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arXiv 2025年

作者： Wei, Quanmin Dai, Penglin Li, Wei Liu, Bingyi Wu, Xiao School of Computing and Artificial Intelligence Southwest Jiaotong University China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

Multi-agent collaborative perception is expected to significantly improve perception performance by overcoming the limitations of single-agent perception through exchanging complementary information. However, training a robust collaborative perception model requires collecting sufficient training data that covers all possible collaboration scenarios, which is impractical due to intolerable deployment costs. Hence, the trained model is not robust against new traffic scenarios with inconsistent data distribution and fundamentally restricts its real-world applicability. Further, existing methods, such as domain adaptation, have mitigated this issue by exposing the deployment data during the training stage but incur a high training cost, which is infeasible for resource-constrained agents. In this paper, we propose a Parameter-Efficient Fine-Tuning-based lightweight framework, CoPEFT, for fast adapting a trained collaborative perception model to new deployment environments under low-cost conditions. CoPEFT develops a Collaboration Adapter and Agent Prompt to perform macro-level and micro-level adaptations separately. Specifically, the Collaboration Adapter utilizes the inherent knowledge from training data and limited deployment data to adapt the feature map to new data distribution. The Agent Prompt further enhances the Collaboration Adapter by inserting fine-grained contextual information about the environment. Extensive experiments demonstrate that our CoPEFT surpasses existing methods with less than 1% trainable parameters, proving the effectiveness and efficiency of our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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Capacity Optimization and Economic Analysis of Off-grid Wind-solar Hydrogen Storage System

Capacity Optimization and Economic Analysis of Off-grid Wind...

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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Zirong Cheng Jingjie Huang Fan Huang Zhuoyan Zhou State Key Laboratory of Disaster Prevention & Reduction for Power Grid Changsha University of Science and Technology Changsha China School of Electrical and Computer Engineering The University of Sydney Australia Sydney Australia

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

To enhance the economic efficiency and operational stability of off-grid wind-solar hydrogen production systems, a novel capacity configuration method is proposed. This method optimizes system equipment capacity in an integrated wind-solar-hydrogen system with the objective of maximizing system revenue. A case study is conducted using data from a wind-solar hydrogen production project in a specific region, where three schemeswind energy, hydrogen production, wind-battery hydrogen production, and wind-solar-battery hydrogen production-are designed and compared. The analysis reveals that, under the given wind and solar conditions, the system achieves maximum benefit when the wind power to photovoltaic installation ratio is 2.75:1. Additionally, energy storage effectively mitigates fluctuations in new energy output. The study also examines the cost composition of the wind-solar hydrogen storage system and the impact of equipment costs on the unit hydrogen production cost. The findings show that the cost of wind turbine equipment significantly influences the unit hydrogen production cost. A 50% reduction in wind turbine costs corresponds to a $4.9 \mathrm{CNY} / \mathrm{kg}$ increase in hydrogen price.

关键词： Photovoltaic systems Production systems Fans Costs Fluctuations Hydrogen storage Hydrogen Wind power generation Stability analysis Wind turbines

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Real-Time Object Detection Using Convolutional Neural Networks for Autonomous Vehicles 3

Real-Time Object Detection Using Convolutional Neural Networ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Premalatha, K.P. Divyarani, M.S. Chaudhary, Pavan Dev, Anoop Patra, Narayan Ghosal, Shreya Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India

ISBN: (纸本)9798331542375

This is where timely and accurate detection by autonomous cars becomes vital. Due to this, Convolution Neural Networks have gained the utmost popularity in object detection and are showing great potential in performing all sorts of sort's r vision tasks. In this project, we propose a Convolution Neural Networks based real-time object detection system for autonomous vehicles. Our system detects and localizes real-time objects based on deep neural network architecture. The function of our software is to take an image from the sensors, like cameras or Liar, on board this car. This image is further input to CNN, which takes these features, extracts the object, and classifies them into a scene. As we previously mentioned, CNN is trained with a large dataset of labeled images, allowing it to recognize hundreds or thousands of different objects accurately. This is then computed further in the CNN and attempts to find bounding boxes around detected objects, giving where that object exists spatially in a scene. Our system's response time has been designed for real-time analysis and should be able to analyze images and detect objects in about milliseconds. This, in return, enables autonomous vehicles to make immediate decisions when considering these objects and navigate safely. Our system allows for real-time object detection, opening the door to high-performance and safe autonomous vehicles that bring us closer than ever before to achieving full autonomy driving. © 2025 IEEE.

关键词： Vehicle detection

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BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction

arXiv

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arXiv 2025年

作者： Catak, Ferhat Ozgur Kuzlu, Murat Cali, Umit Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States University of York School of Physics Engineering and Technology Heslington YorkYO10 5DD United Kingdom

Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communication system in terms of capacity, spectral, and energy efficiency. The performance of MIMO systems is highly dependent on the quality of channel state information (CSI). Predicting CSI is, therefore, essential for improving communication system performance, particularly in MIMO systems, since it represents key characteristics of a wireless channel, including propagation, fading, scattering, and path loss. This study proposes a foundation model inspired by BERT, called BERT4MIMO, which is specifically designed to process high-dimensional CSI data from massive MIMO systems. BERT4MIMO offers superior performance in reconstructing CSI under varying mobility scenarios and channel conditions through deep learning and attention mechanisms. The experimental results demonstrate the effectiveness of BERT4MIMO in a variety of wireless environments. © 2025, CC BY.

关键词： Fading channels

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A portable,low-cost lactate measurement system

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Journal of Measurement science and Instrumentation 2025年第1期16卷 37-46页

作者： WANG Qingqing PAN Yu YU Chuanxin WANG Yanyan ZHANG Kaikai LIU Sheng School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000China Anhui Provincial Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei 235000China

Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone *** experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was *** modified lactate sensors were used for CA testing of *** the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed *** lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.

关键词： lactate measurement portable device embedded development lactate sensor electrochemical analysis

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Structured Process on FL for Big Data Analysis

Structured Process on FL for Big Data Analysis

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Anitha Govindaram J. S. Prasath K. Jayasakthi N. Rajkumar G. Porkodi Jose Anand A Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Bengaluru Karnataka Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The integration of the Internet of Things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated Learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. This paves the way for a wide variety of intelligent IoT applications to be developed. This article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. These include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015–2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT.

关键词： Industries Data privacy Federated learning Smart healthcare Memory Collaboration Internet of Things Informatics Mobile computing Convergence

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Surgical Video Phase Recognition Model Integrating Phase Localization Module and Temporal Context Method

Surgical Video Phase Recognition Model Integrating Phase Loc...

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Sensors and Information technology (ICSI), International Conference on

作者： YiWen Xia Shuang Song Yong Yang Tong Lu Ruoxiu Xiao School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Changzhou Visual Star MedTech Co. Ltd Changzhou China Visual 3D Medical Science and Technology Development Co. Ltd Beijing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Surgical phase recognition plays a crucial role in computer - assisted surgery systems. Its main purpose is to identify which stage of the surgery the video frames represent. In recent years, remarkable progress has been made in identifying surgical phases using deep - learning methods. However, existing methods mainly focus on improving the accuracy and precision of phase recognition, often neglecting the semantic consistency and temporal continuity of video frames within a phase. This easily leads to prediction errors for blurred and similar frames within a phase, posing challenges to reliable phase recognition. To overcome this problem, we propose a novel method for surgical video phase recognition and construct a temporal model combined with surgical phase localization. By predicting the start and end time points of surgical phases, the model improves the prediction consistency within surgical phases. The model consists of a two - stage training process. In the first stage, the spatial features of the video frame sequence are extracted. In the second stage, a temporal network is first used to perform frame-by-frame surgical phase prediction and surgical phase localization respectively. Finally, the prediction results of frame-by-frame surgical phases and the results of phase localization are fused to generate the final prediction information. This method effectively addresses the problem of intra - segment inconsistency in prediction results and significantly improves the accuracy and reliability of surgical phase recognition.

关键词： Location awareness Training Accuracy Computational modeling Surgery Predictive models Sensor fusion Feature extraction Sensors Reliability

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A Study on Educational Data Analysis and Personalized Feedback Report Generation Based on Tags and ChatGPT

arXiv

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arXiv 2025年

作者： Zhou, Yizhou Zhang, Mengqiao Jiang, Yuan-Hao Gao, Xinyu Liu, Naijie Jiang, Bo School of Design and Engineering National University of Singapore Singapore Lab for Artificial Intelligence for Education East China Normal University China Shanghai Institute of Artificial Intelligence for Education East China Normal University China School of Computer Science and Technology East China Normal University China National Institute of Education Nanyang Technological University Singapore College of Education Science and Technology Zhejiang University of Technology China School of Management Science and Engineering Shandong University of Finance and Economics China

This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored prompts to deliver constructive feedback that encourages rather than discourages students. This methodology focuses on accurately feeding student data into large language models and crafting prompts that enhance the constructive nature of feedback. The effectiveness of this approach was validated through surveys conducted with over 20 mathematics teachers, who confirmed the reliability of the generated reports. This method can be seamlessly integrated into intelligent adaptive learning systems or provided as a tool to significantly reduce the workload of teachers, providing accurate and timely feedback to students. By transforming raw educational data into interpretable tags, this method supports the provision of efficient and timely personalized learning feedback that offers constructive suggestions tailored to individual learner needs. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Robust Semi-Supervised Deep Nonnegative Matrix Factorization with Constraint Propagation for Data Representation

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IEEE Transactions on Big Data 2025年

作者： Peng, Siyuan Yin, Jingxing Yang, Zhijing Nie, Feiping Chen, Badong Guangdong University of Technology School of Information Engineering 510006 China Northwestern Polytechnical University School of Computer Science Center for OPTical IMagery Analysis and Learning Xi'an710072 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China

Deep nonnegative matrix factorization (DNMF) technique has attached much great attention in recent year, since it can effectively discover the underlying hierarchical structure of complex data. However, most existing unsupervised and semi-supervised DNMF approaches not only suffer from the noisy data seriously, but also fail to enhance the decomposition quality of DNMF obviously by using the obtained supervisory information. To overcome these drawbacks, a robust semi-supervised DNMF method, called the correntropy based semi-supervised DNMF with constraint propagation (CSDCP), is proposed in this paper for learning a compact and meaningful data representation from the original data. Particularly, instead of adopting the traditional Frobenius norm, CSDCP employs the nonlinear and local similarity measure (e.g., correntropy) as the loss function in DNMF to enhance the robustness of DNMF for the noisy data. In addition, the hypergraph based constraint propagation (HCP) algorithm is adopted in CSDCP to exploit the limited supervisory information fully for capturing good data representation. Moreover, algorithm analysis of CSDCP is presented in this paper, including convergence analysis, robustness analysis supervised information analysis, and computational complexity. Extensive experimental results have illustrated that, in comparison to the most related DNMF approaches, CSDCP usually has better clustering results on six nonnegative datasets in clustering tasks. © 2015 IEEE.

关键词： Self-supervised learning

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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： D. Santhakumar Gopalakrishnan Soundararajan Suman Chintala E. Bharath Krishna Murthy Inumula Kuldeep Chouhan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam – Chennai Tamil Nadu Muscat College Ruwi Sultanate of Oman Business Intelligence Architect USA Department of Artificial Intelligence & Data Science C K College of Engineering & Technology Anna University Cuddalore Tamil Nadu Symbiosis Institute of International Business (SIIB) Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality.

关键词： Supply chain management Costs Accuracy Supply chains Demand forecasting Machine learning Predictive models Generative adversarial networks Standards Genetic algorithms

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