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4th International Conference on Materials Chemistry and Environmental engineering, CONF-MCEE 2024

作者： Hu, Xinyu Pei, Haoyu School of Computer and Electronic Information Science Nanjing Normal University Nanjing210046 China College of Mechanical Engineering Dalian University of Science and Technology Dalian116036 China

A multiple robots system provides advantages over a single robot when it comes to exploring an unfamiliar area. Due to its applications in localization and mapping, search and rescue, etc. This technology has attracted a lot of study interest. The main challenge is properly distributing target points among several robots so that they can each investigate a separate section of the environment simultaneously and ensure the shortest possible total exploration time. However, more work still needs to be done to incorporate research issues in a real setting, such as how to eliminate duplicate exploration areas by giving robots multiple target locations or how to avoid unanticipated barriers in new situations. The allocation algorithms, motion planning techniques, and exploration strategies used in multi-robot collaboration (CME) are reviewed in this paper. Additionally, it provides an explanation of the methods research teams employ to optimize traditional whale optimization algorithm (WOA), how they enhance exploration performance and speed, and a comparative analysis of these methods. We also discussed a cooperative exploration method based on dynamic Voronoi partitioning. However, we also need some obstacle avoidance algorithms to help understand the gaps and issues that will need to be solved in the upcoming years, namely how to handle unexpected impediments in unfamiliar surroundings. Lastly, a summary of potential future lines of inquiry and uses is given. © Published under licence by IOP Publishing Ltd.

关键词： Multipurpose robots

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A Systematic Investigation of Cyber Harassment Intention Behaviors and Its Impacts on Social Media Platforms

A Systematic Investigation of Cyber Harassment Intention Beh...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Abarna, S. Sheeba, J.I. Devaneyan, S. Pradeep Puducherry Technological University Department of Computer Science and Engineering India Sri Venkateshwaraa College of Engineering and Technology Department of Mechanical Engineering Puducherry India

ISBN: (纸本)9798350348217

Several technologies have been able to fundamentally affect our lives in just a few decades by integrating themselves into our society. News and opinion are now mostly shared on social media. There are both positive and negative aspects to this. Nowadays, practically everyone has a smartphone or other internet-accessing gadget. Modern technology becomes available to kids at progressively younger ages. The majority of them use social media, which contributes to the rise of cyber harassment and the negative aspects of the internet. Due to the ease of access to these platforms, cyberbullying is prevalent on social media. Moreover, when dealing with others, the perpetrators have the ability to remain anonymous. Cyberbullying statistics for 2022 highlight the alarming incidence of harassment in the virtual world, and they are disturbing. According to these data, Cyberbullying sufferers are more than twice as likely to self-harm or entertain suicidal thoughts as a coping mechanism. Anxiety and sadness might develop in certain victims of cyberbullying. Psychologists believe that social media has contributed to an increase in stress and depression over the last decade. On the other side, the most typical targets for online bullies are young children, women, and individuals with non-traditional sexual orientations. Students are often unable to concentrate on their studies because they are afraid of the trolls. As a result, this Research Essential focuses on Cyber harassment statistics and facts in order to raise awareness among parents, students, and authorities worldwide and reveal alarming facts about virtual harassment, the intention of the harasser, its impact, the relationship between victim and harasser, and its many different shapes and forms. © 2023 IEEE.

关键词： Social networking (online)

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge 10

Investigation of Effective Electrode Structure for Metal Rem...

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch Technology, EAPPC-BEAMS-EML 2024

作者： Doi, Takuto Oishi, Shota Sakugawa, Takashi Nishimura, Kaisei Shobako, Shinichiro Shimasaki, Shinichi Yamashita, Tomohiko Kumamoto University Graduate School of Science and Technology Department of Computer Science and Electrical Engineering Kumamoto Japan Kagawa College National Institute of Technology Department of Electro-Mechanical Systems Engineering Takamatsu Japan

ISBN: (纸本)9789038661353

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-To-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density. © 2024 Eindhoven University of Technology.

关键词： Tin oxides

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An Optimized Centric Method to Analyze the Seeds with Five Stages Technique to enhance the Quality 2

An Optimized Centric Method to Analyze the Seeds with Five S...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Jemi Gold, P. Maheswari, K. Reddy, P. Nithish Rajan, T. Surulivel Kumar, S Sandeep Vidhya, R.G. Rmk College of Engineering and Technology Department of Computer Science and Engineering India Cmr Technical Campus Department of Computer Science and Engineering Telangana Kandlakoya India Sreenidhi Institute of Science and Technology Department of Mechanical Yamnampet India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Koneru Lakshmaiah Education Foundation Greenfields Department of Computer Science and Engineering Guntur District Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Agriculture and food processing depend on seed quality analysis. This abstract describes a five-stage, improved seed analysis method. Seed collection and preparation provide representative samples and proper cleaning. The seeds are visually inspected to remove damaged or infected seeds before further investigation. Advanced imaging or machine vision techniques measure size, shape, color, and texture in the second stage. This quantitative study helps seed classification and grading. Biochemical analysis evaluates moisture, protein, oil, and nutritional content in the third step. Near-infrared spectroscopy and chromatography are used to accurately measure these biological properties. The fourth step examines the seed's germination and viability. This stage evaluates seed dormancy, vigor, and establishment potential. Genetic analysis, using molecular markers or DNA-based methods, is the final stage. This stage helps breeders and selectors understand seed genetics. Seed quality analysis can be done efficiently by merging these five processes into an optimized focused method. The suggested method increases seed quality assessment accuracy, reliability, and objectivity, improving seed selection, storage, and use decisions. The abstract emphasizes quality improvement during analysis. Eliminating damaged seeds, using advanced imaging, biochemical assessments, measuring germination potential, and considering genetic features can improve the seed supply chain. Optimized centric analysis improves seed quality analysis. The findings help academics, agronomists, and seed producers implement sophisticated seed quality evaluation methods. This enhanced focused method analyzes seeds efficiently and comprehensively. It improves seed quality assessment and decision-making in the agricultural and food processing industries by using a five-stage technique. © 2023 IEEE.

关键词： Food processing

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Scenarios of AI in Smart Grid Systems

Scenarios of AI in Smart Grid Systems

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Sanjay Upadhyay, Rimpi Haripriya Department of Mechanical Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

The power system exhibits traits of openness, ambiguity, and complexity due to the implementation of electricity market reform and the utilization of renewable energy and power demand response in various circumstances. The development and implementation of intelligent power grid systems have emerged as a prevailing phenomenon. The utilization of artificial intelligence (AI) within the context of smart grid infrastructure offers robust technological assistance for the development of digital power networks. This research presents a novel approach that uses Long Short-Term Memory (LSTM) to forecast an institutional building's energy use. The suggested approach makes use of weather forecasts to estimate daily energy consumption ahead of time. The Meteorological Department provides weather forecasts, which allow the model to represent the complex link between energy usage and meteorological conditions. The performance of the LSTM model is compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR) in order to evaluate its efficacy. © 2024 IEEE.

关键词： AI infrastructure Artificial Intelligence energy generation Smart grid systems technology

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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Need for Artificial Neural Networks in today’s world 4th

Need for Artificial Neural Networks in today’s world

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4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Siddesh Kumar, N.M. Promod Kumar, B.M. Haloor, Bheem Kumar Haran, Pranadhi Manoj Gowda, K.S. Hemashree, S.S. Department of Mechanical Engineering PES College of Engineering Mandya India Department of Computer Science and Engineering PES College of Engineering Mandya India Department of Electrical and Engineering PES College of Engineering Mandya India Department of Information Science and Engineering PES PES College of Engineering Mandya India

ISBN: (纸本)9781032426853

Artificial Neural Networks (ANNs) are widely used in various fields due to their versatile applications. They are capable of handling complex functions and can adapt to new conditions. Their applications range from data interpretation, drug design, pattern recognition, text translation, credit card fraud detection, medical diagnosis and more. ANNs have become an essential tool in today’s world due to their ability to interpret large amounts of data, making them crucial in fields such as medical diagnosis and credit card fraud detection. The versatility of ANNs makes them applicable in many different industries and continues to contribute to their growing popularity. The current work focuses on exploring the wide range of applications of ANNs and their significance in our world today. © 2024 The Author(s).

关键词： Diagnosis

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A Study on the Performance Improvement of Axial Flux Permanent Magnet Machines Based on Stator and Rotor Design Modifications

A Study on the Performance Improvement of Axial Flux Permane...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yun, Jangho Grainger, Brandon Ohodnicki, Paul University of Pittsburgh Department of Mechanical Engineering and Materials Science PittsburghPA United States University of Pittsburgh Department of Electrical and Computer Engineering PittsburghPA United States

ISBN: (纸本)9798350376067

Axial flux permanent magnet (AFPM) machines have technological competitiveness for electric propulsion systems that require compact structures and high-power density. However, challenging design and manufacturing problems that reduce the cost competitiveness cause designers to limit designable variables. In this paper, the effects of slot/pole combination, semi-closed slot, magnetic wedge, and segmented PM on performance in double-sided rotor and double-sided stator geometries designed with stator open slots were investigated through 3-D finite element analysis (FEA). FEA results show that the design variables have a significant impact on torque, torque ripple, and losses. Semi-closed slots or magnetic wedges reduce PM losses for each open slot model by more than 80%, significantly reducing losses compared to segmented PMs, and also reducing torque ripple. As an alternative to stator design modification, results indicate the open slot model can be considered with a slot/pole combination for low cogging torque to reduce torque ripple and segmented PMs to reduce PM losses. © 2024 IEEE.

关键词： Stators

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A Machine Learning Based Prediction of Machining Characteristics of Super Alloy in EDM Using Green Synthesized Nano Copper Oxide Dispersed Bio Dielectric Fluid 1st

A Machine Learning Based Prediction of Machining Characteris...

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1st International Conference on Advanced Materials Manufacturing and Structures, ICAMMS 2024

作者： Baskar, S. Pragadish, N. Shanmugapriya, P. Perumal, S. Vishvanath Department of Mechanical Engineering Rajalakshmi Engineering College Thandalam Tamilnadu Chennai602105 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai600124 India Computer Science and Business Systems Rajalakshmi Engineering College Thandalam Chennai602105 India Department of Mechanical Engineering S.A. Engineering College Tamilnadu Chennai600077 India

ISBN: (纸本)9783031725265

This present study describes the various machine learning techniques such as artificial neural network (ANN), and support vector machine (SVM) are used to predict and genetic algorithm (GA) is used to optimize the material removal rate (MRR) and surface roughness (SR) of Ti6Al4V in electrical discharge machining (EDM) by using nano copper oxide dispersed bio dielectric fluid. MRR and SR were estimated during EDM under the various operating parameters such as discharge voltage, current and discharge time. In this study, an optimal ANN architecture has been determined as 3-10-10-2 and SVM factors were altered by using grid search method. The predicted R-value of SVM is 0.9999 and the minimum mean absolute percentage error for MRR is 0.0085% and SR is 0.0564%. In addition to that, GA optimization has been employed by using objective function developed from Box-Behnken design and enhanced 6.41% and 16.94% in MRR and SR respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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