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检索条件"任意字段=6th International Conference on Electrical Engineering, Computing Science and Automation Control"

共 5994 条记录，以下是1-10 订阅

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2009 6th international conference on electrical engineering, computing science and Automatic control, CCE 2009

2009 6th International Conference on Electrical Engineering,...

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2009 6th international conference on electrical engineering, computing science and Automatic control, CCE 2009

ISBN: (纸本)9781424446896

the proceedings contain 116 papers. the topics discussed include: on the advances of electromagnetic theory for inhomogenious media: a quaternionic approach;L1 - L2 robust estimation in prediction error system identification;a priori error estimates of mixed methods for quadratic convex optimal control problem governed by nonlinear parabolic equations;on the robust control design for a class of nonlinear affine control systems: the invariant ellipsoid approach;the bifurcation analysis on dynamic voltage stability of different wind power systems;constraints analysis, determination twists inside singularity and parametrical optimization of parallel mechanisms by means the theory of screws;inclusion of direct tunneling gate current in the symmetric doped double gate MOSFETs model;design and feasibility analysis of the physical layer for the academic WDM ring of Mexico city;and optimal risk-sensitive controller for first degree stochastic polynomial systems;and discrete-time adaptive neural backstepping control for a double fed induction generator.

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Smart Grid Solutions for Sustainable Mining: A Cost-Effective Energy Management Framework with Optimization Algorithms 6

Smart Grid Solutions for Sustainable Mining: A Cost-Effectiv...

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

作者： Parvathareddy, Sravani Yahya, Abid Amuhaya, Lilian Livutse Samikannu, Ravi Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of EEE New Era Engineering College South East Botswana

ISBN: (纸本)9798331522667

this research work focuses on conceptualizing and modeling smart grid infrastructure. the project's main objective is to develop a smart grid infrastructure to improve energy management in industrial processes. Regenerate has deployed a system that uses renewable energy sources like solar photovoltaic (PV) arrays and wind turbines combined with batteries for energy storage. this research work describes a method that first creates synthetic data and then uses a polynomial function to precisely model the statistical fluctuations inherent in the output of renewable energy sources. the process is carried out using MATLAB. A simulation model created using Simulink is also applied to build a smart grid that includes dynamic load control, demand side management DSM, and various control system configurations. the simulation demonstrates that it is possible to produce electricity with minimal or no depreciation related to an increased dependency on renewable energy sources. It can also function at a higher ratio of energy output produced by traditional sources than previously credited within New Zealand while having substantially fewer overall expenses associated with carbon emissions. © 2025 IEEE.

关键词： Demand side management

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Optimizing Wind Energy Output using a SEPIC Converter and Graph Neural Network (GNN)-based control Strategy 6

Optimizing Wind Energy Output using a SEPIC Converter and Gr...

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

作者： Manimegalai, V. Karthic, V.S. Arjun Aparna, M.K. Aruna, R. Mohanapriya, V. Gayathri, A. Department of Electrical and Electronics Engineering Sri Krishna College of Technology India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology India

ISBN: (纸本)9798331522667

this research study presents an optimized wind energy conversion system for increased energy output through the use of Graph Neural Networks (GNNs) and machine learning. In order to increase efficiency, the system incorporates a SEPIC converter and keeps track of torque, acceleration, and RPM for the turbine. the GNN model uses historical and current wind speed data to dynamically modify turbine operation in order to reduce losses. Reliability and consistent performance are guaranteed by automated alerts and remote monitoring for problems like dust buildup. this method establishes a standard for sustainable wind energy systems and greatly increases energy utilization. the integration of GNNs and SEPIC converters enhances wind turbine energy efficiency by dynamically optimizing performance and reducing losses. © 2025 IEEE.

关键词： Windmill

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Multi-controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. this causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. the introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Automated Immune Cell Classification using Image Processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

引用

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

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. this study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. the methodology incorporated preprocessing steps, like normalization and noise reduction. then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. these models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. the data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. the work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 2025 IEEE.

关键词： Convolutional neural networks

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Design and Development of IoT Smart Driving Protocol for Unmanned Ground Vehicles 6

Design and Development of IoT Smart Driving Protocol for Unm...

引用

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

作者： Seelam, Santhosh Kumar Nagakishore Bhavanam, S. Midasala, Vasujadevi Reddy, Edara Sreenivasa Department of Computer Science and Engineering University College of Engineering and Technology Acharya Nagarjuna University Andhra Pradesh Guntur India Jabalpur Madhya Pradesh Barela India Department of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331522667

UGVs have enormous potential in industries such as defense, agriculture, and autonomous transportation. Efficiency and effectiveness can be achieved by UGVs through the sophistication of their control systems, mainly with regard to navigation and decision-making. In this study, a new IoT-based smart direction control system is proposed for UGVs. the system presented herein integrates state-of-the-art sensor fusion techniques against the backdrop of a robust and adaptive communication framework for enhanced navigation accuracy and reduced latency, with the overall goal of improving system efficiency. Equipped with GPS, IMU, and LiDAR sensors, our system provides accurate environmental perception and real-time adaptability to various terrains. the heart of this device is a high-performance, military-grade microcontroller, which ensures the reliability and handling of numerous data streams simultaneously with minimal latency. On the software side, modular design makes use of edge computing and machine learning algorithms to facilitate adaptive control and real-time decision-making. the performance was verified by rigorous testing in simulated environments and real-world field trials. the results show an improvement in the navigation accuracy and system responsiveness compared to previous solutions. A comparative deep analysis shows the superiority of our approach to hardware design, software integration, and prototype testing. © 2025 IEEE.

关键词： Adaptive control systems

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks 6

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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

作者： Venkateswaran, N. Ramprabu, J. Marimuthu, Ramaprabha Ramana, Nagella Venkata Kotha, Mahesh Mallala, Balasubbareddy Department of Master of Business Administration Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Management Studies Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle517325 India CMR Technical Campus Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331522667

With the improvement in EV’s popularity, controlling the load demand of EVCS’s has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. the PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). the proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management. © 2025 IEEE.

关键词： Charging stations

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

引用

6th IEEE international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

this paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. this research aims to improve skin cancer diagnosis. this work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. this discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Development of Touch Sensor-based Headlight and Mirror controller for Automobiles 6

Design and Development of Touch Sensor-based Headlight and M...

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

作者： Vasuki, A. tharmaraj, R. Saruvardhan, K.S. Aakashpriyadharshan, M. Praveen, D. Department of ECE SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Modern cars frequently have manual regulators and switches for headlight and rearview mirror control, which can be difficult and distracting for drivers. this paper presents a more efficient alternative through the implementation of a touch- sensitive control system utilizing the ESP32 microcontroller. the device substitutes conventional controls with touch sensors to streamline the management of headlamp intensity and rearview mirrors. the technology rotates a stepper motor 20 degrees with a single touch to enhance headlight brightness, 40 degrees with a double touch for additional adjustment, and 60 degrees with a triple touch, facilitating precise control over the illumination. Furthermore, an additional touch sensor is employed to regulate servo motors that facilitate the automation of rearview mirror adjustment. A single touch activates the mirrors, while a double touch deactivates them, providing a more fluid experience. this touch-based technology offers drivers a safer and more intuitive method for controlling essential vehicle functions while simultaneously upgrading the car’s interface. this design employs readily available components and straightforward programming, making it both economical and scalable for future smart car applications. © 2025 IEEE.

关键词： Stepping motors

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