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检索条件"任意字段=IEEE Conference on Control Technology and Applications"

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Smart Servo-Operated Ball Valve with IoT Integration and PID control for Precise Fluid Management 3

Smart Servo-Operated Ball Valve with IoT Integration and PID...

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3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Bhanuse, Vijaykumar Bhosale, Shreyash Nitin Bhilare, Abhishek Mahadev Bauskar, Aneesh Rajesh Vishwakarma Institute of Technology Pune411037 India

ISBN: (纸本)9798331515911

This paper presents the design and implementation of an innovative control valve system that integrates a ball valve run by a servo motor, IoT capabilities, and PID control for precise operation. The system uses a 1/4-inch ball valve mounted in a custom 3D printed housing, driven by an MG995 servo motor. An ESP32 WROOM module microcontroller enables Wi-Fi connectivity and data transmission to the cloud via the Thingspeak platform. Ultrasonic sensors are used for tank level measurement, and the valve operation is controlled using a PID algorithm implemented in C++ on the Arduino IDE. The PID parameters are fine-tuned to achieve best performance. This solution offers a cost-effective and precise method for fluid control in various industrial applications. © 2025 ieee.

关键词： Three term control systems

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Wireless Charging Technologies for Electric Vehicles: State of the Art and Future Outlook

Wireless Charging Technologies for Electric Vehicles: State ...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Parrthipan, B.K. Manivel, Murugesan Jayakumar, T. Rao, R Subramani Sambooranalaxmi, S. Department of Electrical and Electronics Engineering Tamil Nadu 638060 India Kamaraj College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Vellakulam625701 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamilnadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Karur India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India

ISBN: (纸本)9798331529833

Wireless charging technology for electric vehicles (EVs) represents a significant advancement in the transition to sustainable transportation, offering a convenient and efficient alternative to traditional plug-in charging systems. This paper provides a comprehensive review of the state-of-the-art wireless charging techniques, with a focus on inductive power transfer (IPT), resonant inductive coupling, and emerging capacitive and magnetic resonance methods. Key components of wireless charging systems, including power electronics, coil design, alignment techniques, and communication protocols, are discussed. We also address the challenges related to efficiency, safety, standardization, and scalability, particularly in dynamic charging systems designed for highway applications. The paper further explores the integration of wireless charging into smart grids, considering the potential for vehicle-to-grid (V2G) applications and renewable energy sources. A comparative analysis of existing technologies and their readiness for widespread adoption is provided, alongside an outlook on future trends, including high-power charging, autonomous charging infrastructure, and advances in materials and control algorithms. The paper concludes by identifying research gaps and potential directions for innovation in wireless charging for EVs. © 2025 ieee.

关键词： Wireless charging

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Reinforcement Learning-Based Optimal Power Flow control in Islanded Microgrids 3

Reinforcement Learning-Based Optimal Power Flow Control in I...

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3rd ieee International conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Kumar, Amit Maity, Dipankar Ojha, Varun Nalini Sunil, M. Dore, Aruna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331542375

One of the popular paradigms for control problems in different applications is Reinforcement Learning. In this paper, we develop an RL-based technique for OPF control in islanded micro grids. Micro grids are small power systems for remote or rural regions that can function independently of the national grid and serve as a dependable alternative. The dependence on their decentralized power generation over DERs is a major complication for micro grid system stability and noteworthy optimization of reactive power flow. The operation of micro grids is both dynamic and stochastic;as a result, operational models based on traditional control methods at the model level or rule-based conflicts with optimized controllers that need to work in an environment where the future long-term forecasts remain uncertain. RL is suggested as a data-driven and adaptive control method to address these issues and optimize power flow in islanded micro grids. The RL-based control proposed in this paper uses realtime data from the micro grid to learn an optimal policy. The presented micro grid environment is for the reinforcement learning agent with which it interacts, and on success or failure of its actions, it will be given a reward signal to learn. The RL agent, through its interactions and learning process by taking actions during the operation of islanded micro grids, can optimize power flow in a distributed manner given DERs availability, load demand dynamics, and network constraints. © 2025 ieee.

关键词： Reinforcement learning

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Development and Analysis of a High-Efficiency T-Type Inverter based 3.7 kW Wireless EV Charging applications

Development and Analysis of a High-Efficiency T-Type Inverte...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Venugopal, R. Balaji, C. Shanmugam, Yuvaraja Srm Institute of Science and Technology Dept. of Electrical and Electronics Engineering Tamil Nadu Chennai603203 India Vignan's Foundation for Science Technology and Research Department of Eee Andhra Pradesh Vadlamudi522213 India

ISBN: (纸本)9798331529833

This paper presents the design and performance analysis of a T-type inverter optimized for wireless charging applications in electric vehicle (EV) batteries, aiming to enhance efficiency and reduce power losses. Traditional H-bridge inverter-based wireless power transfer (WPT) systems face challenges such as complex circuitry, voltage spikes, switching noise, and losses from switching, which impact overall efficiency. The proposed T-type inverter addresses these issues by improving voltage control and reducing switching losses, resulting in enhanced system efficiency. The system employs a series-series (S-S) compensation technique that provides better transmission control and performance, making it more adaptable to diverse load conditions. This compensation method, combined with optimized alignment tolerance and a high coupling coefficient, allows the system to efficiently use rectangular coils, which generate a more uniform magnetic field and minimize current loss. Designed for a 3.7 kW output at an operating frequency of 85 kHz, the T-type inverter-based WPT system was validated through MATLAB/Simulink simulations and a laboratory prototype. Results demonstrate an efficiency of 95.20% in simulation and 93.248% in hardware testing. Additionally, DC-DC efficiencies achieved are 94.78% in simulation and 96.177% in hardware. This research confirms the potential of T-type inverters in high-efficiency wireless EV charging systems. © 2025 ieee.

关键词： Inductive power transmission

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A Survey on Recognizing Lower Limb Movements from Surface Electromyography(sEMG) Signals 1

A Survey on Recognizing Lower Limb Movements from Surface El...

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1st International conference on AIML-applications for Engineering and technology, ICAET 2025

作者： Pandharikar, Atharva Jahagirdar, Aditi Dr. Vishwanath Karad MIT World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350355611

The paper presents a comprehensive framework for recognizing lower limb movements using surface electromyography (sEMG) signals, addressing the growing need for effective human activity recognition (HAR) systems in healthcare and rehabilitation. The study explores various machine learning and deep learning algorithms including Random Forest, Support Vector Machine (SVM), and hybrid models like CNN-LSTM, PC-GNN (Pearson Correlation-based Graph Neural Network) to classify movements based on sEMG sensors data collected from multiple muscles during distinct lower limb activities. The study focuses on important signal processing methods including wavelet denoising and time-domain feature extraction to improve classification accuracy. Challenges such as electrode displacement, muscle fatigue, and inter-subject variability are addressed. The suggested techniques for classifying sEMG signals outperform conventional methods, as shown by performance metrics. The results highlight how important precise sEMG signal analysis is for applications in prosthetic control, rehabilitation, and human-machine interaction. This research aims to advance the understanding of sEMG signal analysis and its practical implications for real-time human activity recognition. Future work will focus on improving model robustness for practical implementation. © 2025 ieee.

关键词： Decision trees

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Design and Implementation of Real-Time Wireless Sensor Network Protocols with Relay Nodes and Localization Methods for Efficient System Performance

Design and Implementation of Real-Time Wireless Sensor Netwo...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Balan, Gunapriya Pandey, Garima Sirisha, N. Ziara, Sajjad Thethi, H. Pal Alekhya, Juttiga New Horizon College of Engineering Department of Electrical and Electronics Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Griet Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper we focus on the design and implementation of reliable real time wireless sensor network (WSN) protocols that make use of relay node and proper localization techniques to enhance the performance of the system in real time applications. However, Wireless Sensor Networks (WSNs) offer a very important application in environmental monitoring, industrial control, and smart cities. Maintaining efficient communication protocols and accurate real time localization of sensor nodes, especially in environments with dynamic topologies, is one of the key challenges. This paper presents a robust WSN protocol with relay nodes which achieve both energy efficiency and communication efficiency. Additionally, it examines specialized localization algorithms for use in real time systems that offer enhanced localization precision without sacrificing system performance. Results show the reliability, scalability and energy efficiency of WSNs if utilized in real time applications can be highly improved using the optimized protocols that are coupled with localization techniques. © 2025 ieee.

关键词： Wireless sensor networks

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H-Shaped Multi-Analyte PCF-Integrated SPR Sensor for Multi-Industry Fluid Analysis

H-Shaped Multi-Analyte PCF-Integrated SPR Sensor for Multi-I...

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2025 ieee International conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Sultana Mim, Most Fahmida Ahmed, Tanvir Electrical and Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331512965

This research conducts a numerical investigation of a multi-analyte H-patterned photonic crystal fiber (PCF) sensor utilizing surface plasmon resonance (SPR) technology developed to recognize industrial fluids in various medical and chemical applications efficiently. The sensor incorporates two fluid channels, simultaneously detecting multiple analytes. It uses a gold-titanium dioxidecoated plasmonic layer, providing high sensitivity, improved electric field confinement, and chemical stability. The sensor is optimized for refractive index ranges of 1.34-1.38 in channel-I and 1.39-1.43 in channel-II, both with high accuracy. This study aims to develop a platform for real-time analyte monitoring. The Finite Element Method is applied for arithmetical assessment after refining the sensor's architectural parameters. The sensor demonstrates a peak wavelength sensitivityof 4,000 nm/RIU in channel-I and 28,000 nm/RIU in channel-II. Additionally, it achieves maximum amplitude sensitivitiesof 410.64 RIU-1 and 1561.55 RIU-1 in the respective channels. This corresponds to a resolution of 2.5 × 10-6 RIU for channel-I and 3.57 × 10-7 RIU for channel-II. This sensor is designed to detect unknown substances with RIs between 1.34 and 1.43, making it valuable for biological fluid diagnosis, food quality control, environmental monitoring, and chemical analysis. © 2025 ieee.

关键词： Photonic crystal fibers

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Ensuring Sustainable Safety in Fine-Grained Android Permission Model: Decidability through Finite Attribute Range Constraints 22

Ensuring Sustainable Safety in Fine-Grained Android Permissi...

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22nd International Learning and technology conference, L and T 2025

作者： Nauman, Mohammad Khan, Sohail Effat College of Engineering Effat University Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

When Android was first introduced, it permitted unrestricted access to all installed applications, posing significant security risks. In one of our previous works, we proposed, implemented, and demonstrated a comprehensive model capable of restricting access to sensitive permissions based on granular constraints. This model was partially adopted by Android and is now a standard feature in all modern Android-based systems. However, the flexibility of this model introduces a critical issue: the safety of the model. A model's safety determines whether the policies enforced by the model achieve the desired security goals. In this paper, we investigate the safety and undecidability problems associated with the model and demonstrate that, in its full form, the model is undecidable. To ensure creation and maintenance of sustainable security policies at both the individual and enterprise level, we propose the addition of a Finite Attribute Range Constraint, which renders the model both decidable and safe. © 2025 ieee.

关键词： Android Decidability Safety Analysis Sustainable Policies Usage control

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Garbage Object Detection Based on Improved YOLOv7

Garbage Object Detection Based on Improved YOLOv7

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Li, Shangzhou Wu, Ruobing Wei, YingMei College of Information Science and Technology Tibet University China School of Economics and Management Zhongyuan University of Technology China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Garbage target detection plays a crucial role in environmental protection and urban management. With the continuous development of computer vision technology, deep learning methods have become one of the main tools for garbage target detection. YOLOv7 is an advanced object detection model known for its high speed and accuracy, but it still faces challenges in garbage target detection. In order to enhance the performance of YOLOv7[1] in garbage target detection, this study proposes an approach based on improved ***, we introduce the GAMAttention[2] attention module to enhance the model's ability to detect small targets. Secondly, we incorporate the Decoupled Head[3] to improve the model's robustness to targets in complex backgrounds. Additionally, we optimize the model's loss function to further enhance detection accuracy. The proposed garbage target detection method based on improved YOLOv7 has made significant progress in improving detection performance, providing more effective tools and technical support for environmental protection and urban management. This approach is expected to be widely promoted in practical applications, addressing challenges and issues in the field of garbage target detection. © 2025 SPIE.

关键词： Attention Module Loss Deep Learning Function Optimization Garbage Detection YOLOv7

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks

Few-shot Structure-Informed Machinery Part Segmentation with...

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2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Schwingshackl, Michael Oberweger, Fabio F. Murschitz, Markus Ait Austrian Institute of Technology Center for Vision and Control Austria

ISBN: (纸本)9798331510831

This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network (GCN) to accurately segment machinery parts. By providing 1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset depicting a truck-mounted loading crane, achieves effective segmentation across various levels of detail. Training times are kept under five minutes on consumer GPUs. The model demonstrates robust generalization to real data, achieving a qualitative synthetic-to-real generalization with a J&F score of 92.2 on real data using 10 synthetic support samples. When benchmarked on the DAVIS 2017 dataset, it achieves a J&F score of 71.5 in semi-supervised video segmentation with three support samples. This method's fast training times and effective generalization to real data make it a valuable tool for autonomous systems interacting with machinery and infrastructure, and illustrate the potential of combined and orchestrated foundation models for few-shot segmentation tasks. © 2025 ieee.

关键词： Semantic Segmentation

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