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

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Benchmarking of Machine Learning Algorithms for Temperature sensor Fault Time Series Classification 15

Benchmarking of Machine Learning Algorithms for Temperature ...

引用

15th International conference on Information, Intelligence, systems and applications, IISA 2024

作者： Prantikos, Konstantinos Pantopoulou, Maria Pantopoulou, Stella Hefeitz, Alexander Tsoukalas, Lefteri H. School of Nuclear Engineering Purdue University West LafayetteIN United States Nuclear Science and Engineering Division Argonne National Laboratory ArgonneIL United States

ISBN: (纸本)9798350368833

Reliable sensors functioning is important for safe and efficient operation of a nuclear system. Accurate detection and classification of sensor anomalies are crucial for efficient maintenance and prevention of reactor shutdowns. In this proof-of-principle study, we benchmark performance of several machine learning (ML) algorithms in temperature sensor fault detection. The database consists of four time series, with one representing normal sensor operation and the remaining three consisting of various sensor faults. The ML models in this study include Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Decision Tree (DT), and Extreme Gradient Boosting (XGBoost). Performance of the ML models is evaluated using Precision, Recall and F1-score metrics. The results of the benchmarking study show that XGBoost achieves the best performance. © 2024 IEEE.

关键词： Reactor shutdowns

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CONFORMAL DESIGN AND FABRICATION OF WEARABLE PIEZOELECTRIC sensor ARRAY FOR SPATIOTEMPORAL DISTRIBUTION OF ARTERIAL PULSE WAVES 37

CONFORMAL DESIGN AND FABRICATION OF WEARABLE PIEZOELECTRIC S...

引用

37th IEEE International conference on Micro Electro Mechanical systems (MEMS)

作者： Zhen, Liyun Zhai, Yueqi Zhu, Xiantao Xu, Mengfei Li, Yizhi Liu, Jingquan Yang, Bin Shanghai Jiao Tong Univ Natl Key Lab Adv Micro & Nano Mfg Technol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Micro Nano Elect Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9798350357929

This paper reports a wearable piezoelectric pressure MEMS sensor array for multi-site conformal sensing of pulse waves. Thinned bulk lead zirconate titanate (PZT) piezoceramic films with high performance are embedded into soft materials, allowing optimal deformability and conformality. Specifically, a spin-on polymeric adhesive is adopted to transfer uniform PZT film during bonding and releasing processes. As a result, the device of single element exhibits high sensitivity with 47.45 mV/N. Furthermore, it can capture a weak pulse with the spatial and temporal distribution without cross talk among channels, which has great potential applications in the diagnosis of cardiovascular disease.

关键词： Conformal Design Piezoelectric sensor Array Pulse Perception Spatiotemporal Distribution

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Comparison of Actual Performance between CDMA and DDMA Waveforms in Vehicle Radar Application 16

Comparison of Actual Performance between CDMA and DDMA Wavef...

引用

16th International conference on Signal Processing systems, ICSPS 2024

作者： Wang, Wanbin Zou, Lin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

The Millimeter-wave radar has become an important sensor for advanced driver assistance systems due to its advantages such as environmental adaptability, low cost and detection accuracy. Most of current vehicle radars use Multiple Input Multiple Output (MIMO) technology to obtain better angular resolution. Among all MIMO radar waveforms, both CDMA and DDMA meet the requirements of next-generation high-performance radars and may become the mainstream radar waveforms in the future. Performance comparisons of the two waveforms are scattered in a large number of literatures and they are all from a theoretical point of view. At present, there is no literature comparing the practical performance of CDMA and DDMA. AWR6843 is a high-precision millimeter-wave radar sensor widely used in automotive safety, environmental monitoring and other applications. This SOC chip has been adopted in numerous automotive radar products. Thus, based on the data collected by a TI's AWR6843 radar evaluation board, this paper compares the performance of CDMA and DDMA in terms of waveform orthogonality, parameter estimation capability, and processing complexity. In addition, the paper discusses the merits and demerits of each technology and the limitations of its practical application, which provides an important reference for the optimization and technical route selection of future radar systems. © 2025 SPIE.

关键词： advanced driver assistance systems

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A Concise Review on Deep Learning Methods in Agricultural applications 2

A Concise Review on Deep Learning Methods in Agricultural Ap...

引用

2nd International conference of Intelligent Methods, systems and applications, IMSA 2024

作者： Maluazi, Nur Alia Sofia Zulkifley, Mohd Asyraf Kadim, Zulaikha Universiti Kebangsaan Malaysia Department of Electrical Electronic and Systems Engineering Bangi43600 Malaysia MIMOS Berhad Advanced Informatics Lab Lumpur Malaysia

ISBN: (纸本)9798350362633

Agriculture is one of the industries that plays a vital role in the development of a country. Industry 4.0 has led to the development of intelligent machines equipped with various sensors for automated monitoring. While sensor-based data collection is a growing field in agriculture, this review primarily focuses on the application of deep learning models like VGG16 and YOLO for image analysis tasks in agricultural sectors. Nevertheless, these large sensor networks produce a large number of data, whereby they need to be processed intelligently. A deep learning approach is currently the state-of-the-art technology that is able to infuse a large number of inputs to deliver high-performance intelligent applications. Deep learning models come in a variety of forms, including deep neural networks, recurrent neural networks, neural convolutional networks, and many more. This paper will focus on a review of the deep learning approach in agricultural applications through three specific key tasks: classification, detection, and segmentation. This review highlights the usage of VGG16 and AlexNet as a commonly employed pre-trained model for the classification task, the YOLO model for the detection task, and an integrated system of deep learning networks for the best segmentation task across several agricultural applications. One of the popular methods used to improve the base model performance is by integrating advanced module as well as pruning the whole model. In conclusion, this paper has contributed to a comprehensive understanding of the reviewed deep neural networks for agricultural applications. For future works, more works need to be done to continuously evolve the deep learning algorithms to help in various domains of agricultural applications. © 2024 IEEE.

关键词： Smart agriculture

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Improving Human Activity Recognition Through Multisensor Data Fusion Techniques in Wireless sensor Networks 3

Improving Human Activity Recognition Through Multisensor Dat...

引用

3rd International conference on Computing and Machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Wireless sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Wireless sensor network(WSN) human activity recognition (HAR) multi-sensor datasets data fusion machine learning classification Covariance Intersection (CI) Kalman Filter (KF)

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Spectro-Temporal Recurrent Neural Network for Robotic Slip Detection with Piezoelectric Tactile sensor

Spectro-Temporal Recurrent Neural Network for Robotic Slip D...

引用

IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Ayral, Theo Aloui, Saifeddine Grossard, Mathieu CEA Leti F-38000 Grenoble France Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9781665476331

In this paper, we present a novel tactile-based method for detecting slippage in robotic manipulation, using a single piezoelectric sensor. The method combines spectral analysis (FFT) and deep learning (GRU) for improved efficiency and adaptability. We implement an automated data-collection process with accurate and unbiased labels of slip events. The proposed method is evaluated through an ablation study characterizing the influence of model hyperparameters and interaction settings. The results show a high classification accuracy of 98.70% at 100Hz and detection delays of 8.5 +/- 23.7ms, demonstrating the relevance of our spectro-temporal pipeline. The proposed method has the potential to enhance the performance of robotic systems and increase their reliability in robotic grasping applications.

关键词： Piezoelectricity

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A TACTILE sensor ARRAY WITH A MONOLITHICALLY INTEGRATED NEURAL NETWORK FOR EDGE COMPUTATION 36

A TACTILE SENSOR ARRAY WITH A MONOLITHICALLY INTEGRATED NEUR...

引用

36th IEEE International conference on Micro Electro Mechanical systems (MEMS)

作者： Lei, Tengteng Hu, Yushen Wong, Man Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665493086

Described presently is a monolithically integrated "intelligent" sensor system consisting of a piezoelectric tactile sensor array and an artificial neural network (ANN). Low-temperature processed, dual-gate thin-film transistors (TFTs) based on semiconducting indium-tin-zinc oxide are deployed in the former for signal acquisition and in the latter for executing the "multiply-accumulate" operation. With the ANN appropriately trained, the system is capable of both "imaging" and "recognizing" a distributed force load applied on the sensor array using 3D-printed stamps. The proposed integration scheme can be deployed to construct intelligent sensor systems for applications other than tactile sensing.

关键词： Tactile sensor array artificial neural network edge computation dual-gate metal-oxide thin-film transistors

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Putting the S back into IoT: Tutorial on sensor and Actor systems in Small Portable Ad-Hoc Networks 18

Putting the S back into IoT: Tutorial on Sensor and Actor Sy...

引用

18th ACM International conference on Distributed and Event-Based systems, DEBS 2024

作者： Norbisrath, Ulrich Machado, Renato Perotto Slavin, Anton Zorec, Matevz Borjan University of Tartu Tartu Estonia

ISBN: (纸本)9798400704437

This tutorial introduces participants to the fundamentals and advanced concepts of integrating sensor and actor systems within the Internet of Things (IoT) through hands-on experience with small, portable ad-hoc networks. We will learn to create and manage our own IoT networks by utilizing several ESP32 and ESP8266-based devices connected to various actors and sensors alongside easily configurable IoT edge network set-ups. We will orchestrate the systems using Node-RED for data flow and event-based integration. Simple dashboard and database connectivity options in Node-RED will help us build IoT applications and workflows without the complexity of traditional programming environments. © 2024 ACM.

关键词： Internet of things

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System Architecture for a Wireless sensor Network for Sustainable Agriculture, Forestry and Green Spaces in Smart Cities 12

System Architecture for a Wireless Sensor Network for Sustai...

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

作者： Rosewick, Sebastian Schorlemer, Felix Kunemund, Frank Fachhochschule Dortmund Department of Computer Science Germany

ISBN: (纸本)9798350358056

This paper presents a wireless sensor network employing heterogeneous mobile robot platforms designed for data-acquisition and research in agriculture, forestry and green spaces in smart cities. Where conventional sensor networks struggle with a lack in flexibility, little variation in their reading points and no possibilities in mobile manipulation, this approach offers new capabilities in accomplishing complex tasks in outdoor and indoor scenarios. A wheel driven robot platform in combination with multiple UAVs11Unmanned Aerial Vehicle forms the basis for the mobile sensor network which can make use of a wide variety of sensors and actuators. The system benefits from new developments in controlling multi-agent systems as well as machine-learning-algorithms for local and independent data-analysis. © 2023 IEEE.

关键词： forestry machine learning mesh-networks mobile robots multi-agent systems neural networks precision agriculture UAVs wireless sensor network

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Existing Devices in Wrist Rehabilitation Robotics: Mechanical Design, Control systems, and User Requirements for Commercialization 20

Existing Devices in Wrist Rehabilitation Robotics: Mechanica...

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20th IEEE/ASME International conference on Mechatronic and Embedded systems and applications (MESA)

作者： Pippo, Irene Massone, Valentina Guazzotti, Marco Gnetti, Benedetta Albanese, Giulia A. Casadio, Maura Berselli, Giovanni Zenzeri, Jacopo ReWing Srl Milan Italy Univ Genoa DIME Genoa Italy Univ Genoa DIBRIS Genoa Italy Fdn Don C Gnocchi La Spezia La Spezia Italy

ISBN: (纸本)9798331516246;9798331516239

Recently, rehabilitation robotics has demonstrated the ability to improve the methodologies significantly. Robotic devices can provide accuracy, repeatability, high-dose and task-specific training, and real-time feedback. These features are essential in rehabilitation, focusing on the upper limb and the wrist, as a joint needed to perform most activities of daily living (ADLs) and thus crucial to maintaining a high quality of life. This is particularly important considering the demand for effective rehabilitation solutions due to frequent motor impairments related to neurological or orthopedic issues. This review focuses on robotic devices for wrist rehabilitation, exploring their characteristics with an eye toward significant and appealing features for commercialization. We explored relevant features of these devices such as a robust and human-centered mechanical design, an appropriate actuation system, and an advanced sensor network integrated with a sophisticated control architecture to monitor and optimize human-machine interactions. We considered usability factors as ergonomics, ease of use, and adaptability to the needs of different users, for clinical applications and broad market adoption. This work reviews existing robotic devices for wrist rehabilitation, comparing their key features to evaluate the current state of the field. It aims to identify which devices best meet the necessary criteria and highlight issues that still require further engineering research.

关键词： robotic device upper limb rehabilitation wrist mechatronic design

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