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Design of Automatic Print Defect Detection system Based on Machine Vision Detection Algorithm

Design of Automatic Print Defect Detection System Based on M...

引用

Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Chao Wang Feiyan Li Zijuan Xu Yunnan Xinhua Printing Factory No. 5 Co. Ltd Kunming China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In the context of today's digital production, the quality inspection of printed materials has become a key technology to improve printing efficiency and quality of finished products. Especially with the rapid development of the printing industry, the traditional manual inspection method has been difficult to meet the needs of large-scale, high-efficiency production. In order to solve this problem, the automatic inspection system of print defects based on machine vision comes into being, which can not only greatly improve the detection speed and accuracy, but also effectively reduce the labor cost. This paper proposes an efficient defect detection method for printed materials, combining key technologies such as image differencing, binarization, mathematical morphology algorithms, Blob analysis and connectivity analysis to realize automatic identification and classification of minor defects in printed materials. The design and experimental results of the system show that the method has significant advantages in real-time and detection accuracy, and is expected to play an important role in future printing quality control.

关键词： Printing Industries Accuracy Machine vision Morphology Production Quality control Inspection Real-time systems Defect detection

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Anomaly Detection for Identity Authentication Using 1D-CNN-LSTM Autoencoder and Doppler Radar with Classifiers

Anomaly Detection for Identity Authentication Using 1D-CNN-L...

引用

ieee MTT-S international Microwave Bio conference ((IMBIOC)

作者： Yu-Hong Wu Chin-Lung Yang Dept. Electrical Engineering National Cheng Kung Univ Tainan Taiwan

ISBN: (数字)9798331510473

ISBN: (纸本)9798331510480

This study presents an integrated framework combining 1D-CNN-LSTM-Autoencoder-based anomaly detection with identity authentication using machine learning classifiers. The 1D-CNN-LSTM Autoencoder enhances system reliability by identifying and excluding faulty intervals in timeseries data caused by system instability or random movements. The dataset, collected under a controlled protocol, features scenarios such as Resting and Valsalva maneuvers for training and testing. To address measurement data interference, which can affect identity recognition accuracy, anomaly detection filters out corrupted signals, maintaining system stability and reliability. Among the machine learning classifiers, Cubic SVM demonstrated superior performance, achieving an accuracy of 8 8. 7 %. This integrated approach enhances system robustness, offering promising applications in secure access control, smart environments, and healthcare monitoring.

关键词： Support vector machines Training Accuracy Autoencoders Authentication Robustness Doppler radar Anomaly detection Thermal stability Testing

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A Zeta Converter-Based Improved BLDC Motor-Driven Water Pumping system for Partial Shading Environment of Solar PV

A Zeta Converter-Based Improved BLDC Motor-Driven Water Pump...

引用

Smart and Sustainable Developments in Electrical engineering (SSDEE), ieee international conference on

作者： Satadal Bhowmik Suryakant Kumar Gauri Shankar Dept. of Electrical Engineering I.I.T. (ISM) Dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper demonstrates a brushless DC motor (BLDCM) fed water pumping system driven by the solar photovoltaic array (SPVA) under dynamic partial shading conditions. For extraction of maximum power from SPVA, a zeta converter (ZC) is utilized. However, under partial shading conditions, the SPVA experience multiple maximum power points (MPP). The conventional MPP tracking methods are ineffective in identifying the global MPP points as they converge to the local MPP points. Therefore, the utmost power cannot be extracted, when SPVA working under partially shaded conditions. Thus, this paper demonstrates the use of hybrid perturb & observe and particle swarm optimization techniques to track the global MPP. In this work, the SPVA's power extracted by ZC is fed to BLDCM for water pumping application using VSI. The VSI of a BLDCM typically functions at a significantly high frequency. This work proposes operating the VSI at a fundamental switching frequency to alleviate difficulties associated with high switching frequency. The suggested system is studied in MATLAB®/Simulink environment to validate its performance under various operational instances.

关键词： Photovoltaic systems Brushless DC motors Switching frequency Motors Water pumps Maintenance Sustainable development Particle swarm optimization MATLAB Water resources

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ICT Enabled Learning: Paving Way Towards an Inclusive Education system in India

ICT Enabled Learning: Paving Way Towards an Inclusive Educat...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Deepak Kumar Manish Singh Department of Humanities and Social Sciences Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The integration of Information and Communication technology (ICT) into India's education system represents a transformative approach toward fostering inclusive education. This paper explores the evolution of ICT in Indian education, highlighting its implementation in rural and aspirational districts as part of the Aspirational Districts Program (ADP). While ICT has shown promise in bridging educational disparities by promoting access, equity, and quality, challenges such as digital divides and resource limitations persist, particularly in underdeveloped regions. Through case studies and programmatic insights, this paper evaluates ICT's role as a catalyst for social change, offering policy recommendations for enhancing ICT-enabled learning in rural India. The findings underscore ICT's potential to address systemic barriers, driving progress toward equitable and inclusive education nationwide.

关键词： Bridges Technological innovation Catalysts Digital transformation Education Information and communication technology Planning Digital intelligence Digital divide Faces

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Enhanced MPPT for Solar systems Using Chaotic PSO and DC-DC Luo Converter

Enhanced MPPT for Solar Systems Using Chaotic PSO and DC-DC ...

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Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： D. Karthikeyan Anish John Paul M K. Rajesh Subharathna N T. Jayakumar M. Sasikumar Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Department of Electrical and Electronics Engineering Mar Ephraem College of Engineering and Technology Elavuvilai India Department of Electrical and Electronics Engineering NPR College of Engineering and Technology Natham Dindigul Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Department of Electrical and Electronics Engineering Nandha Engineering College Erode India Department of Electrical and Electronics Engineering C. Abdul Hakeem College of Engineering & Technology Ranipet District Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This study describes an enhanced PV grid tied system featuring PSO based MPPT controller and PI controller for efficient power conversion and grid support. A Luo converter regulates DC voltage, while a single phase VSI connects to grid. The PSO based MPPT optimizes the Luo converter's duty cycle for maximum power extraction, and the PI controller stabilizes power flow by managing active and reactive power. The proposed system demonstrates superior dynamic response and adaptability to changing environmental conditions, enabling efficient energy utilization and reliable grid integration. The Matlab/Simulink program is utilized to acquire the simulation results, which are then used to evaluate the circuit's efficacy. Simulation results highlight the performance improvements in power extraction and grid stability compared to traditional MPPT methods. When the controllers are compared, the THD value of the intended task is 1.58%, and the efficiency achieved with the Luo converter is 93.5%.

关键词： Renewable energy sources Reactive power PI control Simulation Smart systems Stability analysis Regulation Solar system Voltage control Power harmonic filters

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Research on Solving Mathematical Programming Problems Based on Intelligent Optimization Algorithms

Research on Solving Mathematical Programming Problems Based ...

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Electronics, Energy systems and Power engineering (EESPE), ieee international conference on

作者： Yan Wang Wuhan Technology and Business University Wuhan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper studies the solution methods of mathematical programming problems based on intelligent optimization algorithms, focusing on the application and performance of GA and PSO in complex mathematical programming. First, a hybrid intelligent algorithm that integrates the characteristics of genetic algorithm and particle swarm optimization is designed. By optimizing the coding structure, selection strategy and crossover mutation operator, the global search ability and convergence speed of the algorithm are improved. Secondly, a mathematical model suitable for integer programming and nonlinear programming is constructed, and experimental verification of problems of different scales is carried out in combination with the simulation platform. Experiments show that in typical test functions and actual application scenarios, the improved algorithm improves the solution accuracy by 15.3% and shortens the convergence time by 18.7 % compared with the traditional method. The simulation results prove the effectiveness and robustness of the improved algorithm through quantitative data analysis, especially in solving high-dimensional nonlinear problems. The research in this paper not only expands the application of intelligent optimization algorithms in the field of mathematical programming, but also provides a theoretical basis and practical reference for solving actual engineering optimization problems.

关键词： Power engineering Accuracy Simulation Programming Robustness Particle swarm optimization Optimization Mathematical programming Genetic algorithms Convergence

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Advanced Epileptic Seizure Recognition with a Hybrid CNN-BiLSTM Model on EEG Signals

Advanced Epileptic Seizure Recognition with a Hybrid CNN-BiL...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), ieee international conference on

作者： L Krishna Kumari R Prem Ananth K Ramalakshmi B Mohan S P Santhoshkumar S Mahalakshmi Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Epileptic seizure recognition (ESR) is a vital area in healthcare, neurology, and patient monitoring, particularly for managing epilepsy in real time. While deep learning (DL) models have shown exceptional performance in recognizing seizures from EEG signals, processing sequential and time-series data remains challenging. This study proposes a hybrid deep learning framework combining one-dimensional convolutional neural networks (1D CNN) and bidirectional long short-term memory (BiLSTM) networks to enhance seizure recognition. The 1D CNN efficiently captures localized temporal features from EEG data, while the BiLSTM models bidirectional long-range dependencies, improving temporal feature representation. Tested on a benchmark dataset, the model achieves an outstanding accuracy of 98.18%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating CNN for feature extraction and BiLSTM for sequence modeling, offering a robust and reliable approach for automated ESR in clinical applications.

关键词： Deep learning Bidirectional long short term memory Signal processing Brain modeling Feature extraction Electroencephalography Data models Convolutional neural networks Reliability Sustainable development

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Protocol Design for Irregular Repetition Slotted ALOHA with Energy Harvesting to Maintain Information Freshness

Protocol Design for Irregular Repetition Slotted ALOHA with ...

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ieee conference on Wireless Communications and Networking

作者： Khac-Hoang Ngo Diep N. Nguyen Thai-Mai Dinh Thi Department of Electrical Engineering (ISY) Linköping University Linkoping SE Sweden School of Electrical and Data Engineering University of Technology Sydney Australia Faculty of Electronics and Telecommunications VNU University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We investigate an internet-of-things system where energy-harvesting devices send status updates to a common receiver using the irregular repetition slotted ALOHA (IRSA) protocol. Energy shortages in these devices may lead to transmission failures that are unknown to the receiver, disrupting the decoding process. To address this issue, we propose a method for the receiver to perfectly identify such failures. Furthermore, we optimize the degree distribution of the protocol to enhance the freshness of the status updates. Our optimized degree distribution mitigates the adverse effects of potential transmission failures. Numerical results demonstrate that, despite energy-harvesting constraints, IRSA can achieve a level of information freshness comparable to systems with unlimited energy.

关键词： Potential energy Performance evaluation Protocols Receivers Decoding Batteries Energy harvesting

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Stress Detection Using SVM and Virtual Assistant

Stress Detection Using SVM and Virtual Assistant

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Pranav Amale Anup Vibhute Mahesh S. Mathpati Ashwini V Jatti Somnath B. Thigale Electronics and Telecommunications Department Dr. D. Y. Patil Institute of Technology Pune Instrumentation Engineering Department Dr. D. Y. Patil Institute of Technology Pune Electronics and Telecommunications Department SVERIs College of Engineering Pandharpur Computer Science and Engineering Department Fabtech College of Engineering Sangola

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This paper presents a chatbot model designed to detect stress in users through simple yes-or-no questions. Utilizing a Support Vector Machine (SVM) algorithm, the model achieves high accuracy in identifying stress levels and provides users with actionable advice or medical recommendations. The chatbot’s user-friendly interface is built using Anaconda Navigator and Python, ensuring ease of use for non-technical users. The model achieves a 100% accuracy rate on the given dataset, making it a promising tool for stress detection and early intervention.

关键词： Support vector machines Computer science Accuracy Navigation Virtual assistants Prevention and mitigation Mental health Medical services Predictive models Prediction algorithms

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A Basic Framework for Fetal Monitoring in Detecting Stillbirth Risks Using Heart Rate and Fetal Movements

A Basic Framework for Fetal Monitoring in Detecting Stillbir...

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international conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Anohita Mukherjee Mili Sarkar Saikat Das Sreeparna Das Debayuth Banik Department of Electronics and Communication Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Stillbirth remains a critical issue in obstetrics, with various underlying factors contributing to fetal mortality. Early detection of potential risks can significantly improve clinical outcomes. This paper presents a simple framework for detecting stillbirth risks using fetal heart rate and fetal movement data. The framework is implemented as a Java-based software system that analyzes heart rate and fetal movement data, offering a preliminary model for monitoring fetal health. This approach aims to support clinicians in their decision-making process and foster further development of more sophisticated monitoring systems in the future. The proposed system is not intended to replace medical professionals but rather serves as an auxiliary tool for early detection of potential stillbirth risks based on available monitoring data.

关键词： Pregnancy Fetal heart rate Mortality Software systems Real-time systems Mobile applications Risk management Obstetrics Wearable devices Monitoring

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