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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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computer Modeling in engineering & sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Text-Region Matching for Multi-Label Image Recognition with Missing Labels 24

Text-Region Matching for Multi-Label Image Recognition with ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Leilei Xie, Hongxing Wang, Lei Fu, Yanping Sun, Dengdi Zhao, Haifeng School of Computer Science and Technology Anhui University Anhui Hefei China School of Artificial Intelligence Anhui University Anhui Hefei China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400706868

Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing labels, leveraging VLP prompt-tuning technology. However, they usually cannot match text and vision features well, due to complicated semantics gaps and missing labels in a multi-label image. To tackle this challenge, we propose Text-Region Matching for optimizing Multi-Label prompt tuning, namely TRM-ML, a novel method for enhancing meaningful cross-modal matching. Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual representations in a one-to-one matching manner. Concurrently, we further introduce multimodal contrastive learning to narrow the semantic gap between textual and visual modalities and establish intra-class and inter-class relationships. Additionally, to deal with missing labels, we propose a multimodal category prototype that leverages intra- and inter-category semantic relationships to estimate unknown labels, facilitating pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC, Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art methods by a significant margin. Our code is available here. © 2024 ACM.

关键词： Semantics

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Enhancing Radiology Report Interpretation with Large-Scale Language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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Adaptive Anomaly Detection Using Deep Bi-LSTM and Self-Adaptive Pufferfish Optimization 4

Adaptive Anomaly Detection Using Deep Bi-LSTM and Self-Adapt...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kartheesan, L. Sam Kumar, G.V. Krishnan, Sundara Rajulu Navaneetha Tamilvizhi, T. Surendran, R. Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Trichy Samayapuram Campus Tiruchirappalli India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

The task of anomaly detection has become critical in many applications, including network security, fraud detection, and fault diagnosis. Traditionally, most of the existing methods fail with high-dimensional data, making it difficult and challenging to get accurate anomaly detection with low false positives. The main techniques proposed so far may also face problems in being computationally costly and not adaptive to the variability of the data characteristics. Therefore, there is an urgent need to design a well-built, efficient, and adaptive framework that properly identifies anomalies within large volumes of complex datasets while optimizing the selection of features. To solve these issues, this paper proposes a novel advanced preprocessing, feature extraction, feature selection, and classification methods integrated into the anomaly detection framework. Initially, the dataset undergoes data cleaning, normalization, and standardization to ensure uniformity in scale and distribution. Then it will extract statistical features that encapsulate critical insights from the raw data. For the detection stage, we propose Deep BiLstm, a hybrid deep learning architecture that leverages the strengths of Bi-Lstm by using lightweight densely connected layers for efficient anomaly classification. The hyperparameter of the Deep Bi-Lstm model is fine-tuned using the Self-Adaptive Pufferfish Optimization Algorithm (SAPFOA). The entire framework is implemented using the Python tool. Evaluate the effectiveness of our approach over the Ton-IoT dataset, which is the most frequently used benchmark of network intrusion and anomaly detection. © 2024 IEEE.

关键词： Network security

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CPL-SLAM: Centralized Collaborative Multirobot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Carleton University School of Information Technology Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Traditional visual-inertial Simultaneous Localization and Mapping (SLAM) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues, such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intrarobot and interrobot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Self-supervised extracted contrast network for facial expression recognition

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Multimedia Tools and Applications 2025年第15期84卷 14977-14996页

作者： Yan, Lingyu Yang, Jinquan Xia, Jinyao Gao, Rong Zhang, Li Wan, Jun Tang, Yuanyan School of Computer Science Hubei University of Technology Wuhan430068 China Department of Computer Science Royal Holloway University of London SurreyTW20 0EX United Kingdom School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Zhuhai UM Science and Technology Research Institute Zhuhai519085 China

Self-supervised Contrastive learning has recently demonstrated significant performance in Facial Expression Recognition (FER). However, existing methods fail to address inherent challenges such as similar and blurred facial expressions, and low-quality semantic pairs from image cropping. To tackle these challenges, this paper proposes a Lightweight Self-supervised Distilled Contrastive network for FER (LSDC-FER), which consists of a center suppressed cropping module and a multi-scale feature joint distillation module. The former improves the quality of face expression semantic pairs by face image localization and center suppressed cropping. The latter learns fine-grained features using knowledge distillation and explores multi-scale contextual information to improve feature learning. Experimental results show that LSDC-FER achieves 75.65% and 61.16% recognition accuracy on RAF-DB and FER-2013 public facial expression datasets, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distillation

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A Multi-Angle MIMO-SAR Fusion Imaging Method Based on Improved Range Migration Algorithm and Improved Geographic Information Scale Invariant Feature Transform Algorithm

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13938-13949页

作者： Zhou, Fang Jiao, Zhikuan Shen, Guoqing Xing, Mengdao Gashinova, Marina Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China University of Birmingham School of Engineering United Kingdom Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

In order to resolve the conflict between high pulse repetition frequency (PRF) and wide-swath imaging in multi-angle synthetic aperture radar (SAR), a multi-angle imaging model combined with multiple-input multiple-output synthetic aperture radar (MIMO-SAR) is proposed. The model utilizes more freedom in azimuth degrees provided by MIMO-SAR to remove azimuth ambiguity caused by low PRF, and successfully solves that contradiction. At the same time, considering the problem of low utilization of spectrum in multi-angle imaging SAR, an improved range migration algorithm (ImRMA) is proposed, which can effectively increase the utilization of the spectrum for data processing. Finally, an improved method of geographic information-scale invariant feature transform (ImGI-SIFT) is used to achieve the efficiency fusion of multi-angle imaging results. Simulation and real data experiments show that the effectiveness and superiority of the proposed imaging method, and the precision of the proposed fusion method. © 2008-2012 IEEE.

关键词： Data reduction

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An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

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Annals of Data science 2024年第5期11卷 1525-1544页

作者： Zubair, Md. Iqbal, MD. Asif Shil, Avijeet Chowdhury, M.J.M. Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Department of Computer Science and Information Technology La Trobe University Victoria3086 Australia School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD4072 Australia

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Principal component analysis

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AI-Enabled Swarm Clustering for UAVs-Aided IoT Data Acquisition

AI-Enabled Swarm Clustering for UAVs-Aided IoT Data Acquisit...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Truong, Thanh Phung Do, Tung Son Oh, Junsuk Won, Dongwook Nguyen, The Vi Lee, Chunghyun Cho, Sungrae School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798331507022

This paper presents a novel artificial intelligence (AI)-enabled framework for optimizing data acquisition in Unmanned Aerial Vehicle (UAV)-aided Internet of Things (IoT) networks through intelligent swarm clustering. The proposed framework addresses the challenge of efficient data collection from distributed IoT devices (IoTDs) by employing a two-stage optimization approach: a K-Means-based clustering algorithm for IoTD association and a data-weighted centroid optimization strategy for UAV positioning. We introduce a dynamic clustering mechanism that considers spatial distribution and data volume heterogeneity of IoTDs to determine optimal UAV hovering locations. Extensive simulations demonstrate the superior per-formance of our proposed framework compared to conventional approaches. Results show that our method achieves up to 41.5% reduction in data collection time compared to random clustering with greedy UAV positioning (RC-GU) and maintains consistent performance advantages across various transmission power levels. The framework exhibits particular efficiency in low-power scenarios, making it especially suitable for energy-constrained IoT environments. This work advances intelligent UAV swarm management and provides a promising solution for large-scale IoT data acquisition scenarios. © 2025 IEEE.

关键词： Drones

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Reinforcement Learning-Based Backhaul Routing for APs in Cell-Free Massive MIMO Networks

Reinforcement Learning-Based Backhaul Routing for APs in Cel...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Seo, Junho Saad, Malik Muhammad Ajmal, Mahnoor Siddiqa, Ayesha Jeong, Bomi Kim, Su Kim, Dongkyun School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9798331507022

The integration of Distributed Access Points (APs) in cell-free massive MIMO networks poses challenges for efficient routing and parent selection, particularly in scenarios involving dense AP deployment. This paper introduces a reinforcement learning-based approach for optimal parent selection in Destination Oriented Directed Acyclic Graph (DODAG) based routing within cell-free massive MIMO networks. Unlike traditional routing protocols that rely heavily on static or computationally intensive methods, our framework enables each AP to act as an autonomous agent, dynamically selecting parent nodes based on throughput, congestion, hop count, and link stability. We model the problem using Q-learning, where each AP learns an optimal parent selection policy through interactions with the network. Simulation results demonstrate the proposed scheme's ability to maintain high throughput and packet delivery ratios even in dense network environments. The framework achieves an average throughput of 14.9 Mbps and a packet delivery ratio of 79% for a 50-node network, effectively addressing the challenges of interference and congestion. This work highlights the potential of reinforcement learning to enhance the scalability and performance of cell-free networks, paving the way for their application in future 6G communication systems. © 2025 IEEE.

关键词： Reinforcement learning

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