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Helicopter Turboshaft Engines' Neural Network System for Monitoring sensor Failures

sensors 2025年第4期25卷 990-990页

作者： Vladov, Serhii Scislo, Lukasz Szczepanik-Scislo, Nina Sachenko, Anatoliy Perzynski, Tomasz Vasylenko, Viktor Vysotska, Victoria Kharkiv Natl Univ Internal Affairs 27 L Landau Ave UA-61080 Kharkiv Ukraine Cracow Univ Technol Fac Elect & Comp Engn 24 Warszawska St PL-31155 Krakow Poland Cracow Univ Technol Fac Environm Engn & Energy 24 Warszawska PL-31155 Krakow Poland European Org Nucl Res CERN 1 Esplanade Particules CH-1211 Geneva 23 Switzerland West Ukrainian Natl Univ Res Inst intelligent Comp Syst 11 Lvivska St UA-46009 Ternopol Ukraine Casimir Pulaski Radom Univ Fac Transport Elect Engn & Comp Sci PL-26600 Radom Poland Lviv Polytech Natl Univ Informat Syst & Networks Dept 12 Bandera St UA-79013 Lvov Ukraine Osnabruck Univ Inst Comp Sci 1 Friedrich Janssen St D-49076 Osnabruck Germany

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series analysis and identification, facilitating key information retention from previous states. Modules such as sensorFailClean and sensorFailNorm implement adaptive discretization and quantisation techniques, enhancing the data input quality and contributing to more accurate predictions. The developed system demonstrated anomaly detection accuracy at 99.327% after 200 training epochs, with a reduction in loss from 2.5 to 0.5%, indicating stability in anomaly processing. A training algorithm incorporating temporal regularization and a combined optimization method (SGD with RMSProp) accelerated neural network convergence, reducing the training time to 4 min and 13 s while achieving an accuracy of 0.993. Comparisons with alternative methods indicate superior performance for the proposed approach across key metrics, including accuracy at 0.993 compared to 0.981 and 0.982. Computational experiments confirmed the presence of the highly correlated sensor and demonstrated the method's effectiveness in fault detection, highlighting the system's capability to minimize omissions.

关键词： sensor failures neural network system helicopter turboshaft engines sensors recurrent layers approximation anomaly detection

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A Comprehensive Review on Wireless Healthcare Monitoring: System Components

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IEEE ACCESS 2024年 12卷 35008-35032页

作者： Kaidi, Hazilah Mad Izhar, Mohd Azri Mohd Dziyauddin, Rudzidatul Akmam Shaiful, Nur Ezzati Ahmad, Robiah Univ Teknol Malaysia Razak Fac Technol & Informat Kuala Lumpur 54100 Malaysia

Healthcare monitoring systems in hospitals and other health facilities have grown significantly, and portable healthcare monitoring systems using emerging technologies such as Internet of Things (IoT) technologies have aided the advancement of healthcare monitoring systems. Many studies have focused on intelligent healthcare systems in an IoT context to improve components, including wearable sensors and hardware devices, intelligent data collecting and processing, and network connections. Even while these applications are necessary and helpful for enhancing wireless healthcare settings related to monitoring, detection, and diagnostics, it might be challenging to fully understand how IoT characteristics are currently intertwined with its architecture. Accordingly, this work adds to the academic literature by thoroughly reviewing all significant areas of wireless healthcare monitoring system advancements. This research also examines a state-of-the-art healthcare monitoring system component under IoT. One hundred and ninety-four related articles were collected and filtered based on the system components defined. This study includes a thorough review and a list of genuine concerns with novel critical solutions. The study can facilitate academics and practitioners by giving them direction and vital information for future research.

关键词： sensors Medical services Biomedical monitoring Temperature sensors Temperature measurement Wireless sensor networks Internet of Things healthcare monitoring fall detection sensors technology

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Application of sensors in Flexible Process Control of intelligent Manufacturing 39

Application of Sensors in Flexible Process Control of Intell...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Qiu, Jianxiong He, Zhaoshui Yan, Cheng Zhou, Hailong Guangdong Univ Technol Guangzhou Peoples R China Guangdong Univ Technol Sch Automat Key Lab Informat Proc & Syst Integrat IoT Minist Educ Guangzhou Peoples R China Intelligent Mfg Engn Technol Res Ctr Huizhou Met Cutting Proc Unit Huizhou Peoples R China Tianjin Bonuo Intelligent Robot Technol Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In order to carry out the intelligent production of typical chemical products for the application of order-based flexible production system, based on intelligent measurement and control technology, a new generation of information technology such as industrial Internet, intelligent management and control, data visualization, and informatization is integrated. In this paper, we study the establishment of an order-based flexible production process with definable configuration according to the intelligent processing mode of process automation. After selecting the sensor reasonably, Configuration of the industrial intelligent detection system, System signal processing and digitalization, Industrial information network construction and debugging, Programming operation and debugging of intelligent measurement and control systems, Production tasks such as visualization and remote monitoring of the production process. The study found,The sensor plays a vital role in the control system control of the DCS -based controller, Liquid level sensors and flow/weight sensors and other key sensors can monitor various parameters in the production process in real time, provide accurate data support for the control system to ensure the accuracy and stability of the ingredient system. And realize the flexible ingredients controlled by program control, and then make the feeding at the same time to improve production efficiency. This research provides a strong technical guarantee for actual production, and provides a feasible implementation method for our intelligent manufacturing process industrial control.

关键词： sensor Process control Visualization DCS control

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Design and implementation of intelligent Water Cup system based on wireless sensor network 24

Design and implementation of Intelligent Water Cup system ba...

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5th International conference on Computer information and Big Data Applications, CIBDA 2024

作者： Wang, Nan Liu, Yan Yan, Zhiqiang College of Computer Science Wuhan Donghu University WuHan430299 China College of Computer and Artificial Intelligence Wuhan University of Engineering Science Wuhan430200 China

ISBN: (纸本)9798400718106

This project has designed an intelligent water cup system based on based on wireless sensor network. The cup cover is provided with a memory area of a memory chip and an intelligent control system is embedded on the cup body, through intelligent sensors (temperature sensor, GPS sensor, pressure sensor, etc.) to achieve measurement and remind users to drink water. Its specific functions include: Using GPS sensors to monitor the location of the cup to avoid losing track;using temperature sensors can determine the ambient temperature and water temperature;through Bluetooth, transmission of basic information in the cup, including the cup of electricity, water temperature, water allowance and other information. The intelligent Cup is also equipped with display, alarm, communication, storage, positioning and other modules, so as to achieve intelligent control, to bring more convenience to people’s lives. © 2024 ACM.

关键词： Wireless sensor networks

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Feature engineering based intelligent intrusion detection system for IoT network security

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-25页

作者： Sadhwani, Sapna Sriwastawa, Asmi Muthalagu, Raja Pawar, Pranav M. Birla Inst Technol & Sci Pilani Dept Comp Sci Dubai Campus Dubai 345055 U Arab Emirates

Internet-of-Things (IoT) helps create smart systems by allowing physical environments to be controlled or monitored by users through extensive sensor networks. Such networks are heavily resource-constrained due to the low processing power and decentralised placement of sensors. Having been implemented in several backbone sectors such as industry, healthcare, etc., they often deal with sensitive information. Thus, there is an imminent need to secure IoT networks using lightweight intrusion detection systems. A novel feature engineering-based intelligent network intrusion detection framework, dubbed FEIIDS (Feature Engineering based intelligent Intrusion Detection System) entirely based on the Machine Learning concept is proposed. Its working is explained in depth and its time complexity is analyzed. The model is trained and tested over the UNSW-NB15 dataset and found to be competitive with existing works, achieving an accuracy of up to 98.8% and 97.03% for binary and multiclass classification. The proposed work also attains 99% for precision, recall, and F1 score. On the other hand, state-of-the-art work that uses a bidirectional long short-term memory (BiLSTM) model for network intrusion detection is explored over different run-time hardware environments (CPU, GPU, and TPU). Early stopping is implemented to improve model performance and enable better analysis of the results. Discussions on the results reveal that FEIIDS is more stable, transparent, efficient, and lightweight compared to existing state-of-the-art models.

关键词： Feature engineering Feature subsetting Intrusion detection system IoT FEIIDS BiLSTM Linear regression

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Triboelectric Nanogenerator Assisted by Machine Learning

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ACS APPLIED ELECTRONIC MATERIALS 2023年第12期5卷 6549-6570页

作者： Yang, Dongfang Shang, Yuchao Li, Zhaoting Wang, Kai Shaanxi Univ Sci & Technol Haojing Coll Xian 712046 Shaanxi Peoples R China Qingdao Univ Weihai Innovat Res Inst Sch Elect Engn Qingdao 266000 Peoples R China Shandong Suoxiang Intelligent Technol Co Ltd Weifang 261101 Peoples R China Brown Univ Sch Engn Providence RI 02912 USA

In the era of the Internet of Things (IoT), the development of an intelligent sensor system with a sustainable power supply, convenient deployment, and versatile use has emerged as an urgent challenge. Triboelectric nanogenerators (TENGs) stand out as some of the most promising energy solutions in this context. They possess the capability of directly transforming mechanical stimuli into electrical signals, making them highly valuable for self-powered sensor applications. sensors based on a TENG exhibit the advantages of uncomplicated structures and remarkable instantaneous power density, thereby serving as a critical component in the construction of intelligent sensor systems. In parallel, machine learning (ML), with its attributes of cost-effectiveness, short development cycles, and robust data processing and predictive capabilities, has matured over more than five decades to meet the research requirements of various disciplines. ML demonstrates exceptional prowess in managing the substantial volume of electrical signals generated by a TENG. This paper presents a comprehensive overview of recent advancements in signal processing and intelligent identification within sensor systems based on a TENG. The technical characteristics and research status of this convergence are explored across five key domains: traffic safety, environmental monitoring, information security, motion tracking, and human-computer interaction. Finally, this paper delves into the current challenges and future development trends within this field, offering an analytical perspective on how to enhance its prospects and ultimately opening up a broader landscape for potential applications. We believe that the fusion of ML technology and TENG sensors will catalyze the rapid evolution of intelligent sensor networks in the foreseeable future, providing valuable insights for other research domains as well.

关键词： nanogenerators triboelectricnanogenerator machine learning Internet of Things motion monitoring human-machine interaction

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Data Fusion and processing of Smart sensors in Mechatronics Devices 3

Data Fusion and Processing of Smart Sensors in Mechatronics ...

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3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Yu, Qiang Song, Changjian Xia, Zhengyu Wang, Chongxin Wang, Chenglin Shandong Jianzhu University Shandong Jinan250000 China

ISBN: (纸本)9798331519032

With the rapid development of mechatronics equipment, smart sensors play an increasingly important role in various applications. The diversity and complexity of smart sensors make data processing and fusion a key link in improving system performance. This paper studies the application and performance of embedded smart sensor networks in smart electronic systems, analyzes the fundamentals, working principles and communication protocols of embedded smart sensor networks, reveals its key technologies in smart electronic systems, focuses on its application scenarios and advantages in smart electronic systems, and demonstrates its role in improving system intelligence, optimizing performance, and reducing energy consumption through case studies. Including small molecule semiconductors, polymer semiconductors and conductive polymers and other organic sensing materials, as well as chemical sensors, temperature sensors, optical sensors and mechanical sensors and other organic intelligent sensor devices of cutting-edge applications, the results show that the effective application of data fusion and processing methods can significantly improve the level of intelligent mechatronics equipment and operational efficiency, for the further development of the relevant fields to provide a theoretical basis and practical guidance. Theoretical foundation and practical guidance for the further development of related fields. © 2024 IEEE.

关键词： Smart sensors

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Reinforcement Learning-Based Sensing Decision for Data Freshness in Blockchain-Empowered Wireless networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第12期13卷 3276-3280页

作者： Kim, Dongsun Yun, Sinwoong Lee, Sungho Lee, Jemin Quek, Tony Q. S. Daegu Gyeongbuk Inst Sci & Technol Dept Elect Engn & Comp Sci Daegu 42988 South Korea Gumi Elect & Informat Technol Res Inst Future Mobile Commun Res Ctr ICT Convergence Res Div Gumi 39253 South Korea Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

Recently, blockchain (BC)-empowered wireless sensor networks (WSN) emerged as a promising solution for secure and reliable data management. However, the integration of BC and WSN brings several challenges including long processing delay at BC, which reduces freshness of sensed data. Motivated by this, we first model the BC-empowered WSN and define the age of information (AoI), the elapsed time from the sensor's data collection until its commitment to the BC. We then formulate the AoI violation probability minimization problem and propose the reinforcement learning-based sensing decision (RL-SD) algorithm. Using the RL-SD, the sensor intelligently makes sensing decisions, considering wireless channel conditions, BC process latency, and energy status. We further introduce the pause mechanism to save energy, where the sensor pauses sensing and transmission for a while after the successful transmission. Our experiments demonstrate that the proposed algorithm outperforms the probabilistic sensing decision algorithm that senses randomly with the optimal probability. We also verify the performance of the RL-SD for various environments with different block sizes, pause times, and AoI thresholds.

关键词： sensors Wireless sensor networks Data integrity Throughput Signal to noise ratio Communication system security Training Permissioned blockchain reinforcement learning wireless sensor networks age of information

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Liquid-Crystal-Driven Reconfigurable intelligent Surface With Cognitive sensors for Self-Sustainable Operation

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第12期71卷 9415-9423页

作者： Youn, Youngno An, Donggeun Kim, Daehyeon Hwang, Myeonggin Choi, Hyengcheul Kang, Byounggwan Hong, Wonbin Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea Corning Technol Ctr Korea Asan 31452 South Korea

For the first time, this article describes the concept of a sustainable reconfigurable intelligent surface (RIS) for random wireless propagation channel management at millimeter-wave (mmWave) bands. Reconfigurable beam scanning capabilities are obtained by modifying the permittivity of liquid-crystal (LC) molecules. In order to configure a self-sustainable feedback loop for the proposed RIS operation, a lens-based beamforming circuit is embedded in the LC-driven reconfigurable beamforming surface. This sensing circuit enables the acquisition of channel estimation information without additional hardware implementation and signal processing costs. The proposed RIS is verified by link-level field trials for near-field indoor and far-field outdoor environments, satisfying the 5G new radio specifications for proof-of-concept purposes. This proposed RIS is expected to be applied in energy-efficient wireless channel management for mmWave radios.

关键词： Array signal processing sensors Reflection Metals Millimeter wave communication Lenses Wireless sensor networks Beamforming millimeter-wave (mmWave) reconfigurable intelligent surface (RIS) Rotman lens

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Research on Real-Time Monitoring and Early Warning System of Lightning Protection and Grounding System Based on Computer intelligent sensor 2

Research on Real-Time Monitoring and Early Warning System of...

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2nd IEEE International conference on Image processing and Computer Applications, ICIPCA 2024

作者： Liu, Jian College of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798350360240

In the study of real-time monitoring and early warning of the application of intelligent sensors to lightning protection and grounding system, an innovative work is to combine Graph Neural networks (GNN) and Multi-Objective Evolutionary Algorithm based on Decomposition, MOEA/D) combined to build a new hybrid model called Graph Evolutionary networks (GEN) to address the challenges in monitoring complex systems. With its efficient processing of network structure data, GNN can effectively capture and analyze the topological relationship and real-time data flow between lightning protection and grounding system sensors, and achieve accurate evaluation and prediction of system status. In view of the fact that the real-time monitoring and early warning of lightning protection and grounding system involves a number of mutually dependent objectives, such as response speed, monitoring accuracy and energy consumption, the researchers introduced the MOEA/D algorithm to seek the balance point between the objectives by decomposing the multi-objective problem into multiple single-objective sub-problems for synchronous optimization, so as to ensure the overall optimal operation of the system. The GEN model successfully combines the advantages of GNN and MOEA/D: GNN is used to analyze the complexity of sensor networks and obtain critical information in real time;MOEA/D is used to coordinate and optimize multiple performance indicators in system monitoring and early warning. Therefore, the GEN model can not only realize real-time and accurate system condition monitoring, but also make reasonable tradeoff decisions between different performance objectives, which significantly improves the safety and reliability of lightning protection and grounding systems. This interdisciplinary innovation opens up a new technical path for the application of intelligent sensors in the field of complex system monitoring and early warning, which has wide practical value and far-reaching industry develo

关键词： Smart sensors

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