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Application of AI and RSM to Optimize WEDM Process Parameters on D4 Steel 2

Application of AI and RSM to Optimize WEDM Process Parameter...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Bisht, Yashwant Singh John, Vivek Aggarwal, Saurabh Anandaram, Harishchander Rastogi, Neha Joshi, Kapil Uttaranchal University Mechanical Engineering Department Dehradun India Amrita School of Engineering Centre for Computational Engineering Networking Coimbatore India Uttaranchal University Uttaranchal Institute of Management Dehradun India Uttaranchal University Department of CSE Dehradun India

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

This electronic When precision is required, WEDM is a conductive material machining technology that is widely used. The rough cutting operation in WEDM is regarded tough because precision work demands the use of numerous machining performance measurements, such as metal removal rate (MRR), surface finish (SF), and cutting width (kerfs).According toResponse Surface Methodology, discharge current, pulse frequency, wire-speed, wire tension, and dielectric flow are all key machining characteristics that influence performance metrics (RSM). Each performance metric's best set of parameters has been discovered to be different. This study uses nonlinear regression analysis to stabilize the relationship between control variables and response variables like MRR, SF, and kerf, resulting in a valid mathematical model. Little research has been done to establish the ideal quantity of machining parameters for the best machiningquality in tough machining materials such as hot die steel D4 according to the literature review. Hot die steel D4 is widely utilized in applications such as hot work forging, extrusion, punching tools, mandrels, mechanical press forging dies, plastic molds and die casting dies, aviation landing gears, helicopter rotor blades, and shafts, and others. Wire electrical discharge machining is challenging to obtain consistent quality because the process parameters are difficult to maintain. The most challenging problems for researchers and engineers to address are those listed above. Manufacturers strive to find control elements to increase machining quality based on their operational experiences, manuals,or failed attempts. Electronic Process Tools Limited's wire cut EDM was used to machine D4 hot die steel specifically for this application (Elektra Spirit cut 734). © 2022 IEEE.

关键词： Wire

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Path Planning Solution for Intelligent Robots Using Ex*RRT Algorithm in Disaster Relief 5

Path Planning Solution for Intelligent Robots Using Ex*RRT A...

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5th IEEE International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Hariprasath, V.Y. Mukesh, S. Aruna, T.N. Gayana, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Tiruchirappalli India KGiSL Institute of Technology Department of Information Technology Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350351378

Unmanned Ground Robots (UGVs) are increasingly becoming vital tools in disaster relief operations, offering a means to navigate through hazardous environments where human intervention may be risky or impossible. These intelligent robots are designed to perform a variety of tasks, such as searching for survivors, delivering supplies, and assessing damage. However, the effectiveness of UGVs in such critical missions largely depends on their ability to navigate complex and unpredictable terrains, a challenge that necessitates advanced path-planning strategies. The paper introduces a novel approach to path planning for UGVs in disaster relief scenarios. This approach combines the strengths of advanced rapidly Rapidly-exploring Random Trees (RRT) algorithms with the capabilities of YOLOv4, a state-of-the-art technique for object detection, specifically tailored here for human detection. RRT algorithms are known for their efficiency in exploring complex spaces, making them ideal for navigating through the unpredictable terrains typical of disaster zones. Meanwhile, YOLOv4 enhances the UGVs' ability to detect and respond to human presence, a critical requirement in rescue operations. The UGVs equipped with this novel path-planning strategy were able to navigate safely and efficiently, adapting to the dynamic nature of the environment. More importantly, the accuracy in detecting human presence was significantly improved, a crucial factor in ensuring that aid and rescue efforts are directed where they are most needed. By enhancing the path planning capabilities of UGVs with advanced algorithms and human detection technology, this approach holds the potential to revolutionize disaster response operations, making them more efficient and impactful, ultimately leading to faster and more effective aid delivery to those in dire need. © 2024 IEEE.

关键词： Motion planning

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Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks 2

Optimizing Wireless Sensor Networks in E-Commerce with Messa...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jeyaselvi, M. Saravanan, B. Boobalan, M. Ganesha, M. Lamba, Anil Kumar Suganthi, D. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Sri Eshwar College of Engineering Department of Cse Tamilnadu Coimbatore India Vivekanandha College of Engineering for Women Tiruchengode Namakkal India Ajiet Department of Cse Karnataka Mangaluru India Geeta University School of Computer Science and Engineering Haryana India Saveetha College of Liberal Arts and Sciences Simats Department of Computational Intelligence Thandalam Chennai India

ISBN: (纸本)9798350388916

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM. © 2024 IEEE.

关键词： Long short-term memory

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INFLUENCE of ADDITIONAL SPECTRAL BANDS for EPIPHYTE SEGMENTATION on DRU-NET

INFLUENCE of ADDITIONAL SPECTRAL BANDS for EPIPHYTE SEGMENTA...

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2022 Annual Conference, ASPRS 2022

作者： Menon, Anjana K. Sajith Variyar, V.V. Sivanpillai, Ramesh Sowmya, V. Brown, Gregory K. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore India Wyoming GIS Center University of Wyoming LaramieWY82072 United States Department of Botany University of Wyoming LaramieWY82072 United States

Dense Residual U-Net (DRU-Net) is a neural network used for image segmentation. It is based on the U-Net architecture and isa combination of modified ResNet as the encoder and modified DenseNet as the decoder blocks. DRU-Net captures both the local and contextual information. Previous studies on DRU-Net have not tested the influence of the spectral resolution of the images. In an earlier study, the DRU-Net was trained with grayscale images for epiphyte segmentation. The network trained and tested with grayscale images underperformed while varying the illumination and occupancy of the target in the frame. In this study, the same network was trained and tested with RGB images for assessing the increase in overall learning. The performance of the network in segmenting epiphytes under conditions such as good/poor illumination and high/low target occupancy was analyzed. Dice and Jaccard scores were used as evaluation metrics. The DRU-Net model trained with RGB images had an improvement of 20% over the grayscale model in both average Dice and average Jaccard scores of the target class. Based on the higher Dice and Jaccard scores, adding additional spectral information improves DRU-Net learning. The increased computation time required for training DRU-Net with RGB images will result in better output. This model could be further used for identifying multiple epiphytes in images with poor illumination and different occupancy conditions. © 2022 A. K. Menon et al.

关键词： Image segmentation

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Real-Time Human Action Recognition with Multimodal Dataset: A Study Review 5th

Real-Time Human Action Recognition with Multimodal Dataset: ...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Joshi, Kapil Rastogi, Ritesh Joshi, Pooja Anandaram, Harishchander Gupta, Ashulekha Mohialden, Yasmin Makki Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of IT Noida Institute of Engineering and Technology Greater Noida India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Dehradun India Computer Science Department Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9789819906000

Due to difficulties including a cluttered background, partial occlusion, and variations on dimensions, angle, illumination, or look, identifying human endeavors using video clips or still images is a challenging process. A numerous mechanism for recognizing activity is necessary for numerous applications, such as robotics, human–computer interaction, and video surveillance for characterizing human behavior. We outline a classification of human endeavor approaches and go through their benefits and drawbacks. In specifically, we classify categorization of human activity approaches into the two broad categories based on whether or not they make use of information from several modalities. This study covered a depth motion map-based approach to human recognizing an action. A motion map in depth created by adding up to the fullest differences with respect to the two following projections maps for each projection view over the course of the entire depth video series. The suggested approach is demonstrated to be computationally effective, enabling real-time operation. Results of the recognition using the dataset for Microsoft Research Action3D show that our method outperforms other methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Revolutionizing Face Detection: VisionaryNet's Breakthrough in Unraveling Facial Camplexity 2

Revolutionizing Face Detection: VisionaryNet's Breakthrough ...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Nallarasan, V. Arivazhagan, N. Lakshminarayanan, R. Sharmila, D. Sangolgi, Nagaveni B Anand, M. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sharnbasva University Department of Electronics and Communication Engineering Kalaburagi India Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur India

ISBN: (纸本)9798350387933

The initial step in facial recognition and analysis systems often involves facial detection. Traditional methods like Haar cascades and directed gradient histograms relied on manually designed features, limiting their effectiveness in diverse image environments. However, the emergence of deep learning in computer vision has accelerated the development of face detection frameworks, significantly improving accuracy. Detecting small-scale faces, varying positions, occlusions, blurs, and partially obscured faces in uncontrolled settings remains a persistent challenge in face identification. To address this, we propose a new approach called 'Visionary Net.' This single-stage face detector has undergone refinements, boosting both detection speed and accuracy. We tested Visionary Net on two well-known datasets: Mega Face and CelebA. Notably, on the Mega Face benchmark, our method achieved an impressive AP of 48.5 at a speed of 15.2 FPS using single-scale inference and an exceptional AP of 52.7 with multi-scale inference. Implementing it with the PyTorch framework resulted in a remarkable 97.3% accuracy in successfully detecting faces. Our comprehensive experiments show that Visionary Net surpasses existing models, demonstrating superior detection and recognition capabilities across various evaluation metrics. This innovative algorithm holds significant promise for robust face detection in challenging real-world scenarios. © 2024 IEEE.

关键词： Face recognition

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Technical and Economic Sustainability of Alternative Livelihood Strategies at the Grassroots: A Case Study in Byse, Karnataka, India 8th

Technical and Economic Sustainability of Alternative Livelih...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Cornet, Souresh Viswanathan, P.K. Winkler, Jake Praveen, Neeraja Sengupta, Angan Sneha, S. Geetha, P. Amrita School for Sustainable Futures Amrita Vishwa Vidyapeetham Amritapuri India Amrita School of Business Amrita Vishwa Vidyapeetham Amritapuri India School of Management University at Buffalo BuffaloNY United States Amrita School of Business Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819766741

In the context of climate change and agriculture distress, Indian rural areas increasingly need to develop non-agriculture-related livelihood opportunities. This is to ensure year-long stability, irrespective of seasons and natural calamities. However, the rural context poses many challenges in terms of socio-cultural complexity and resource constraints. This study aimed to better understand how to develop alternative livelihood opportunities at the grassroots that could be sustainable in the long term. It explores the reality of rural areas through the case study of a community in the state of Karnataka, India. A multidisciplinary, collaborative, and participatory approach is undertaken to thoroughly understand the community and identify the key technical and business requirements for developing a new income-generating activity in that specific community. Guidelines for developing this activity are provided. Findings illustrate the synergies between the technical and business aspects when developing livelihood opportunities, and outline the importance of a multidisciplinary and bottom-up approach to capture the complexity of rural communities when conducting grassroots projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sustainable agriculture

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Artificial Intelligence Framework for COVID Protocol Detection

Artificial Intelligence Framework for COVID Protocol Detecti...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd International Conference on

作者： Kabilan N Sajith Variyar V V Adithan P Sowmya V P S Sai Ganesh Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781665499453

Artificial intelligence is now penetrating into all the domains. Any domain can incorporate artificial intelligence to automate their process. In the outbreak of COVID pandemic, artificial intelligence has been very useful in many ways. artificial intelligence helps in automating process where it’s not always possible for people to do and to reduce the wastage of human resource. Here we proposed a frame work to automate the detection of covid protocol violation in public places. Our work detecting people with & without masks and detects social distancing with a single model. The best performing model from the standard convolution neural network architectures namely VGG16 and MobileNetV2 are used in the present work, from the experiments it’s found that MobileNetV2 outperformed VGG16. The developed system can easily be integrated/implemented on various embedded devices with limited computational capacity by using the MobileNetV2 architecture. Compared to other previous works, our work outstands by having good accuracy and compatible to use in real life application because of its requirement of less computational complexity.

关键词： COVID-19 Protocols Pandemics Convolution computational modeling Neural networks Human factors

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Ros-based pedestrian detection and distance estimation algorithm using stereo vision, leddar and cnn 6th

Ros-based pedestrian detection and distance estimation algor...

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6th International Conference on Information System Design and Intelligent Applications, INDIA 2019

作者： Mukherjee, Anjali Adarsh, S. Ramachandran, K.I. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwavidyapeetham Coimbatore India Centre for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwavidyapeetham Coimbatore India

ISBN: (纸本)9789811553998

Pedestrian detection systems are of paramount importance in today’s cars, as we depart from conventional ADAS toward higher levels of integrated autonomous driving capability. Legacy systems use a variety of image processing methodologies to detect objects and people in front of the vehicle. However, more robust solutions have been proposed in recent years with the advent of better sensors (lidars, stereo cameras and radars) along with new deep learning algorithms. This paper details a ROS-based pedestrian detection and localization algorithm utilizing ZED stereo vision camera and Leddar M16, employing darknet YOLOv2 for localization, to yield faster and credible results in object detection. Distance data is obtained using a stereo camera point cloud and the Leddar M16, which is later fused using ANFIS. YOLOv2 was trained using the Caltech Pedestrian dataset on a NVIDIA 940MX GPU, with the sensor interfacing done via ROS. © Springer Nature Singapore Pte Ltd 2021.

关键词： Stereo vision

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