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GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions 2

GANs for Synthetic Environmental Data Generation (Air Qualit...

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2nd international conference on advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Verma, Seema Jain, Neha Mahapatra, Madhumita Setia, Sonia Sachdeva, Vrinda Kumar, Manoj Dept. of Computer Science & Engineering Delhi Technical campus Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering Sharda University Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering G.L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331541217

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality. © 2024 IEEE.

关键词： Generative adversarial networks

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Comprehensive Analysis of Deep Learning in Spine Image Segmentation

Comprehensive Analysis of Deep Learning in Spine Image Segme...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Khan, Shaeba Shrivastava, Laxmi Bhadauria, Sarita Singh Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal India Madhav Institute of Technology and Science Gwalior India Soit Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal India

ISBN: (纸本)9798350388800

A clinical diagnosis and treatment plan for spinal disorders must start with an imaging assessment. The complicated structure of the spine is encircled by nerve tissue, blood vessels, and muscles. Spine morphology can be easily altered by pathological alterations. Precise division of spine images can be useful. Physicians are able to precisely identify lesions, assess lesions quickly, and direct surgical intervention. Because of its strong feature learning capabilities, deep learning has been applied extensively in the field of spine segmentation in recent years. We compare the traditional deep learning frameworks, provide the widely used data sets and evaluation indicators in spine image segmentation, and provide an overview of CNN, FCN, U-Net, and GAN in order to examine the state of research and development of deep learning in spine image segmentation tasks. The most recent developments in the use of various network models for spine segmentation are presented, along with an analysis of the models' characteristics, a detailed discussion of the difficulties and issues that deep learning is currently facing, and a prospect for future development. © 2024 IEEE.

关键词： Image segmentation

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A Low-Cost Wearable Exoskeleton for Sitting and Standing Assistance 20

A Low-Cost Wearable Exoskeleton for Sitting and Standing Ass...

引用

20th international conference on Body Sensor Networks

作者： Blalack, Kayla Wang, Leo Maldonado, Maximus Marbury, Lauren Zare, Soroush Sun, Ye Univ Virginia Dept Mech & Aerosp Engn Charlottesville VA 22903 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA USA

ISBN: (纸本)9798331530143

This paper presents the design, prototyping, and testing of a low-cost wearable exoskeleton that assists sitting-to-standing and standing-to-sitting (STS) transitions for users with lower limb mobility impairment. This exoskeleton design utilizes the mechanism of invisible chairs as the supporting structure and powers the knee and hip joints to provide fast and smooth transitions between sitting and standing. This exoskeleton features low cost, and minimally invasive design, making it suitable for everyday use. The study highlights the potential of this device to improve the quality of life for elderly individuals by promoting safer and more independent mobility.

关键词： Lower-limb exoskeletons assistive technology sit-to-stand transition elderly mobility wearable robotics

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Advancements and Challenges in Federated Learning : A Survey

Advancements and Challenges in Federated Learning : A Survey

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Prajapat, Vivek Singh Rathore, Narendra Pal Sethi, Kamal Kumar Rajput, Shiv Shankar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains like healthcare and finance, have impeded its widespread adoption. Federated Learning (FL), pioneered by Google in 2016, presents a promising solution by enabling devices to collaborate on model training without sharing raw data. In FL, each device retains its data locally, and only model updates are exchanged with a central server for aggregation. This decentralized approach preserves data privacy while still improving model performance through collaborative learning. FL has garnered interest across industries, with approximately 32% of companies intending to integrate it into their systems soon. Furthermore, investment in FL is projected to rise substantially from 107 million in 2020 to 538 million by 2025, as indicated by forecasts from KPMG. This paper provides an overview of FL, its applications across various sectors, current adoption trends, and future growth prospects, highlighting its significance in addressing data privacy concerns while advancing machine learning capabilities. © 2024 IEEE.

关键词： Federated learning

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Customized Touch UI Development for E-Cockpit in Automobile 3

Customized Touch UI Development for E-Cockpit in Automobile

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3rd international conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Sivaraj, D. Rathika, P.D. Jayasree, D. Dwarkesh Psg College of Technology Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Robotics and Automation Engineering Coimbatore India Psg Polytechnic College Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370867

In recent times, the automotive industry has made significant steps towards digitization by focusing on electronic cockpits (E-Cockpits) to enhance the driving experience, comfort and safety. The incorporation of Touch-Based User Interface (UI) technologies has played a crucial role in revolutionizing Driver-Vehicle interaction [10]. The project of E-Cockpit represents a concept that consolidates various internal car systems onto one unified digital platform, enabling seamless control and access to essential infotainment, safety features, and connectivity services [14]. For the realization of the complete potential of an E-Cockpit, it is important to develop an innovative and User-Friendly touch UI as this directly influences both user experience and safety concerns. © 2023 IEEE.

关键词： User interfaces

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Weak Signal Detection technology in Big Data Based on Knowledge Base 24

Weak Signal Detection Technology in Big Data Based on Knowle...

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2nd international conference on advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Lei School of Cixi Zhejiang Business Technology Institute Ningbo China

ISBN: (纸本)9798400712883

With the development of science and technology and the progress of data processing technology, big data weak signal detection is playing an increasingly important role in scientific research and engineering application. However, the existing detection systems face three main challenges: high false alarm rate and redundant information problems, difficulty in identifying multi-target dispersed homologous signals, and complex signal detection problems with spatio-temporal dispersion. To solve these problems, this paper proposes a weak signal detection model based on knowledge base, by integrating the data of distributed computing framework and ESOM time series analysis methods into the system design, and adds data filtering and signal analysis and state evaluation engine to the existing detection sensors. The experimental results show that this method not only significantly reduces the systematic false alarm rate, but also effectively improves the detection ability of complex signals, providing a new technical path for weak signal detection in big data environment. © 2024 Copyright held by the owner/author(s).

关键词： Spatio-temporal data

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Performance Optimization of Silicon Nanowire MOSFETs for Next-Generation Low Power Applications

Performance Optimization of Silicon Nanowire MOSFETs for Nex...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Shirly Edward, A. Hanisha Abirami, B. Palani, Aditya Tamilarasi, R. Sharon, R. Linie Srm Institute of Science and Technology Dept. of Ece Vadapalani Campus Chennai India

ISBN: (纸本)9798350388602

This paper presents performance optimization of Silicon-Nanowire-Metal-Oxide-Semiconductor-Field-EffectTransistors (Si-NW-MOSFETs). As the semiconductor industry progresses toward smaller and more efficient device dimensions, the advantages of silicon nanowire MOSFETs have become increasingly prominent. Si NW MOSFETs are positioned as key candidates for future integrated circuit technologies, owing to their exceptional attributes, including high on-current and minimal leakage current. This study establishes crucial parameters at a gate voltage of 1.5 V and a drain voltage of 1 V: Sub-threshold Swing (SS=66 mV/decade), Leakage Current (loff=1 pA/μ m), Threshold Voltage Vth=0.346 V, On Current ((Ion=14 μ A/μm), On/Off Ratio (10n/Ioff=107), and Trans-conductance (gm=800 μ S/μ m). Results indicate marked enhancements in drive current and sub threshold slope when compared to traditional MOSFETs, underscoring the potential of nanowire configurations to address future scaling challenges. © 2024 IEEE.

关键词： Nanowires

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Research on Architecture and Simulation of Reconfigurable Integrated Processing Microsystem for Micro Space Robot

Research on Architecture and Simulation of Reconfigurable In...

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2024 international conference on Guidance, Navigation and Control

作者： Li, Wenjie Chen, Lei Zhang, Yanlong Zhu, Tianrui Ni, Feng Li, Nan Xu, Kun Ding, Xilun Beihang Univ Beijing 100086 Peoples R China China Acad Aerosp Elect Technol Beijing 100094 Peoples R China Beijing Microelectron Technol Inst Beijing 100076 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Nanjing Inst Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9789819622542;9789819622528;9789819622511

The explosive growth of micro-satellites has brought about increasingly saturated competition for low Earth orbit resources, and more complex potential needs of on orbit operations grow quickly. Aerospace robotic arms are still the most important optimal option to replace human beings to perform complex operation tasks for a long term. Faced with extreme requirements and environmental constraints different with on the ground, the miniaturization of the electronic system is the bottleneck that determines space robots' performance and energy efficiency ratio. This paper proposes an architecture of reconfigurable integrated processing microsystem for the application of micro space dual arm robotics. The core architecture design is studied from the task demand orientation, with processing, communication, sensing, execution, control and other functions in a highly integrated architecture, through system modeling, simulation and software hardware collaborative design. A corresponding assessment is provided for the subsequent implementation of hardware system miniaturization using the microsystem 3D integration process.

关键词： Computer Architecture Microsystem Space robotics

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Design, Kinematic and Variable Stiffness Analysis of a Tensegrity-Based Joint

Design, Kinematic and Variable Stiffness Analysis of a Tense...

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Wu, Jinlong Liu, Gang Li, Ke Zhang, Niansong Yu, Zhiyuan Wu, Haiteng Liu, Yixiang Li, Yibin School of Control Science and Engineering Shandong University Jinan250061 China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Mechatronics Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Beijing Institute of Precision Mechatronics and Controls Laboratory of Aerospace Servo Actuation and Transmission Beijing100076 China Hangzhou Shenhao Technology Co. Ltd Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou311121 China Jilin University Key Laboratory of Bionic Engineering Ministry of Education Jilin130022 China

ISBN: (纸本)9781665481090

In this paper, a new compact, low-inertia, and flexible tensegrity joint is proposed, which aims to overcome the shortcomings of traditional collaborative robots and tendon-driven robots. The joint is composed of two circular frames, and is connected by three surrounding pneumatic muscles and an intermediate spring. The stiffness of the joint is changed by inflating and releasing air to the pneumatic muscles, which is also the principle of the joint's variable stiffness. Firstly, a detailed kinematic analysis of the proposed tensegrity joint was carried out to explore its position change in multi-axis motion. Secondly, the stiffness analysis was carried out to evaluate the deformation response and stiffness characteristics of the joint under different loads, which provided an important reference for its design optimization. Finally, the performance of the model is verified by simulation analysis, and the reliability and practicability of the model in dynamic work are verified by comparison with actual data. This study provides a theoretical and experimental basis for the promotion of new tensegrity joints in robot applications, and provides new design ideas and methods for the development and innovation of future robotics technology. © 2024 IEEE.

关键词： Collaborative robots

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