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Predictive Analytics and automation in Supply Chain Management with Internet of Things (IoT) 3

Predictive Analytics and Automation in Supply Chain Manageme...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Yesodha, Karthik Kalyan Raj Kumar Rajendran, Prabhakaran Bhalerao, Manoj Gupta, Ketan Yuvaraj, N. Supply Chain and Manufacturing Caterpillar Inc BloomingtonIL United States Cscs Llc CummingGA United States Hub Group Oak BrookIL United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

Predictive analytics and automation with the Internet of Things (IoT) is a growing trend in supply chain management. The generation permits cost-efficient operations and organizations to reveal and control their supply chain systems. The combination of predictive analytics algorithms and automatic structures can offer insights into supply chain operations, expect adjustments in customer demands, and allow agencies to plan better and put things together for the future. IoT can also be used to screen supply chain techniques, become aware of regions for development, and proactively manipulate potential disruptions. U sing IoT -based analytics and automation, advantages that include increased visibility in supply chain operations, elevated customer satisfaction, and cost-effective supply chain processes may be achieved. Furthermore, predictive analytics and automation can help improve choice-making with actual-time records insights and ensure everyone's tactics are optimized for efficient performance. © 2025 IEEE.

关键词： Supply chain management

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Transmission scheme based on collaborative AUV communication 16

Transmission scheme based on collaborative AUV communication

引用

16th international conference on Signal Processing Systems, ICSPS 2024

作者： Sun, Jiahao Qi, Chao Liu, Zengli Zhao, Xuanzhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Center Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510689251

Underwater wireless sensor networks can monitor ocean information, which provides a new approach to marine environmental monitoring, disaster warning and resource exploration. However, the development of underwater wireless sensing networks is limited by factors such as ocean currents and limited energy of the sensors themselves. Based on this situation, this paper proposes a transmission scheme based on collaborative communication with autonomous underwater vehicles (AUVs), in which autonomous underwater vehicles (AUVs) are used as a tool for distributed data collection and forwarding, which can solve the problem of imbalanced energy consumption of sensor nodes in the traditional multi-hop underwater communication network. At the same time, we consider the influence of ocean currents on the node positions to maximize the imitation of the real underwater environment, and the simulation results show that the proposed transmission scheme for collaborative communication with AUVs can significantly prolong the service life of underwater sensor networks. © 2025 SPIE.

关键词： Autonomous underwater vehicles

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The application of intelligent optical sensor networks in industrial automation 5

The application of intelligent optical sensor networks in in...

引用

5th international conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Yang, Qiwei Chen, Tao Li, Rui Tian, Zhen Fuzhou University Fuzhou China Chongqing University of Posts and Telecommunications Chongqing China University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781510691667

With the advancement of industrial automation, Wireless Sensor Networks (WSNs), particularly those incorporating optical sensing technologies, have shown great potential in areas such as industrial process monitoring and equipment condition management due to their flexible deployment, real-time environmental sensing capabilities, and reliable automation control advantages. This paper comprehensively evaluates the role of wireless sensor networks, with emphasis on optical sensing nodes, in the field of industrial automation through literature review, demand analysis, and analysis of typical application cases. The research shows that compared to traditional wired systems, wireless sensor networks integrated with optical sensing elements enable more flexible layouts and provide real-time, accurate, and reliable environmental monitoring and process control, including precise optical measurements for quality inspection and process optimization, thereby improving production efficiency, safety, and resource utilization efficiency. However, this technology also faces technical challenges such as network reliability, optical signal interference, and system security that need to be overcome. Overall, wireless sensor networks with optical sensing capabilities are driving industrial automation systems towards flexibility, intelligence, and sustainability, despite some technical challenges that remain to be addressed. This study provides practical theoretical support and solution strategy references for the application of wireless sensor network technology with optical sensing in industrial automation. © 2025 SPIE.

关键词： Fault prediction Industrial process control Intelligent optical sensor networks Optical sensing Wireless communication

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms 4

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

引用

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

作者： Narejo, Sanam Khan, Muhammad Taimoor Shaikh, Muhammad Zakir Dhirani, Lubna Luxmi Chowdhery, Bhawani Shankar Mehran Univerity of Engineering and Technology Department of Computer Systems Jamshoro Pakistan Mehran Univerity of Engineering and Technology Jamshoro National Centre for Robotics Automation and Artificial Intelligence Pakistan University of Malaga Mechanical Engineering and Energy Efficiency School of Industrial Engineering Malaga Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798331518882

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car's navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car's driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car's evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations. © 2025 IEEE.

关键词： Reinforcement learning

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Enhancing Domain Modeling with Pre-trained Large Language Models: An Automated Assistant for Domain Modelers 43rd

Enhancing Domain Modeling with Pre-trained Large Language Mo...

引用

43rd international conference on Conceptual Modeling

作者： Prokop, Dominik Stenchlak, Stepan Skoda, Petr Klimek, Jakub Necasky, Martin Charles Univ Prague Fac Math & Phys Prague Czech Republic

ISBN: (纸本)9783031758713;9783031758720

Domain modeling involves creating abstract representations of information within a specific domain using techniques such as conceptual modeling and ontology engineering. Traditionally, manual creation and maintenance of domain models are labor intensive and require modeling expertise. This paper explores the automation of domain modeling using pre-trained large language models (LLMs), presenting an experimental LLM-based conceptual modeling assistant that collaborates with a human expert. The assistant provides modeling suggestions based on a given textual description of the domain of interest, aiding in the design of classes, attributes, and associations. We present a generic framework for domain modeling assistants that consists of class, attribute, and association generators, and show how they can be implemented using an LLM. We demonstrate a concrete configuration of this framework and its prototype implementation. We evaluated the effectiveness of the framework configuration across various domains. Our findings indicate that the assistant significantly enhances the efficiency of modeling while maintaining reasonable quality of the outputs.

关键词： Domain Modeling Conceptual Modeling Large Language Models Domain Modeling automation

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Design and Internal Structure Analysis of an automation-Based Smart Dining Table 24

Design and Internal Structure Analysis of an Automation-Base...

引用

4th international Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Yan, Shimao Wang, Xuechun Hao, Hanlin Dong, Xinran Wan, Weixin School of Mechanical and Automation Zhixing College Hubei University The Zhixing College of Hubei University Hubei Wuhan China School of Power and Mechanical Engineering Wuhan University The Zhixing College of Hubei University hubei Wuhan China School of Computer and Information Engineering Zhixing College Hubei University The Zhixing College of Hubei University Wuhan China

ISBN: (纸本)9798400710100

In response to the increasing societal demand for time-saving and labor-efficient solutions in table cleaning, this paper presents the design of a novel smart dining table that supersedes manual cleaning tasks. The introduced smart table integrates automatic cleaning, dish handling, and waste collection functionalities, optimizing spatial utilization to automate the entire process from storage, disinfection, categorization, washing, to drying. This comprehensive automation not only reduces the physical effort required for post-meal cleanup but also ensures a more hygienic and efficient dining *** article meticulously outlines the primary operational units of the smart table. It begins with an in-depth examination of the internal structure of the tabletop cleaning robot, which features advanced sensors and navigation systems to ensure thorough and precise cleaning. The design of a flexible four-claw mechanism is discussed, enabling the robot to securely handle various types of dishes and utensils. Additionally, the development of a cleaning tank conveyance system is explored, detailing how it efficiently transports dirty dishes to the washing station. Theoretical computations for the control panel are provided, highlighting the sophisticated algorithms that manage the table's operations. A materials analysis of the internal conveyor mechanism within the table is also presented, emphasizing durability and efficiency. By offering integrated smart services, this innovation enables users to better enjoy a comfortable and healthy dining experience, significantly enhancing the overall quality of life. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Washing

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conference Committee

Conference Committee

引用

international conference on computer and automation engineering, ICCAE

conference Committee.

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

conference Committee.

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Research on modeling and feature extraction of maritime vessel targets based on micro-doppler effect characteristics 5

Research on modeling and feature extraction of maritime vess...

引用

5th international conference on Signal Processing and computer Science, SPCS 2024

作者： Cai, Fan Jiang, Yuesong Zhu, Tongbo School of Electronic and Electrical Engineering Minnan University of Science and Technology Quanzhou362700 China School of Electronic and Information Engineering Beihang University Beijing100000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The micro-Doppler effect captures the fine motion characteristics of maritime targets, serving as a crucial feature for distinguishing between sea clutter and targets, thereby enhancing radar target detection and recognition capabilities. In this study, a long-term radar echo model for micro-motion targets in sea clutter is established using maritime surveillance radar. By analyzing the morphological differences between constant-velocity targets and micro-motion clutter in Short-Time Fourier Transform (STFT) spectrograms, a sliding window cancellation method based on STFT spectrograms is proposed to remove micro-motion clutter. This method effectively separates target echoes from micro-motion clutter, facilitating the extraction of micro-motion target features. Both simulation and experimental results validate the effectiveness of the proposed method, laying a theoretical foundation for advancing maritime vessel detection and recognition. © 2025 SPIE.

关键词： Surveillance radar

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The dot-matrix assisted focusing method for parallel phase-shifting digital holographic microscopy

The dot-matrix assisted focusing method for parallel phase-s...

引用

2024 international conference on Optical and Photonic engineering, icOPEN 2024

作者： Zheng, Hanjie Hua, Zhijie Zhang, Xu School of Mechatronic Engineering and Automation Shanghai University Shanghai China Wuxi Research Institute Huazhong University of Science and Technology Jiangsu China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Digital holographic microscopy can achieve automatic focusing of the object under test by simulating the diffraction propagation of the object wave through a computer. However, existing automatic focusing techniques require multiple iterative calculations of the reconstruction results of the object wave at different diffraction propagation distances, and then selecting the optimal reconstruction distance based on criteria such as sharpness measure and energy concentration. This process is time-consuming and the calculation accuracy depends on the iteration step size. This paper proposes a dot-matrix assisted focusing method for parallel phase-shifting digital holographic microscopy, which integrates the dot-matrix assisted focusing optical path structure into the parallel phase-shifting digital holographic microscope. This method allows the simultaneous acquisition of a polarization-modulated hologram and an auxiliary dot-matrix focusing image. By calculating the defocus amount based on the sharpness of the auxiliary dot-matrix, the optimal diffraction propagation distance of the object wave can be directly obtained, thereby reducing the computation time for diffraction reconstruction distance during hologram reconstruction and improving focusing accuracy. The measurement results of the micro-optical elements experiment verify the effectiveness and reliability of this method. © 2025 SPIE.

关键词： Holograms

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Integrating NLP for Intelligent Cybersecurity Risk Detection from Bulletins

Integrating NLP for Intelligent Cybersecurity Risk Detection...

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2025 international conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mondal, Kalimuddin Ebenezar, U. Samson Karthikeyan, L. Gururama Senthilvel, P. Preetham, B.S. Simats Engineering Saveetha Institute of Medical and Technical Sciences 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 Saveetha University Department of Computer Science and Engineering Chennai India Ms Ramaiah Institute of Technology Department of Medical Electronics Bangalore India

ISBN: (纸本)9798331538538

The rate of developed threats increases and poses challenges when it comes to the recognition of the risks and measures to be taken. Current cybersecurity risk assessment is mainly done using word-of-mouth through reviewed security announcements, vulnerable product or service reports, and threat intelligence data feeds, all of which are time-consuming and often contain human errors. The use of Natural Language Processing (NLP) in cybersecurity brings the efficiency of automation where text information is analyzed with the aim of identifying and categorizing threats in real time. In this paper, NLP techniques including Named Entity Recognition (NER), sentiment analysis and topic modelling are used to analyze the security bulletins and determine the security threats. Supervised, semi-supervised, and unsupervised learning methods of the NLP system can bene fit to mine adequate security-related text data, identify suitable patterns and or make forecasts of susceptible vulnerabilities. The proposed framework is primarily a framework to improve threat intelligence through vulnerability, attack vectors, and related systems connection. In addition, NLP-based risk identification also has several advantages: it increases the accuracy of risk identification and decreases the time for risk detection, as well as strengthens cybersecurity infrastructure. The paper demonstrates that by applying the technique of IBM security bulletins, organizations can reduce the risks and improve the controls effectively. The paper concludes with areas of future work in enhancing the application of NLP techniques in cybersecurity with an agreement on the idea of adaptive models that reflects the dynamic and ever-changing nature and trends of cyber threats. © 2025 IEEE.

关键词： Deep learning

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