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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications XI"

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Machine Learning based Wild-Animal Detection near Roads using IoT sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

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2024 IEEE International conference on Intelligent systems and advanced applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

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Design a Touch sensor Using Conductive Spandex Fabric 7th

Design a Touch Sensor Using Conductive Spandex Fabric

引用

7th International conference on Green Technology and Sustainable Development

作者： Minh Ky Tran Thi Tuyet Nga Ly Bao Hy Nguyen Chi Cuong Vu HCMC Univ Technol & Educ Dept Facil Elect & Elect Engn 01 Vo Ngan St Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Recent advancements in sensor technology have propelled the e-textile industry to the forefront of innovation. E-textiles, also known as smart fabrics, possess physical and dimensional flexibility, making them suitable for wearable sensor technology. These technologies are developed to cater to the needs of advanced daily life, which aligns with the IoT development trend. This paper introduces an applied resistive sensor that uses an electronic fabric made from elastic fabric and carbon nanotubes (CNTs) fabric as the electrical layer. The sensor achieves high sensitivity (2.6241 N-1 with force less than 1 N), thickness (similar to 3 mm), and versatility make it ideal for load performance and fast resistance switching without waiting for capacitor charging and discharging as capacitive sensors, facilitating faster interaction with e-touch. The study provides an overview of the sensor's characteristics and future directions, emphasizing the creation of soft sensors based on pyralux for medical devices and health monitoring applications in the future.

关键词： Touch sensor Carbon nanotubes (CNTs) Conductive spandex fabric Force-sensing resistor (FSR)

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Evaluating the Impact of Lossy Compression on ADAS Deep Learning Models using Fisheye Cameras 26

Evaluating the Impact of Lossy Compression on ADAS Deep Lear...

引用

26th Irish Machine Vision and Image Processing conference, IMVIP 2024

作者： Simha, Srinidhi Mukanahallipatna Molloy, Dara Fahy, Darren Valeo Vision Systems Ireland University of Galway Ireland

ISBN: (纸本)9781837242672

The increasing deployment of advanced Driver Assistance systems (ADAS) alongside the continual rise in camera sensor resolution has led to high bandwidth, and generally high cost, computation, and intra-vehicle communication. While the sensor bandwidth impacts the vehicle architecture, it also affects the data collection, storage, deep learning model training, and validation infrastructures. However, if the bandwidth was low, while still achieving the goal of high accuracy ADAS perception, the time and cost associated with creating and deploying the system would be greatly reduced. This study investigates the influence of lossy compression on multi-task deep learning models for real-time perception in ADAS employing fisheye cameras. We leverage a large-scale dataset and train a representative multi-task ADAS perception model for pedestrian, kerb, line, and soiling classification. The testing dataset is subjected to compression using the popular H.264 video codec at varying compression ratios. Through rigorous evaluation, we analyse the effects of compression on model performance, providing insights into the feasibility of employing lossy compression techniques in ADAS applications. Our results reveal that lossy compression could be deployed in automotive perception applications and that a compression ratio of up to 98% (720Mb/s to 12Mb/s), could be utilised with negligible performance degradation. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： advanced driver assistance systems

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Ph.D. Forum: Responsive On-Device DNN Adaptation for Non-Stationary Mobile Environments 24

Ph.D. Forum: Responsive On-Device DNN Adaptation for Non-Sta...

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22nd ACM conference on Embedded Networked sensor systems, SenSys 2024

作者： Fang, Cheng Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798400706974

On-device adaptation to unpredictable domain shifts is crucial for mobile applications like autonomous driving and augmented reality. Test-time adaptation (TTA) offers a solution by tuning model parameters with unlabeled live data before prediction, but its forward-backward-reforward pipeline increases latency, compromising responsiveness. This paper presents AdaShadow, a responsive TTA framework for non-stationary mobile data distribution and resource dynamics via selective updates of critical layers. It utilizes a backpropagation-free assessor for quick identification of critical layers, a unit-based runtime predictor for latency estimation, and an online scheduler for effective updates. Evaluations show AdaShadow reduces adaptation latency by up to 3.5× and improves accuracy by up to 25.4%, with low memory and energy costs. © 2024 Copyright is held by the owner/author(s).

关键词： latency-efficient test-time adaptation

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Design of a Novel Vacuum Head with Haptic Perception for Negative Pressure Therapy on Limbs 17th

Design of a Novel Vacuum Head with Haptic Perception for Neg...

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17th International conference on Intelligent Robotics and applications

作者： Li, Zihao Nie, Zhenguo Zhao, Huichan Shao, Qi xie, Fugui Liu, xin-Jun Tsinghua Univ Dept Mech Engn DME State Key Lab Tribol Adv Equipment Beijing 100084 Peoples R China Tsinghua Univ Beijing Key Lab Precis UltraPrecis Mfg Equipment & Control Beijing 100084 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

Negative pressure (NP) therapy can effectively manage irregular extremity lymphedema but requires significant physical therapist effort. Considering the repeated work during the therapy, using the NP therapeutic robotic systems is recommended to help the therapist decrease the workload. The therapist could make the rapid suction to deform the head lip with the dexterous hands, while the robot fails to feel the interaction during the therapy due to the lost sensors. Therefore, this paper proposes a novel suction head with a variable-sized soft lip for haptic perception. The design of the soft lip is optimised from the previous work, and the detection sensor with strain gauges (SGs) is designed to be located under the lip for haptic perception. Then, the SG voltage was calibrated to the force with the standard weights, and the lip pressure was compensated to the SG voltage in the later experiment. The haptic experiment was conducted when the head contacted the phantom surface by using the manipulator. The results illustrated that the head could detect the contact pose through the distributed forces from SGs during the robot rotation.

关键词： Negative pressure therapy Suction cup Soft robot Haptic perception

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Quantitative Analysis and Optimization of sensor Deployment for Enhanced Activity Recognition in Smart Home Environments: A Multidimensional Approach to Data-Driven Intelligence and Efficiency in Universal Computing Spaces

Quantitative Analysis and Optimization of Sensor Deployment ...

引用

2024 International conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Aziz, Khalid Dua, Sakshi Gupta, Prabal Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350363289

This paper presents a comprehensive study on the enhancement of smart home automation and monitoring systems through the application of advanced activity recognition algorithms and sensor deployment optimization. We start by taking a CASAS dataset capturing a wide range of daily living activities, detected through an array of environmental sensors. This study conducts an in-depth analysis of activity recognition performance, leveraging precision, recall, and f1score metrics to identify areas for improvement in existing machine learning models. Further, we investigate sensor activation data to propose optimizations in sensor deployment, aimed at increasing the accuracy of activity recognition while reducing unnecessary sensor data processing. Through temporal analysis of activity patterns, we develop predictive models that forecast future activities with significant accuracy, facilitating smarter resource management and personalized automation strategies. This paper also explores the implications of our findings on user experience enhancement. Our methodology showcases the integration of machine learning techniques, statistical analysis, and practical considerations for deploying smart home technologies. The results indicate a promising avenue for future research and development in smart environments, with potential applications extending beyond residential settings to include healthcare facilities and office spaces. The findings underscore the importance of adaptive and intelligent systems in enhancing the quality of life through technology. © 2024 IEEE.

关键词： Smart homes

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DYNAMIC DUAL-VECTOR FEEDBACK PREDICTIVE CONTROL FOR PMSM-BASED EMA

DYNAMIC DUAL-VECTOR FEEDBACK PREDICTIVE CONTROL FOR PMSM-BAS...

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2024 CSAA/IET International conference on Aircraft Utility systems, AUS 2024

作者： Wu, Han Li, Bingqiang Wang, Heyu Wang, Dongheng Riaz, Saleem Zhou, xiong School of Automation Northwestern Polytechnical University Xi'an China Avic Chengdu Aircraft Design and Research Institute Chengdu China

This paper presents a novel strategy for optimizing space vectors in model predictive control (MPC) of Electro-Mechanical Actuators (EMAs), specifically designed to enhance torque stability under dynamic conditions. EMAs are critical in aerospace applications due to their high reliability and power density, which facilitate rapid and precise actuation required in such advanced systems. Conventional single-vector model predictive current control often suffers from significant torque fluctuations, particularly under rapid operational changes. To overcome these challenges, this study proposes the integration of dual effective vectors within a single control cycle. This approach not only mitigates torque variability but also preserves the dynamic responsiveness of the actuators, crucial for maintaining performance under varying aerospace conditions. The proposed enhanced dual-vector model predictive control (DVMPC) extends the operational capabilities of EMAs by ensuring smoother torque output and enhanced system stability. Validation through detailed simulation confirms the proposed scheme significantly outperforms traditional methods, offering a robust solution for high-performance aerospace applications requiring exceptional control accuracy and reliability. © The Institution of Engineering & Technology 2024.

关键词： Predictive control systems

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The Optimal Design of a Rotor System with Elastic Ring 10

The Optimal Design of a Rotor System with Elastic Ring

引用

10th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, DependSys 2024

作者： Shi, Yihang Yang, Zhongyu Feng, Yinli Liu, Di University of Chinese Academy of Sciences Chinese Academy of Sciences School of Aeronautics and Astronautics Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences School of Engineering Sciences Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China Chinese Academy of Sciences Institute of Engineering Thermophysics National Key Laboratory of Science and Technology on Advanced Light-duty Gasturbine Beijing China

ISBN: (纸本)9798331540500

Elastic ring (ER) is widely adopted for its compact size and ease of assembly and have become a prevalent type of elastic support within rotor-support systems. However, it may exhibit stiffness nonlinearity under specific operating conditions, potentially compromising the safety of the rotor system. This paper examines the influence of elastic ring stiffness on the critical speed of a specific aero-engine rotor support structure, focusing on related nonlinear phenomena. The Levenberg-Marquardt method was employed to optimize key parameters, including the stiffness and clearance of the elastic ring, with the second-order critical speed as the optimization goal. Finite element analysis was performed to validate the optimization results, which were found to be both effective and practically applicable. The finding provides a significant reference for the design and analysis of aero-engine rotor systems and similar applications. © 2024 IEEE.

关键词： Optimal systems

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Investigation of Hardware-Software Solutions for an Energy-Efficient Wireless sensor Network 12

Investigation of Hardware-Software Solutions for an Energy-E...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Yatsenko, Anastasiia Parkhomenko, Anzhelika Wolff, Carsten Tulenkov, Artem Parkhomenko, Andriy National University Zaporizhzhia Polytechnic Zaporizhzhia Ukraine Institute for the Digital Transformation of Application and Living Domains University of Applied Sciences and Arts Dortmund Germany

ISBN: (纸本)9798350358056

This work is devoted to the study of methods and tools for minimizing power consumption of wireless sensor networks. The results of processing experimental data for various operating modes of network nodes, as well as options for organizing their power supply and software settings are given. Practical recommendations have been developed to improve the energy efficiency of wireless sensors networks for the Smart Home systems. © 2023 IEEE.

关键词： communication module energy efficiency Smart Home system solar panel wireless sensors network

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A Predictive Maintenance Platform for a Conveyor Motor sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural Computing for advanced applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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