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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Enhanced adaptive data rate strategies for energy-efficient Internet of Things communication in LoRaWAN

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International Journal of Communication Systems 2025年第2期38卷

作者： Ali Lodhi, Muhammad Wang, Lei Mahmood, Khalid Farhad, Arshad Chen, Jenhui Kumari, Saru School of Software Technology Dalian University of Technology Dalian China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science FAST-National University of Computer and Emerging Sciences Islamabad Pakistan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Mathematics Chaudhary Charan Singh University Meerut India School of Chemical Engineering and Physical Sciences Lovely Professional University Jalandhar India

The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy-efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter-based ADR (KF-ADR) and moving median-based ADR (Median-ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. © 2024 John Wiley & Sons Ltd.

关键词： adaptive data rate Internet of Things Kalman filter LoRa LoRaWAN mobility resource allocation

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Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

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IEEE Internet of Things Journal 2025年

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

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A lot streaming scheduling algorithm with variable sub-lots for solving a multi-objective flexible job shop scheduling problem

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Applied Soft Computing 2025年 177卷

作者： Shao, Shuai Xu, Gaochao Li, Jiaxing Meng, Xianqiu Xu, Xu Wang, Haidong Jin, Zhenjun College of Computer Science and Technology Jilin University Chang chun130012 China College of Computer Science and Engineering Changchun University Of Technology Chang chun130012 China

To adapt to the current market change of customized production, a long-distance guided gray wolf optimization algorithm is proposed to solve the lot streaming scheduling problem. Aiming to simulate the real production situation, the problem takes into account the switching time of the jobs as well as the limitation of the number of work equipment. A multi-objective optimization model is also proposed to minimize the maximum completion time and machine idle rate. Secondly, an operation-level encoding and the corresponding adaptive segmented decoding method are designed to improve the search efficiency of the algorithm and effectively reduce the solution space. Moreover, an innovative two-segment hierarchical initialization strategy is designed to ensure the high quality of the initial population, which initializes the workpiece order and the number of machines, respectively. In addition, an improved social hierarchy is proposed to properly guide the wolf pack search direction to avoid falling into local optimization. Finally, the effectiveness of the proposed method is verified with examples of different scales, and the superiority of this method is proved by comparing it with existing methods. © 2025 Elsevier B.V.

关键词： Machine shops

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ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings

arXiv

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arXiv 2025年

作者： Obioma, Peace Agbodike, Obinna Chen, Jenhui Wang, Lei Department of Computer Science University of Manchester M139PL United Kingdom Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Guishan Dist Taoyuan33375 Taiwan iTrustal Department of Computer Science and Information Engineering Chang Gung University Guishan Dist. Taoyuan33302 Taiwan Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system’s performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems. © 2025, CC BY.

关键词： Constrained optimization

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Research on Data stream Mining Algorithm for Frequent Itemsets Based on Sliding Window Model 2

Research on Data stream Mining Algorithm for Frequent Itemse...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Hongmei Wang Fentian Li Dongkai Tang Zeru Wang School of Computer Science and Engineering Chang Chun University of Technology

ISBN: (纸本)9781509036202

With the continuous expansion of the data stream applications,the data stream frequent pattern mining is becoming a hot research topic in the field of data mining,the domestic and foreign scholars put forward a large number of data stream frequent itemsets mining *** paper improves the related definitions of frequent itemsets and sliding windows,classifies sliding windows from data processing models,analyzes the use of sliding windows in typical frequent itemsets mining algorithms,and summarizes the mining techniques and efficiency of typical frequent itemsets mining algorithms.

关键词： data stream frequent itemsets sliding window data processing model

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MetaOcc: Surround-View 4D Radar and Camera Fusion Framework for 3D Occupancy Prediction with Dual Training Strategies

arXiv

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arXiv 2025年

作者： Yang, Long Zheng, Lianqing Ai, Wenjin Liu, Minghao Li, Sen Lin, Qunshu Yan, Shengyu Bai, Jie Ma, Zhixiong Zhu, Xichan School of Automotive Studies Tongji University Shanghai201804 China 2077AI Foundation Singapore Autonomous Driving Development NIO China College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Automobile Chang’an University Shaanxi Xi’an710018 China School of Information and Electrical Engineering Hangzhou City University Hangzhou310015 China

3D occupancy prediction is crucial for autonomous driving perception. Fusion of 4D radar and camera provides a potential solution of robust occupancy prediction on serve weather with least cost. How to achieve effective multi-modal feature fusion and reduce annotation costs remains significant challenges. In this work, we propose MetaOcc, a novel multimodal occupancy prediction framework that fuses surround-view cameras and 4D radar for comprehensive environmental perception. We first design a height self-attention module for effective 3D feature extraction from sparse radar points. Then, a local-global fusion mechanism is proposed to adaptively capture modality contributions while handling spatio-temporal misalignments. Temporal alignment and fusion module is employed to further aggregate historical feature. Furthermore, we develop a semi-supervised training procedure leveraging open-set segmentor and geometric constraints for pseudo-label generation, enabling robust perception with limited annotations. Extensive experiments on OmniHD-Scenes dataset demonstrate that MetaOcc achieves state-of-the-art performance, surpassing previous methods by significant margins. Notably, as the first semi-supervised 4D radar and camera fusion-based occupancy prediction approach, MetaOcc maintains 92.5% of the fully-supervised performance while using only 50% of ground truth annotations, establishing a new benchmark for multi-modal 3D occupancy prediction. Code and data are available at https: //***/LucasYang567/MetaOcc. Copyright © 2025, The Authors. All rights reserved.

关键词： Semi-supervised learning

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Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study

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computers in Biology and Medicine 2025年 190卷 110070-110070页

作者： Chao, Yi-Ping Chuang, Hai-Hua Lee, Zong-Han Huang, Shu-Yi Zhan, Wan-Ting Shyu, Liang-Yu Lo, Yu-Lun Lee, Guo-She Li, Hsueh-Yu Lee, Li-Ang Department of Computer Science and Information Engineering Chang Gung University Taoyuan33302 Taiwan Department of Otorhinolaryngology Head and Neck Surgery Sleep Center Linkou Medical Center Chang Gung Memorial Hospital Chang Gung University Taoyuan33305 Taiwan Department of Community Medicine Cathay General Hospital Taipei10630 Taiwan School of Medicine College of Life Science and Medicine National Tsing Hua University Hsinchu300044 Taiwan Department of Industrial Engineering and Management National Taipei University of Technology Taipei10608 Taiwan Department of Biomedical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Pulmonary and Critical Care Medicine Linkou Main Branch Chang Gung Memorial Hospital Chang Gung University Taoyuan33305 Taiwan Faculty of Medicine National Yang Ming Chiao Tung University Taipei112304 Taiwan Department of Otolaryngology Taipei City Hospital Ren-Ai Branch Taipei106243 Taiwan

This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70–250 Hz) and carotid artery pulsations (0.01–1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings. © 2025 The Authors

关键词： Piezoelectric transducers

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