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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 ieee.

关键词： Genetic algorithms

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Smart Classroom Power automation: An Adaptive And Predictive Approach

Smart Classroom Power Automation: An Adaptive And Predictive...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhurima, Gangireddy Reddy, Panduga Venkata Jaya Srikanth Srinivasa Perumal, R. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529833

The growing demand for energy efficiency in educational institutions has led to the exploration of smart technologies for managing power consumption. This project introduces a "Power automation for Classrooms"system that leverages long-range RFID (Radio Frequency Identification) technology to automate the activation of classroom lights and fans based on student attendance. The system operates by detecting student entries through RFID tags without the need for manual scanning, thus ensuring a seamless and instant process. The project aims to reduce energy wastage by activating the power in stages depending on the number of students present, with specific power allocation thresholds set according to the class strength. The data on students entered, as well as the power consumption for each classroom, is stored in the cloud, allowing for real-time monitoring, analysis and supports data-driven decision-making for future energy planning. © 2025 ieee.

关键词： Decision making

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 ieee.

关键词： Fruits

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Feedback Control Based on Trust for Varied automation Transparency 1

Feedback Control Based on Trust for Varied Automation Transp...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Bandyopadhyay, Krishnan Banerji, Nandan Mishra, Ishani Kandpal, Ankit St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Karnataka Bengaluru India Sikkim Manipal Institute of Technology Sikkim Manipal University Department of Computer Science and Engineering Sikkim India New Horizon college of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Interactions between people and automation depend heavily on human confidence in the latter. Lack of trust can cause people to not use automation, while too much trust can cause them to put their faith in a flawed autonomous system, which could have detrimental effects on them. Transparency and trust in automation are vital for the usefulness and security of human-robotization frameworks. The majority of the research that has been done on the trust that human operators have in robotics and automation has been an ongoing variable that has received little attention. paid to how trust changes over time. In order to fill this research void, we offer a study that examines how trust is dynamic. Using real-time trust measurements, we first discovered options to quantify trust of entirety, which we described as a metric that considers trust throughout the entire human-automation interaction experience. In this study, a feedback control strategy for powerfully changing mechanization straightforwardness and transparency concurring on client trust is analysed. The proposed approach tweaks mechanical way of behaving and correspondence by incorporating an input control component with a trust assessment model. The adequacy of the feedback control framework in improving errand execution and trust adjustment under different levels of computerization straightforwardness and transparency is checked utilizing MATLAB reenactments. © 2025 ieee.

关键词： Human robot interaction

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Analysis of CAD tools in VLSI Physical design

Analysis of CAD tools in VLSI Physical design

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Bharath, Duttuluru Jagadeesh, A. Chaitanya M, Uday Jaya, T. Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529833

The increasing complexity of VLSI physical design has elevated the need for advanced CAD tools capable of efficiently managing tasks such as floor planning, placement, routing, and optimization. This study presents a detailed evaluation of prominent CAD tools, analyzing their effectiveness in addressing the critical demands of modern VLSI design, including power efficiency, performance, and scalability. By comparing the strengths and limitations of each tool, this work aims to guide practitioners in selecting optimal solutions for their specific design requirements. Additionally, emerging trends in CAD development, particularly the integration of AI and machine learning, are discussed as pathways to further enhance design automation. Our analysis provides insights that not only underscore the current capabilities of CAD tools but also suggest directions for future research to meet the evolving needs of the VLSI industry. © 2025 ieee.

关键词： Power systems computer aided design

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Guest Editorial: Special Issue of Selected Extended Papers From the ieee international conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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ieee Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Development of robotic arm for fruits and vegetables sorting

Development of robotic arm for fruits and vegetables sorting

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Likithkumar, C. Thrisha, K. Anbarasi, S. Navarasu, S. Srm Institute of Science and Technology Electronics and Instrumentation Engineering Faculty Chennai India Srm Institute of Science and Technology Electronics and Instrumentation Engineering Student Chennai India

ISBN: (纸本)9798331529833

Among various automation approaches, the pick and-place robotic arm stands out due to its versatility and potential for wide application in sorting, picking, and placing tasks. The robotic arm is equipped with advanced computer vision capabilities for automated agricultural tasks. The system incorporates object detection algorithms using THONY software to accurately identify and localize fruits or vegetables within the arm's workspace. The integration of object detection and classification enhances the system's versatility and adaptability, making it suitable for agricultural applications which includes sorting. By automating repetitive tasks and utilizing real-time object detection, the robotic arm enhances operational efficiency, reduces labor requirements, and improves precision in handling and sorting. © 2025 ieee.

关键词： Fruits

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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ieee Transactions on automation science and engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 ieee.

关键词： Stochastic programming

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State Estimation of a Spatial 2-D Linear Diffusion Process With Mobile Sensors

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ieee Transactions on automation science and engineering 2025年 22卷 12024-12032页

作者： Zhang, Xiao-Wei Chen, Kui Wu, Huai-Ning Wang, Jin-Liang Wang, Zi-Peng Beijing University of Technology School of Information Science and Technology Beijing100124 China Tiangong University School of Control Science and Engineering Tianjin300387 China Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Beijing University of Technology School of Information Science and Technology Beijing Institute of Artificial Intelligence Beijing100124 China

This paper studies the state observer design of a spatial two-dimensional (2-D) linear diffusion process described by a linear parabolic partial differential equation (PDE) under mobile sensors. Firstly, we analyze the well-posedness of the PDE system and give the structure form of the state observer with mobile sensors. Subsequently, according to the number of mobile sensors, the 2-D space domain is divided into multiple sub-domains, and the mobile sensors are guided by the projection operator method, which can guarantee that the mobile sensors can only move in their respective 2-D sub-domains. Then, in the light of Lyapunov theory, Poincaré-Wirtinger inequality and Barbalat lemma, we propose an observation-plus-guidance design method to ensure the asymptotic stability of the state estimation error system. In the designed mobile strategy, the actual guidance of mobile sensors is essentially a physical synthesis of two direction guidance laws, where two dimensional guidance laws are designed separately. Moreover, the existence condition of the observer is given by linear matrix inequalities. At last, a numerical example is provided to demonstrate the efficacy of the proposed design scheme. © 2004-2012 ieee.

关键词： Asymptotic stability

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Advanced NDRE Techniques for Precision Crop Monitoring and Analysis

Advanced NDRE Techniques for Precision Crop Monitoring and A...

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Hari Haran, B. Swetha, A. Chitra, R. Vishwa, M. Sri Sairam Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529833

Monitoring chlorophyll content is essential for assessing plant health and detecting early stress, yet traditional methods are often labor-intensive and prone to inaccuracies. An automated system utilizing the NDRE index offers a solution, employing NIR and RE spectral bands to calculate chlorophyll levels accurately across large datasets. Advanced image segmentation and machine learning techniques enhance precision, making the NDRE index a reliable indicator values below 0.5 point to potential deficiencies, while values above 0.5 signify healthier chlorophyll levels. automation minimizes human error and accelerates evaluation, enabling early stress detection and proactive crop management. This efficient, data-driven approach supports sustainable agricultural practices and improves productivity, proving invaluable in modern crop monitoring and decision-making. © 2025 ieee.

关键词： Image segmentation

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