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Global Conference for Advancement in Technology (GCAT)

作者： Firoj Parwej Suganya A Burri Naresh Veer Sudheer Goud S. Kaliappan G. Manikandan Department of Computer Science and Engineering Mandsaur University Daulatpura Mandsaur Madhya Pradesh India Department of Information Technology Karpagam College of Engineering Coimbatore Tamilnadu India Department of CSE -CS CVR College of Engineering Hyderabad Telangana India Deptartment of AI&DS St. Martin's Engineering College Secunderabad India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This research introduces SmartFit, a state-of-the-art technology that seamlessly integrates biometric data and AI-powered training recommendations. SmartFit monitors the user's pulse rate, oxygen saturation, and muscle activity in real time. Subsequently, it implements machine learning algorithms to develop customized training regimens. This ongoing adjustment to the user's evolving fitness levels and objectives guarantees that the workout regimens are both highly effective and customized to their unique requirements. The proposed system is designed to optimize exercise efficacy, promote extended engagement in fitness activities, and improve user satisfaction. The novel method significantly enhanced the fitness ratings and overall satisfaction of participants in comparison to more conventional forms of exercise, as evidenced by a three-month pilot study. The results demonstrate the potential for SmartFit's data-driven, individualized training recommendations to transform the fitness industry.

关键词： Training Biometrics Machine learning algorithms Transforms Machine learning Muscles Real-time systems User experience Monitoring Optimization

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Analysis of Convolutional Neural Network Architectures for Image Denoising and Restoration

Analysis of Convolutional Neural Network Architectures for I...

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Automation and Computation (AUTOCOM), International Conference on

作者： Mukul Mishra Rahul Gupta Anilloy Augustine Frank Trapty Agarwal Vibhor Mahajan Sushree B. B. Priyadarshini Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Convolutional Neural Networks have performed excellently in image processing applications such as Image Demising and Restoration. CNN's performance and accuracy depend on its design and architecture. This work analyzes delousing and image restoration using different CNN architectures. In this experiment, three popular CNN architectures, U-Net, Res Net, and Dense Net, are trained on different noise levels. In this section, we analyze each parameter for the proposed CNN models, i.e., filter size and depth, etc., to evaluate performance variations due to these hyper parameters. Our results indicate that with a longer network and large filter sizes, it may be possible to deny the images more accurately for restoration. This development set opened as we experimented with fine-tuning the other tasks (DE noising and restoration) using pretrained models. We provide empirical evidence that has shown that incorporating pretrained models can result in a substantial improvement in the performance of CNNs with a limited amount of data. This work represents a complete investigation of CNN architectures, such as DE noising and restoration, and explains why specific engineering choices are effective. These results can be useful in enhancing accuracy and performance for image processing tasks using CNN models.

关键词： Visualization Accuracy Noise reduction Training data computer architecture Image restoration Convolutional neural networks Kernel research and development Noise level

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Reinforcement Learning-Based Optimal Power Flow Control in Islanded Microgrids

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

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Automation and Computation (AUTOCOM), International Conference on

作者： Ankita Agarwal Amit Kumar Dipankar Maity Varun Ojha Nalini Sunil M Aruna Dore Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

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.

关键词： Adaptation models Costs Power system dynamics Stochastic processes Reinforcement learning Power system stability Predictive models Stability analysis Load flow control Load flow

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Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Microgrid Control

Deep Learning-Based Forecasting of Solar Photovoltaic Genera...

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Automation and Computation (AUTOCOM), International Conference on

作者： K. Ankith Kumar Mohan Garg Manita D Shah Kalyandurg Rafeeq Ahmed Trapty Agarwal Vivek Saraswat Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control is a new approach to accurately predicting solar photovoltaic generation in micro grids. These feature renewable energy such as Solar PV and are a responsive approach for providing efficient, sustainable power solutions. In contrast, solar PV power production is not dispatch able because it depends on the changing weather and other factors. The new method applies deep learning algorithms, mainly artificial neural networks, to historical data to accurately forecast solar PV output. The latter sorts the variation due to a broad array of meteorological and operational parameters, including temperature, humidity level, cloud cover, and shading Moreover, the model can forecast results online and keep learning with new data for real-time micro grid control. The deep learning-based method has shown superior performance to traditional forecasting methods in improving the precision of PV generation forecasts. Benefits include more efficient energy management, better integrating renewable sources into the micro grid, and lower costs. This means that the Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control could significantly improve the reliability and efficiency of micro grid operations and promote a more sustainable and resilient energy system.

关键词： Photovoltaic systems Deep learning Renewable energy sources Temperature distribution Weather forecasting Predictive models Real-time systems Temperature control Reliability Forecasting

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Real-Time Object Detection Using Convolutional Neural Networks for Autonomous Vehicles

Real-Time Object Detection Using Convolutional Neural Networ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Premalatha K P Divyarani M S Pavan Chaudhary Anoop Dev Narayan Patra Shreya Ghosal School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This is where timely and accurate detection by autonomous cars becomes vital. Due to this, Convolution Neural Networks have gained the utmost popularity in object detection and are showing great potential in performing all sorts of sort's r vision tasks. In this project, we propose a Convolution Neural Networks based real-time object detection system for autonomous vehicles. Our system detects and localizes real-time objects based on deep neural network architecture. The function of our software is to take an image from the sensors, like cameras or Liar, on board this car. This image is further input to CNN, which takes these features, extracts the object, and classifies them into a scene. As we previously mentioned, CNN is trained with a large dataset of labeled images, allowing it to recognize hundreds or thousands of different objects accurately. This is then computed further in the CNN and attempts to find bounding boxes around detected objects, giving where that object exists spatially in a scene. Our system's response time has been designed for real-time analysis and should be able to analyze images and detect objects in about milliseconds. This, in return, enables autonomous vehicles to make immediate decisions when considering these objects and navigate safely. Our system allows for real-time object detection, opening the door to high-performance and safe autonomous vehicles that bring us closer than ever before to achieving full autonomy driving.

关键词： Image recognition Three-dimensional displays Convolution Object detection Feature extraction Real-time systems Convolutional neural networks Automobiles Time factors Autonomous vehicles

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Deep Learning-Based Speech Enhancement for Robust Speech Recognition in Noisy Environments

Deep Learning-Based Speech Enhancement for Robust Speech Rec...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sowmya C S Neeraj Das Divya Sharma Sanjoy Mondal Ishika Soni Naveen Kumar V Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

It is a very important problem since voice-controlled devices and speech-to-text transcription are only two examples of how automatic recognition of spoken language in noisy environments may impact our lives. Still, speech recognition systems may only sometimes work effectively in background noise, which can result in errors and misinterpretations. This problem dealt with the deep learning model-based speech enhancement techniques for robust ASR in adverse environments. These methods employ deep learning to learn and deny the noisy speech signals, rendering it a better-suited input for recognition in a typical ASR system. Deep learning-based speech enhancement is a machine-learning system using a huge, loud, clean speech data dataset to train DNN. So, the network is trained to find a mapping from some noisy speech signals to ideally clean versions of these speech sounds by removing its background noise. This spectral representation of speech will now have reduced noise, which the ASR much more easily recognizes. These methods have significantly improved against traditional speech enhancement techniques in demising, eventually improving recognition performance. They can turn in high noise levels and are resilient against acoustics drifts. This makes them well-suited to real-world applications when noise levels can vary.

关键词： Deep learning Noise Speech recognition Artificial neural networks Speech enhancement Noise measurement Background noise Tuning Noise level Speech to text

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Intelligent Energy Management System for Microgrids with Energy Storage Integration

Intelligent Energy Management System for Microgrids with Ene...

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Automation and Computation (AUTOCOM), International Conference on

作者： Simranjeet Nanda Maddu Naveen Kumar Paramjit Baxi Monoo John Manaswini R Dhananjay Kumar Yadav Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement-PG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The items are a software solution, it is designed for micro grids with energy storage integration to monitor, control, and best utilize available grid resources. Also called a mini-grid, the micro grid is a distributed energy system consisting of local small-scale generation and storage facilities that could operate independently from the main grid in an emergency. Micro grids are frequently paired with solar panels and wind turbines, making them more sustainable. The capacity of energy storage systems in micro grids has been playing an important role, as renewable energy in devices with temporal availability generates the need for their more efficient use. Based on real-time data, the items apply energy transmission and distribution management algorithms to control where the power is used in a micro grid. The items continuously monitor the demand and supply of energy to investigate the ideal combination of renewable generation and stored energy for peak shaving. In doing so, the micro grid can run optimally and minimize energy waste without running up bills. The items can also predict how and when individuals use energy, thus allowing better scheduling of the available resources. This enables the items to conduct predictive maintenance on those micro grid components to long and function as efficiently as possible.

关键词： Renewable energy sources Real-time systems Software Wind turbines Reliability Solar panels Energy management systems Monitoring Energy storage Predictive maintenance

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Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

arXiv

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arXiv 2024年

作者： Tang, Xiao Jiang, Yudan Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu School of Information and Communication Engineering Xi’an Jiaotong University Xi’an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronics and Information Northwestern Polytechinical University Xi’an710072 China School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Houston Houston77004 United States

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination. Copyright © 2024, The Authors. All rights reserved.

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Cognitive Computing for Smart City Infrastructure Management

Cognitive Computing for Smart City Infrastructure Management

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Advances in Computing, communication and Applied Informatics (ACCAI), International Conference on

作者： P Murthi Jyoti Jayesh Chavhan Prithu Sarkar Kritika Khandelwal Shaik Chandini Joshuva Arockia Dhanraj Department of Civil Engineering School of Engineering SR University Warangal Telangana India Computer Engineering SIES Graduate School Of Technology Mumbai University Maharashtra Amity School of Communication Amity University Kolkata Department of Business Administration Manipal University Jaipur Jaipur-Ajmer Express Highway Dehmi Kalan Near GVK Toll Plaza Jaipur Rajasthan Department of Civil Engineering Gulf American University UAE University Centre for Research & Development (UCRD) Chandigarh University Mohali Punjab India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The accelerated urbanisation and the increasing complexity of municipal infrastructure require the implementation of innovative strategies to regulate and improve urban environments. By integrating By integrating Cognitive computing, which combines AI, machine learning, and data analytics, could completely change how smart cities manage their infrastructure. The main goal of this project is to see what the possible benefits are of adding cognitive computing to the systems that are already in place, architecture of smart cities, resource allocation, enable predictive maintenance, facilitate informed decision-making, and provide real-time monitoring. We demonstrate the potential of cognitive systems to enhance the durability, duration, and efficacy of urban infrastructure through the examination of case studies and contemporary technological advancements. The results suggest that cognitive computing is the most effective solution for addressing current urban challenges and, as a result, is the most suitable choice for the development of intelligent cities in the future.

关键词： Data analysis Smart cities Decision making Machine learning Real-time systems Resource management Cognitive systems

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Optimizing Controller Tuning using Bio-Inspired Algorithms for Robot Path Tracking

Optimizing Controller Tuning using Bio-Inspired Algorithms f...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Taneti Lilly Rani S. Deepa Rakesh Kumar Joon P Jayaseelan Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Civil Engineering Jawaharlal Nehru Technological University College of Engineering Kakinada (University College of Engineering in JNTU Kakinada Andhra Pradesh India Department of Computer Science and Business Systems RMD Engineering College Kavarapettai Tamil Nadu India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Kablana Haryana Department of Mechanical Engineering Achariya College of Engineering Technology Puducherry India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India University Center for Research &amp Development (UCRD) Chandigarh University Mohali Punjab India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

In the field of industrial and commercial robotics, there has been a recent boom in research towards the navigation of wheel-based mobile robots. There are numerous aspects of this complex problem that need fixing, including issues with sensory data gathering and processing, trajectory planning, and controlling motion. In the field of motion control, creating reliable trajectory controllers is the highest priority. This study improves the Proportional-Integral-Derivative (PID) controller using Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO) to get the best possible trajectory tracking. The kinematics of a two-wheeled mobile robot serve as the foundation for its mathematical model. The user-specified reference trajectory is given to the robot. The conventional PID, ACO-PID, and CSO-PID controller's trajectory tracking capacity are evaluated using the metrics. The performance metrics such as the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) are taken. According to the simulation results, the CSO-PID controller beats the conventional PID and ACO-PID controllers in terms of error metrics. This discovery highlights the effectiveness of using CSO-PID to improve the trajectory-tracking performance of mobile robots.

关键词： Measurement Accuracy Trajectory tracking Simulation Robot sensing systems Mathematical models Trajectory

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