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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Mestre, Antonio Faustino, dinis Silva, Bruno Cruz, Jorge Muvu Technologies R&d Department Lisbon Portugal Nova School of Science and Technology Department of Computer Science Lisbon Portugal

ISBN: (纸本)9798350371154

The fourth industrial revolution has ushered in a transformative era for the industrial sector, marked by the integration of advanced technologies that increase production efficiency and quality. Among these innovations is Smart Scheduling, which aims to optimize production processes while minimizing costs and meeting manufacturing requirements. This article explores the development and application of a Hybrid Genetic Algorithm, optimized by Tabu Search, to meet the complex challenge of multi-objective industrial production planning. The proposed framework introduces a Genetic Algorithm designed for intelligent, multi-objective industrial production planning, demonstrating its effectiveness in generating high-quality planning. It significantly reduces production times while maintaining quality and business needs, as validated in a real-world case study. The algorithm improves on traditional planning methods by dynamically adapting to manufacturing priorities. It also describes potential future adaptations to a wider range of industrial contexts and sectors. This research highlights the fundamental role of genetic algorithms in advancing smart manufacturing, offering a scalable and adaptable solution to the challenges of Industry 4.0. © 2024 IEEE.

关键词： Genetic algorithms

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A Calculation Method of double Strength reduction for Layered Slope Based on the reduction of Water Content Intensity

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Computer Modeling in Engineering & sciences 2024年第1期138卷 221-243页

作者： Feng Shen Yang Zhao Bingyi Li Kai Wu School of Civil Engineering Suzhou University of Science and TechnologySuzhou215011China R&D Department Suzhou Jcon Greenbuild Fabricated Co.Ltd.Suzhou215004China

The calculation of the factor of safety(FOS)is an important means of slope *** paper proposed an improved double strength reductionmethod(drM)to analyze the safety of layered *** physical properties of different soil layers of the slopes are different,so the single coefficient strength reduction method(SrM)is not enough to reflect the actual critical state of the *** that the water content of the soil in the natural state is the main factor for the strength of the soil,the attenuation law of shear strength of clayey soil changing with water content is *** paper also establishes the functional relationship between different reduction ***,a USdFLd subroutine is programmed using the secondary development function of finite element *** the relationship between field variables and calculation time realizes double strength reduction applicable to the layered ***,by comparing the calculation results of different examples,it is proved that the stress and displacement distribution of the critical slope state obtained by the improved method is more realistic,and the calculated safety factor is more *** newly proposedmethod considers the difference of intensity attenuation between different soil layers under natural conditions and avoids the disadvantage of the strength reduction method with uniform parameters,which provides a new idea and method for stability analysis of layered and complex slopes.

关键词： double strength reduction slopes stability water content factor of safety numerical methods

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design and Simulation of Improved Fast Charging Technique for Modern Electric Vehicle

Design and Simulation of Improved Fast Charging Technique fo...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Sharavanan, M. Karthikumar, K. Karunanithi, K. Veltech Rangarajan Dr. Sagunthala R&d Institude of Science and Technology Department of Eee Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529833

The design and development of an electric car charging station in this study are suitable for a wide range of electric vehicles. When designing and integrating charging stations into real-time projects, the availability and accessibility of charging infrastructure are key considerations. Whereas developing charging systems, charging standards, procedures, charging levels, charging time reduction technique and control strategy. Most of the tasks carried out by the grid with this e-storage technology include charging and controlling the batteries in electric cars. The rules and modelling used in this research design are described in order to demonstrate how the charging system behaves. The findings demonstrate the capability of the suggested paradigm and the potential of the to demonstrate how the charging system behaves. The findings demonstrate the viability of the suggested paradigm and the potential of the charging systems. © 2025 IEEE.

关键词： Charging stations

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Proposal and Implementation of a Hand Gesture Control Method for Manipulation of Indoor Wheeled Mobile robot

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Journal of Institute of Control, robotics and Systems 2025年第1期31卷 44-53页

作者： Yun, Seungho Park, Haeun Park, Suyoung Lee, Seongjae Lee, Hui Sung Department of Design Ulsan National Institute of Science and Technology Korea Republic of Department of Creative Design Engineering Ulsan National Institute of Science and Technology Korea Republic of Department of PGM Core Tech R&D Lab LIG Nex1 Korea Republic of Department of Electrical Engineering Ulsan National Institute of Science and Technology Korea Republic of

Mobile robots are widely used and provide convenience to people in places like restaurants and airports. However, there are situations in which their limited autonomy necessitates additional user interaction. While existing studies suggest using hand gestures to manipulate mobile robots, the practical application of such an approach is limited, as they cannot fully address the needs of the user needs and can just primarily be operated in teleoperation environments. In this work, a novel hand gesture operation method has been proposed that can operate a wheeled mobile robot through a manipulation method. In addition to implementing the suggested gesture recognition technique, representative hand gestures were selected by conducting experiments to identify natural gestures based on context for improving usability. A combination of the YOLOv8-based object recognition and pose recognition models was employed to achieve the hand gesture recognition function. Finally, a wheeled mobile robot was also designed to evaluate the hand gesture manipulation system for performing appropriate movements according to the gesture. The overall system was evaluated through experiments and interviews with participants about the workload and usability of the gesture manipulation system. © ICrOS 2025.

关键词： Embedded systems

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Atrous Convolution-Based residual deep CNN for Image dehazing with Spider Monkey-Particle Swarm Optimization

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Intelligent Automation & Soft Computing 2023年第8期37卷 1711-1728页

作者： CH.Mohan Sai Kumar r.S.Valarmathi Department of Electronics and Communication Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision *** to severe air dispersion,fog,and haze over the environment,hazy images pose specific challenges during information *** the advances in the learning theory,most of the learning-based techniques,in particular,deep neural networks are used for single-image *** existing approaches are extremely computationally complex,and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow ***,the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing *** article proposes a new architecture“Atrous Convolution-based residual deep Convolutional Neural Network(CNN)”method with hybrid Spider Monkey-Particle Swarm Optimization for image *** large receptive field of atrous convolution extracts the global contextual *** swarm based hybrid optimization is designed for tuning the neural network parameters during *** experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo *** is observed from the statistical analysis that Mean Square Error(MSE)decreases from 74.42 to 62.03 and Peak Signal to Noise ratio(PSNr)increases from 22.53 to *** proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.

关键词： Image dehazing computer vision convolutional neural network color distortion over-saturation pseudo-shadow phenomenon convergence rate

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design and implementation of novel variants of multi-level inverters for traction and heavy vehicle applications

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International Journal of Powertrains 2023年第1期12卷 54-80页

作者： Kumar, S. Sri Krishna dhal, P.K. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

In the advent of increased transportation systems, the importance and growth of heavy vehicle movements and their applications play a significant role. The transportation sector involving heavy vehicles needs appropriate components for systematic movements. With the prominence of components utilised in heavy vehicle systems and power train mechanism, this paper develops novel variants of multi-level inverters for effective functioning of the vehicular system. Along with the classic multi-level inverter (MLI), new variants – modified multi-level inverter and reduced switch multi-level inverter are designed with complete operation based on their switching states and are implemented for a nine level output voltage module. The proposed inverter models for the heavy vehicle system is tested and validated based on the total harmonic distortion (THd). For each level and their switching states, THd is evaluated and the inverter performance is proved for its superiority for heavy vehicle system and their applicability in power train applications. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Power quality

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Improving Sign Language recognition for deaf and Hard of Hearing Individuals using the VGG16 Model 3

Improving Sign Language Recognition for Deaf and Hard of Hea...

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3rd International Conference on Automation, Computing and renewable Systems, ICACrS 2024

作者： Benjamin, Minu Inba Shanthini Watson Patan, Yaseen Kumar, Ansilin S.M. reddy, Pocharam Nithyanandh Ahamad, Syed Faheez Puneeth, Muvvala Sai Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331532420

Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greater research to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. recognizing sign language using VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Employee Turnover Prediction on Synthetic and real datasets 9

Employee Turnover Prediction on Synthetic and Real Datasets

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9th International Conference on Computer science and Engineering, UBMK 2024

作者： Murzaeva, Azhar Illik, Volkan Koc, Kadir Yunus Ibss Technology and Software R&d Department Istanbul Turkey

ISBN: (纸本)9798350365887

When an employee leaves the job, it leads to issues such as financial losses, loss of productivity, re-employment costs, the adaptation period for the new employee, and loss of time. Many studies are dedicated to predicting employee turnover to avoid these negative impacts. In this study, we train classic machine learning models to predict employee turnover. We train Logistic regression, decision Tree, XGBoost, random Forest, Gradient Boosting, Ada Boosting, and Support Vector Machine methods. We use two publicly available datasets: IBM's synthetic data and an anonymous US company's real data. We compare the outcomes of models trained by collected real datasets and models trained by synthetically generated datasets. For the IBM dataset, with 92% accuracy and 0.92 f1-score, random Forest demonstrates superior results. Additionally, random Forest out- performed other models with 89% accuracy and 0.89 f1-score for the US company's dataset. All applied classification models demonstrated better results on the synthetic data. © 2024 IEEE.

关键词： Support vector machines

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Segmentation of the Corpus Callosum from Brain Magnetic resonance Images Using dual deep Learning Classifiers and Optimized U-Shaped Neural Networks

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SN Computer science 2024年第1期5卷 1页

作者： Sarma, A. Padmanabha Saranya, G. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Segmentation of the corpus callosum (CC) from Mr images is an important step in neuroimaging analysis for various applications, such as brain morphometry, tractography, and connectivity analysis. This research proposes a deep learning-based model for segmenting CC in T1-weighted midsagittal brain MrIs. The model consists of five major phases, namely, image augmentation, pre-processing, segmentation, feature extraction, and feature selection. The collected raw MrI images are augmented using synthetic minority oversampling technique (SMOTE) and pre-processed using improved Gabor filtering and contrast stretch for contrast enhancement. The region of Interest (rOI) has been identified through optimized U-NETS using a new hybrid optimization model. Shape, texture, and colour features are extracted from the segmented image, from which optimal features are selected through proposed hybrid optimization model. The CC segmentation phase will employ a dual deep-learning classifiers approach, with bidirectional LSTM and convolutional block attention module (CBAM) trained using optimally selected features, and a modified convolutional neural network (M-CNN) to provide final detected outcomes. The proposed model aims to improve the accuracy of CC segmentation in brain MrIs. © The Author(s),.

关键词： Corpus callosum Improved Gabor filtering Modified convolutional neural network (M-CNN) Optimized U-NETS

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An Automated deep Learning-based VAr for Enhancing referee decision-Making in Football 3

An Automated Deep Learning-based VAR for Enhancing Referee D...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IdCIoT 2025

作者： Zaken, Hamza Abu Prasanth, A. Jahjah, Mohamad Ali Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant referee (VAr) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAr system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAr methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating. © 2025 IEEE.

关键词： Convolutional neural networks

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