang, Zeyu Wang, Fangyijie Wang, Ziyang Ye, Zi University of Science and Technology of China Hefei China School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom School of Medicine University College Dublin Dublin Ireland Department of Computer Science University of Oxford Oxford United Kingdom Institute of Intelligent Software Guangzhou China

ISBN: (纸本)9789819638628

In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is crucial since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, causing more difficulty in accurate segmentation. Present methods lack efficiency and are prone to mistakenly segmenting some background noise areas, such as the left ventricular area, due to noise disturbance. We introduce LV-Mamba for efficient pediatric echocardiographic left ventricular segmentation to relieve the two issues. Specifically, we turn to the recently proposed vision mamba layers in our vision mamba encoder branch to improve our model’s computing and memory efficiency while modeling global dependencies. In the other DWT-based PMD encoder branch, we devise DWT-based Perona-Malik Diffusion (PMD) Blocks that utilize PMD for noise suppression while preserving the left ventricle’s local shape cues. Leveraging the strengths of both encoder branches, LV-Mamba achieves superior accuracy and efficiency to established models, such as vision transformers with quadratic and linear computational complexity. This innovative approach promises significant advancements in pediatric cardiac imaging and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pediatrics

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Quantum Reinforcement Learning for Cognitive Radio-Assisted UAV Traffic Offloading

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IEEE Transactions on Vehicular Technology 2025年

作者： He, Ying Huang, Chao Yu, F. Richard Liu, Zhiquan Zhou, Guang Shenzhen University College of Computer Science and Software Engineering China Jinan University China DeepRoute AI Inc. China

With the continuous development of communication industry, the demand for spectrum resources is gradually increasing. Simultaneously, unmanned aerial vehicles (UAVs) have been extensively studied as a means to assist with traffic offloading by establishing wireless links between base stations (BSs) and UAVs. However, the current spectrum resources are limited and far from optimal utilization. In response to this situation, cognitive radio (CR) technology and non-orthogonal multiple access (NOMA) technologies have been proposed to enhance the efficient utilization of spectrum resources. In this paper, we address the challenge of using CR technology to assist UAVs in traffic offloading within specific areas. At each instance, the UAV must consider the allocation of time for data collection, traffic transmission, and UAV charging. Given the dynamic nature of spectrum resources, UAVs must make adaptive decisions based on the current conditions. Finally, we model the aforementioned issues as a quantum reinforcement learning (QRL) system to expedite the training process. In particular, we design a quantum soft actor-critic algorithm employing a variable quantum circuit (VQC) and encode the experience data into a quantum summation tree while transforming it into a quantum priority experience replay buffer. Simulation results demonstrate the superior performance of the proposed framework. © 2025 IEEE.

关键词： Drones

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An influence study of task-irrelevant factors on the neural network accuracy

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Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Liu, Yi Zhang, Changsheng Zhao, Haitong Zhang, Chen Wu, Wenjing Department of Software Engineering Northeastern University Shenyang110167 China Department of Computer Application Technology Northeastern University Shenyang110167 China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

In the field of data-driven deep learning, the dataset quality is one of the essential factors that determine the neural network (NN) accuracy. During the sampling process, the sample may contain many task-irrelevant factors. However, there is a lack of study on the influence of task-irrelevant factors in the dataset on the NN accuracy. This paper analyzes the influence of task-irrelevant factors through mathematical derivation, and finds that regularization cannot solve the problem of task-irrelevant factors affecting the NN inference performance. To reduce the influence of task-irrelevant factors, this paper proposes an idea, making the mathematical expectation of task-irrelevant features in the training set to be zero. Furthermore, this paper proposes a method that can reduce the influence of the background factor on the image classification model. To verify the effectiveness of this method, a series of controlled experiments are conducted on the image classification and object detection task. Experimental results show that this method not only effectively improves the accuracy of the classification model, but also improves the precision of the object detection model. The code related with this paper can be download from https://***/CaedmonLY/BackgroundFactor/tree/master. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Object detection

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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A Meta-Model to Support Compatibility Testing of Cross-Browser Web Application

A Meta-Model to Support Compatibility Testing of Cross-Brows...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Mishal Muneer Uzair Rasheed Muhammad Majid Hussain Department of Computer Science & IT The University of Lahore Lahore Pakistan Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Ensuring the user interface (UI) compatibility of web applications across diverse client-side configurations, including various operating systems and browsers, is a significant challenge due to the extensive range of possible combinations. Traditional tools often struggle to address this complexity, leading to visual inconsistencies and malfunctions. To improve compatibility testing, we propose a meta-model that initiates the process with a checklist covering critical configurations across different browsers. This checklist is then translated into Interaction Flow Modeling Language (IFML) constructs, enabling a model-driven approach to compatibility assessment. By leveraging this checklist-IFML integration, we systematically generate test cases that target compatibility issues more effectively. Our approach is validated through the Laptop-Web Case Study, where we assess compatibility by comparing checklist items with IFML constructs within the case study’s domain model. The results show that our approach enhances the accuracy and efficiency of compatibility testing, addressing a core gap in current methods. This research introduces a structured, model-driven method for compatibility testing, providing a more reliable framework for identifying and mitigating UI compatibility issues in web applications across varying client-side configurations.

关键词： Visualization Translation Flow production systems Computational modeling Operating systems User interfaces Reliability engineering Browsers Complexity theory Testing

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KANOCLIP: ZERO-SHOT ANOMALY DETECTION THROUGH KNOWLEDGE-DRIVEN PROMPT LEARNING AND ENHANCED CROSS-MODAL INTEGRATION

arXiv

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arXiv 2025年

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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