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Privacy-Preserving Publishing Mechanism for Real-Time Vehicle Trajectory in Vehicle-to-Grid Networks 5

Privacy-Preserving Publishing Mechanism for Real-Time Vehicl...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Wang, Yulong Xu, Jianbo Gong, Bo School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331533694

The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirectional energy exchange with the grid system. As distributed energy storage units within the V2G network, MESVs help meet the grid's load-balancing demands. To accurately schedule grid loads, V2G networks rely on MESVs' real-time trajectory data. After collecting this data, the central authority (CA) publishes it to the grid enterprise (GM) for load-balancing decisions. However, before publication, the CA must process the data to protect the location privacy of MESVs and prevent GM from extracting sensitive information. To address this privacy protection challenge during data publication, we propose a differential privacy-based mechanism. Our mechanism uses the unscented Kalman filter to predict and correct the published data at the sampling point and intelligently adjusts the sampling interval. Additionally, we introduce a privacy budget allocation method based on regional access frequency and travel time, enhancing privacy protection of trajectory in high-access areas and peak hours. Through security analysis and experimental validation, our mechanism effectively preserves the privacy of real-time vehicle trajectory data while ensuring data validity. © 2025 ieee.

关键词： Vehicle-to-grid

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Leveraging Immersive Reality and LLMs for Social Reintegration: AI-Driven Skill development in Correctional Rehabilitation 7

Leveraging Immersive Reality and LLMs for Social Reintegrati...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： De Oliveira, Elisa Ayumi Masasi De Freitas Cintra, Luiza Martins Barbosa, Gustavo Higino Webster Sousa, Rafael Teixeira Filho, Arlindo Rodrigues Galvão Advanced Knowledge Center in Immersive Technology Akcit Goiânia Brazil Advanced Knowledge Center in Immersive Technology Akcit Recife Brazil Federal University of Mato Grosso Advanced Knowledge Center in Immersive Technology Akcit Barra do Garças Brazil

ISBN: (纸本)9798331521578

This paper explores the use of immersive technolo-gies and AI-driven virtual interactions to aid the social reintegration of incarcerated individuals. By integrating a ChatGPT-based AI within a virtual reality framework, the developed application offers interactive, realistic simulations aimed at fostering essential skills, such as effective communication and vocational abilities. Users interact with a lifelike avatar, designed to simulate scenarios like job interviews and interpersonal dialogues, promoting empathy, self-confidence, and behavioral adaptability. Initial assessments indicate that the application effectively supports engaging and empathetic interactions, which are crucial for preparing individuals for reintegration into society. The application will be open-access, allowing further refinement and adaptability by experts and developers, ultimately aiming to reduce recidivism and support a more humane approach to correctional rehabilitation through technological advancement. © 2025 ieee.

关键词： Virtual reality

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A Systematic Review: development of AI Based computer Vision Scrap Sorting System for Metal Scrap

A Systematic Review: Development of AI Based Computer Vision...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Gedam, Pragati B. Khan, Atiya Purohit, Neha Jha, V.K. G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India Jawaharlal Nehru Aluminium Research Development and Design Centre Department of Aluminium Electrolysum Nagpur India

ISBN: (纸本)9798331509828

The metals recycling industry has undergone a revolution thanks to the quick development of computer vision and artificial intelligence (AI), which have made it possible to sort metal trash using efficient and automated techniques. The existence of pollutants, the requirement for high-speed processing without sacrificing precision, and variations in scrap form, size, and texture are some of the ongoing difficulties. Though they provide strong feature extraction capabilities, current methods like Convolutional Neural Networks (CNNs), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) frequently necessitate substantial preprocessing and sizable, precisely labeled datasets in order to integrate them with classification models like Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Furthermore, scalability and resilience in real-world scenarios continue to be major challenges. An AI-driven computer vision system is proposed in this work for identifying and sorting different kinds of metal trash, such as alloys made of copper, iron, and aluminium. Utilizing sophisticated feature extraction techniques and combining SVM and ANN models, the system seeks to maximize classification performance while tackling issues with operational effectiveness and dataset variability. YOLO LabelImg makes annotations easier by producing accurate bounding boxes, which are essential for tasks involving object identification and categorization. The study emphasizes the value of integrating models to increase accuracy and investigates how AI-powered solutions might raise the efficiency, dependability, and affordability of applications for sorting metal waste. © 2025 ieee.

关键词： Convolutional neural networks

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HDXpose: Harnessing Hyperdimensional Computing’s Explainability for Adversarial Attacks 24

HDXpose: Harnessing Hyperdimensional Computing’s Explainabi...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Asgarinejad, Fatemeh Pozina, Flavio Gungor, Onat Rosing, Tajana Aksanli, Baris Department of Computer Science and Engineering UC San Diego La Jolla CA92093 United States Department of Electrical and Computer Engineering SDSU CA92182 United States

ISBN: (纸本)9798400710773

Hyperdimensional Computing (HDC), a promising alternative to address the limitations of edge devices, is not exempt from the security challenges confronted by machine learning algorithms, in particular, adversarial attacks. The limited body of research exploring the security implications of HDC overlooks its inherent algorithm. In this paper, we propose a novel and effective adversarial attack technique targeting HDC. Our approach analyzes and prioritizes the impact of input features as well as encoded elements on decision boundaries and perturbs the input towards incorrect decisions in a guided manner. We evaluate our method on different datasets and attack models (i.e., untargeted/targeted, white-box/gray-box). Experimental results indicate that our proposed design, HDXpose, significantly outperforms the state-of-the-art attack techniques by achieving higher success rate with smaller distortion and execution time, rendering its efficacy for real-time attack generation. © 2024 Copyright is held by the owner/author(s).

关键词： Adversarial machine learning

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Advanced Facial Emotion Classification with 135 Classes for Enhanced Cybersecurity Applications 4

Advanced Facial Emotion Classification with 135 Classes for ...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Powers, Gregory Jin, Aobo Tonmoy, Abdul Basit Cao, Haowei Gohel, Hardik Deng, Qixin Wabash College Mathematics and Computer Science Crawfordsville United States University of Houston Victoria Computer Science Victoria United States Johns Hopkins University Electrical and Computer Engineering Baltimore United States

ISBN: (纸本)9798331518882

A key component of behavior analysis and human-computer interaction (HCI) is facial expression detection, which helps systems understand and react to human emotions more effectively and nuancedly. While previous research predominantly focuses on classifying seven or eight primary emotions, there has been limited exploration into more detailed and varied emotional expressions. In this paper, we address this gap by utilizing a recently published dataset that includes 135 distinct emotional classes, significantly increasing the complexity and granularity of emotion recognition tasks. To tackle this challenge, we propose a novel framework that combines U-Net and ResNet architectures, leveraging the strengths of each to capture intricate emotional cues and spatial features. Our approach achieves higher accuracy than current state-of-the-art (SOTA) methods, demonstrating its effectiveness for high-dimensional emotion classification in complex, real-world applications. © 2025 ieee.

关键词： computer vision deep learning facial emotion recognition

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research on Question Answering System of Intelligent Robot Based on Deep Learning 5

Research on Question Answering System of Intelligent Robot B...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Dexing, Bian Wenjing, Wang Honghao, Bai Tianjin Richsoft Electric Power Information Technology Co. Ltd Digital Applications Business Unit Tianjin China

ISBN: (纸本)9798331533694

The preset function modules of intelligent customer service robot question-answering system are usually executed on the basis of the imported question-answering objectives, and the question-answering has deviation, which leads to the stable recall rate. Therefore, the design and research of intelligent customer service robot question-answering system based on deep learning are proposed. According to the current measurement, we access the central processing unit, set up DM368 memory control and voice circuit, and realize the design of system hardware;Combined with deep learning technology, the question-and-answer deviation is controlled, and the question-and-answer function module of customer service robot is designed, and the application connection with dynamic switching database is established to realize the design of system software. The system test shows that: For randomly selected test groups, in the context of 6 time periods, the deep learning intelligent customer service robot QA system designed this time has a faster task processing speed and a higher recall rate, which shows that the system itself has strong pertinence and stability, and the QA processing efficiency is obviously improved, which has practical application value and innovative significance. © 2025 ieee.

关键词： Sales

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research on Mango Ripeness Based on Yolo 13

Research on Mango Ripeness Based on Yolo

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Qiao, Tiezheng Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Mangoes are a crucial agricultural crop in southern China, especially in the Guangxi region, where their production significantly contributes to the local economy. However, chal¬lenges in ripeness identification lead to quality control issues in storage, transportation, and sales. This research proposes a detection method for mango ripeness based on an improved YOLOv5 model. Traditional fruit ripeness detection methods have certain limitations in terms of accuracy and efficiency. To improve detection accuracy, this research replaces the backbone network of the YOLOv5 model with ResNet50 and introduces the Coordinate Attention (CA) mechanism to enhance the models feature extraction ability and the accuracy of ripeness judgment. Experimental results show that the improved model demonstrates higher precision and robustness in detecting mangoes at different stages of ripeness. The research results indicate that the improved YOLOv5 model can effectively identify and distinguish mango ripeness, providing technical support for fruit quality manage¬ment and harvesting in the agricultural field. © 2025 ieee.

关键词： Fruits

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Artificial Intelligence-Powered Internet of Things for Farming and Plant research 3

Artificial Intelligence-Powered Internet of Things for Farmi...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Padma, A. Suresh, P. Narsimlu, G. Amarnath, M. Sreedhar, B.R. Chaitanya Bharathi Institute of Technology Department of Mathematics India

ISBN: (纸本)9798331509675

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring. © 2025 ieee.

关键词： Smart agriculture

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research on Efficient development and Management Methods for Satellite Models 24

Research on Efficient Development and Management Methods for...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Du, Kunmei Han, Xiaohong Li, Yingjie Liang, Ting Xiong, Jing CAST-Xi’an Institute of Space Radio Technology Shaanxi Xi’an China

ISBN: (纸本)9798400710834

As a new type of space infrastructure, a specific satellite model has achieved the interconnection of information networks. It is an important symbol of our China’s construction of a strong aerospace and network power. To meet the needs of this model of satellite for high-density delivery, mass production, and low-cost development, and to adapt to the transformation of user needs from customization to mass production, this paper analyzes the existing inefficient scientific research and production model of the the CAST-Xi’an Institute of Space Radio Technology, pointing out the existing problems. It proposes establishing a large-scale scientific research and production model and its management ideas and framework, and describes in detail the specific content of large-scale work. Practice has shown that this large-scale scientific research and production model is an effective development and management method. It has successfully solved the challenges of low cost, short cycle, and high-density launch, effectively promoted the progress of scientific research and production tasks, and provided a reference for similar tasks in the future. © 2024 Copyright held by the owner/author(s).

关键词： research and development management

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Real-Time Object Tracking Algorithm Based on Attention Mechanism 5

Real-Time Object Tracking Algorithm Based on Attention Mecha...

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5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Zhang, Bin Chen, Zhenyu China Electronics Technology Group Corporation 20th Research Institute Xi'an China

ISBN: (纸本)9798331533694

In this paper, we delve into real-time object tracking algorithms grounded on the attention mechanism. Moreover, we present an innovative spatio-temporal attention mechanism (PRO-AM) to enhance the performance of extant algorithms. In the experiment, the large-scale dataset GOT-10K was employed, and a series of ablation experiments were designed to evaluate the influence of different configurations on the performance of the algorithm. The results suggest that the integration of the temporal attention module and the spatial attention module significantly enhances the algorithm's accuracy and robustness in complex environments. The temporal attention module enables the model to better comprehend the temporal information of the target, whereas the spatial attention module effectively subdues the background noise and accentuates the target area. Additionally, the experimental results demonstrate that the appropriate quantity of dynamic templates and update interval settings can yield the optimal tracking effect. This not only sustains the real-time performance of the algorithm but also elevates the tracking accuracy. This research offers novel ideas and technical backing for the realm of real-time object tracking, thereby facilitating the further advancement of this field. © 2025 ieee.

关键词： Target tracking

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