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共 5994 条记录，以下是21-30 订阅

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. the idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. the system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. the findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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Hybrid Demand Forecasting Model for Big Marts using Seasonal Decomposition, ARIMA, and Gradient Boosting Regressor 6

Hybrid Demand Forecasting Model for Big Marts using Seasonal...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Balaji, S. Manikavelan, D. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai602 105 India

ISBN: (纸本)9798331522667

Demand forecasting is a pivotal factor of the retail enterprise, permitting businesses to successfully optimize stock management and meet client demand. this paper affords a hybrid model that integrates 3 key methodologies—Seasonal Decomposition (SD), Auto Regressive integrated shifting common (ARIMA), and Gradient Boosting Regressor (GBR)—to decorate forecasting accuracy for large marts. the version is constructed on ancient income facts, where Seasonal Decomposition first separates the time collection into seasonal, trend, and residual components, revealing underlying patterns. ARIMA is then implemented to the detrended records, accounting for time-dependent autocorrelations and shifting averages, while GBR is hired to model and predict the residuals, thereby refining the overall forecast. the performance of the SD-ARIMA-GBR version is empirically tested on income statistics from 1559 products throughout 10 stores, yielding an impressively low implied Mean Absolute percentage error (MAPE) of 3.0%. When in comparison to standard models like ARIMA and Exponential Smoothing, the hybrid technique proves substantially extra correct. the combination of machine mastering, especially GBR, performs a crucial function in improving predictive precision. this complete version gives huge marts an effective tool for awaiting calls for fluctuations, using more accurate stock-making plans, and enhancing operational efficiency. © 2025 IEEE.

关键词： Inventory control

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Luminance Correction based Countermeasure against Adversarial Examples 24

Luminance Correction based Countermeasure against Adversaria...

引用

6th international conference on Information Technology and Computer Communications, ITCC 2024

作者： Tojo, Takumi Kumagai, Ryo Takemoto, Shu Nozaki, Yusuke Yoshikawa, Masaya Graduate School of Science and Technology Meijo University Nagoya Japan Faculty of Informaton Engineering Meijo University Nagoya Japan

ISBN: (纸本)9798400717789

Recently, many kinds of deep learning-based image classification are introduced in various fields, such as face recognition for security authentication and autonomous driving, in order to improve their accuracy. However, previous studies have pointed out the vulnerability of them against Adversarial Examples (AE) which are able to cause misclassification intentionally. the AE utilize micro-perturbations which cannot be detected by the naked eye for causing misclassification. For practical use of the deep learning-based image classification, countermeasure against AE is a mandatory requirement. therefore, this paper proposes countermeasures of the deep learning-based image classification against AE. the proposed method introduces luminance correction to eliminate the effect of perturbations on AE. After the proposed luminance correction, the deep learning-based image classification classifies images which are contained perturbations. Experiments prove the validity of the proposed method. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Hue and Saturation oriented Imperceptible Adversarial Examples and their Evaluation 24

Hue and Saturation oriented Imperceptible Adversarial Exampl...

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6th international conference on Information Technology and Computer Communications, ITCC 2024

作者： Kumagai, Ryo Takemoto, Shu Nozaki, Yusuke Yoshikawa, Masaya Graduate School of Science and Technology Meijo University Nagoya Japan Faculty of Informaton Engineering Meijo University Nagoya Japan

ISBN: (纸本)9798400717789

With the advancement of machine learning technology, AI can be applied to various systems such as autonomous driving and medical diagnosis. On the other hand, the vulnerability of AI system is pointed out. the adversarial examples (AEs), which causes incorrect decisions by AI, is one of the threats for AI. For using AI safely, AEs has to be investigated thoroughly. In order to evaluate threat of AEs, this paper proposes the generating method for AEs which is based on hue and saturation property of the image. the proposed method which is based on the conventional gradient-based method, introduces constraints for projection in the HSV color space. the proposed method utilizes properties of human vision which is insensitive to changes in color and sensitive to changes in brightness. these properties contribute imperceptibility of AEs. Experiments revealed the vulnerability of DNNs against imperceptible AEs and clarified the importance of countermeasures. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Quantum-Driven Hybrid Network Architecture Leveraging 5G for High-Speed and Resilient Data Transmission 6

Quantum-Driven Hybrid Network Architecture Leveraging 5G for...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, Kusuma Ashok Mohandas, R. Suneel, Sajja Mahalakshmi, M.R. Kumar, Avinash Singh, Manmohan Department of Physics B V Raju Institute of Technology Medak District Telangana state Narsapur502313 India Chennai Institute of Technology Chennai India Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Department of ECE Sri Muthukumaran Institute of Technology Tamil Nadu Chennai600 069 India Department of Computer Science and Engineering IES College of Technology Bhopal India

ISBN: (纸本)9798331522667

the integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. this paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. the architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. Simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). the proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. this work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks. © 2025 IEEE.

关键词： Quantum communication

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computing Performance Analysis of IEC 61499 Distributed automation Systems 26

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th international conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

this work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. these results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 IEEE.

关键词： Parallel processing systems

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Autonomous and Adaptive Communications Systems for Smart Home Applications 5th

Autonomous and Adaptive Communications Systems for Smart Hom...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Krishnamoorthy, Ramkumar Das, Neeraj Sharma, Khushboo Jindal, Kanika Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819780426

Smart home packages have become more and more standard in nowadays society, giving customers the potential to without problems connect and control various devices within their domestic. Self-sustaining and adaptive communication structures are crucial for smart home packages, allowing seamless integration of these devices and efficient electricity intake. Self-sufficient and adaptive verbal exchange structures for smart home applications. It examines exclusive existing methods for clever domestic automation as well as discusses techniques inclusive of dispensed computing, simulated annealing, reinforcement studying, and game idea. Eventually, it discusses capability destiny tendencies and advances in self-sufficient and adaptive communication systems that would improve the user revel in of smart home applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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Real-Time Anomaly Detection in Industrial automation Using Generative Adversarial Networks 5

Real-Time Anomaly Detection in Industrial Automation Using G...

引用

5th IEEE international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Changala, Ravindra Khan, Shamim Ahmad Kumar, Vijay Rani, V. Kanpur Dhilli, K. Raj, I. Infant Department of Cse Guru Nanak Institutions Technical Campus Hyderabad India Glocal School of Science & Technology Glocal University Department of Ece Uttar Pradesh India Department of Physics Graphic Era Hill University Dehardun India Graphic Era Deemed to be University Dehradun India Hindustan Institute of Technology and Science Department of Eee Chennai India Vignan's Institute of Engineering for Women Dept of Ece Visakhapatnam India K. Ramakrishnan College of Engineering Department of Cse Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

the study presents the use of GANs in anomaly detection for real-time industrial automation. A new model is proposed and implemented in Python, taking into consideration the dual nature of the algorithm known as GANs, which work on a dual framework including a generator and a discriminator. the normal behavior of industrial automation must be generated by a generator;meanwhile, the discriminator distinguishes between normal and anomalous data. It would facilitate the effective identification of anomalies, through adversarial training, based on learning normal operational patterns in real time, with the advantage of being adaptable in very dynamic and complex industrial environments. the ability to be timely about intervention, given its real-time capability, significantly enhances such attributes as predictive maintenance, improved quality control, and safe and efficient operations. this has been found in the study that the right identification of anomalies will prevent expensive downtime and will be a sign of smooth industrial process. In the proposed GAN model, 99.12% accuracy is achieved compared with other approaches, such as DBN, AnoGAN, and BiGAN, with an impressive gap of 4.05%. thus, superior performance indicates that GANs are robust and reliable for dealing with real-time industrial automation challenges. A critical characteristic of this Python implementation model is its adaptability, flexibility, and amenability to a practitioner in the industry as much as a researcher. Overall, this study further provides significant additions to the overall understanding of anomaly detection in an industrial setting with its demonstration capabilities toward the resiliency enhancement and efficiency potential in modern systems of industry through GAN applications. © 2025 IEEE.

关键词： Office automation

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Magnetic Levitation control Using Deep Reinforcement Learning and Prioritized Experience Replay with Action Noise Variations 4

Magnetic Levitation Control Using Deep Reinforcement Learnin...

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4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Pare, Gopala Shil, Manas Pillai, G.N. Dey, Arnab Department of Electrical Engineering Indian Institute of Technology Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee India

ISBN: (纸本)9798331530402

In recent years, Reinforcement Learning (RL) has emerged as a promising approach for addressing control problems in complex, nonlinear environments. the ability of RL algorithms to autonomously learn optimal control policies without requiring an explicit system model makes them particularly suited for real-time applications in fields like robotics and industrial automation. this paper investigates the application of RL algorithms for controlling a magnetic levitation (maglev) system, which presents significant challenges due to its nonlinear dynamics. the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and the integration of Prioritized Experience Replay (PER) are employed to enhance learning efficiency. the impact of varying action noise (random perturbations added to actions) for exploration and system performance is explored;results demonstrate that TD3 with PER achieves superior stability, faster convergence, and reduced rise and settling times without overshoot. these findings emphasize the importance of tailored exploration strategies in optimizing control policies for complex environments, highlighting the potential of advanced RL techniques for real-time control applications. © 2025 IEEE.

关键词： Reinforcement learning

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Revolutionizing Residential EV Charging through IoT-Enabled Smart Energy Metering for Sustainability 6

Revolutionizing Residential EV Charging Through IoT-Enabled ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sivakumar, N. Charles Raja, S. Meenakshi Devi, M. Saraswathi Meena, R. Dept. of EEE Thiagarajar College of Engineering Tamilnadu Madurai India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331522667

the rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. this paper proposes an IoT-based Smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. the system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. the system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. the proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability. © 2025 IEEE.

关键词： Predictive maintenance

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