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Application of Wireless sensor Technology in Urban Energy Management 24

Application of Wireless Sensor Technology in Urban Energy Ma...

引用

8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Yajuan Chen, Jie Gong, Zhijia College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The rapid growth of urbanization has led to an increasing need for efficient energy management systems to optimize energy consumption and reduce environmental impacts. Wireless sensor technology emerges as a pivotal solution, providing advanced capabilities in real-time monitoring, control, and management of urban energy systems. This paper explores the practical applications of wireless sensor networks (WSN) in urban energy management, highlighting their role in smart grids, intelligent building systems, smart street lighting, public transportation, water management, and renewable energy integration. Additionally, it addresses the prominent challenges such as data security, sensor durability, network scalability, data management, energy consumption, and infrastructure integration. [1-2] The paper further discusses optimization pathways to enhance the effectiveness and sustainability of WSNs in urban energy management. By overcoming these challenges and leveraging the potential of wireless sensor technology, cities can achieve significant energy savings, operational efficiencies, and contribute to broader environmental sustainability goals. © 2024 Copyright held by the owner/author(s).

关键词： Green buildings

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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...

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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...

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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...

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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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Arduino-Powered Sonar Radar MOKSH: Design, Implementation, and applications 1

Arduino-Powered Sonar Radar MOKSH: Design, Implementation, a...

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1st International conference on advanced Computing and Emerging Technologies, ACET 2024

作者： Seejal, Seejal Kumar, Harish Taluja, Anuradha Gandotra, Ekta Gupta, P.K. Computer Science and Engineering Jaypee University of Information Technology Himachal India Computer Science and Engineering Sdgi University Ghaziabad India Computer Science and Engineering Aktu Lucknow Ghaziabad India Computer Science and Engineering Jaypee University of Information Technology India Computer Science and Engineering Mohan Babu University Tirupati India

ISBN: (纸本)9798350367720

The Arduino-controlled radar systems constituting the fundamental elements of this RADAR system, an ultrasonic sensor and servo motor are employed. The fundamental operation of the system is to detect objects within the range specified. Attached to the servo motor is an ultrasonic sensor that rotates approximately 180 degrees and provides a graphical representation on the processing IDE software. In addition to providing a graphical representation, the Processing IDE also provides the object's angle, position, and distance. The administration of this system is facilitated by Arduino. The Arduino UNO board is adequate for both interface development and ultrasonic sensor and display device control. We learned about extant navigation and obstacle detection innovations as well as various systems that make efficient use of ultrasonic sensors through our research. This RADAR system is primarily utilized in the fields of object identification, mapping, surveillance, and monitoring, in addition to navigation and positioning. Additionally, these low-cost systems are appropriate for implementation indoors. © 2024 IEEE.

关键词： Ultrasonic sensors

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Feature Identification and Extraction for Battery Aging Estimation in Aircraft Auxiliary applications

Feature Identification and Extraction for Battery Aging Esti...

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2024 IEEE International conference on Electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electrification conference, ESARS-ITEC 2024

作者： Shekhar, Shivanshu Gauchia, Lucia Amaris, Hortensia University Carlos Iii of Madrid Dept. Electrical Engineering Madrid Spain

ISBN: (纸本)9798350373905

Aircraft applications are increasingly including energy storage, especially batteries, as part of their main and auxiliary power systems. Considering the auxiliary power one, this paper studies the impact of temperature on the identification and extraction of battery features, and their adequacy to be used as indicator to estimate the battery State-of-Health (SOH). For that purpose, four different features were extracted for 6 cells cycled under aircraft auxiliary loads at 2°C and 55°C and were fed into Support Vector Regression (SVR) algorithms, and optimized versions, which are particularly useful for nonlinear applications. Results show the temperature greatly affects the data, requiring more advanced SVR algorithms for weaker features. © 2024 IEEE.

关键词： Battery storage

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