Measurement Sensors最新文献_第4页

Research on parameter identification and fault prediction method of hydraulic system in intelligent sensing agriculture

Measurement Sensors Pub Date : 2025-01-25 DOI: 10.1016/j.measen.2025.101813

Wenbo Liu, Jiaheng Zheng, Guangdong Shi, Qingshu Yuan, Yongping Lu

{"title":"Research on parameter identification and fault prediction method of hydraulic system in intelligent sensing agriculture","authors":"Wenbo Liu, Jiaheng Zheng, Guangdong Shi, Qingshu Yuan, Yongping Lu","doi":"10.1016/j.measen.2025.101813","DOIUrl":"10.1016/j.measen.2025.101813","url":null,"abstract":"<div><div>This study aims to explore the application of deep learning techniques, particularly optimized long short-term memory networks (LSTM), in the diagnosis of hydraulic system faults and parameter recognition in intelligent sensing agriculture. Firstly, the hydraulic system was modeled and the key parameters and state variables in the model were identified. Next, the LSTM network is introduced to optimize the model through its unique internal structure. LSTM can effectively capture long-term dependencies in time series data, making it an ideal choice for handling hydraulic systems involving dynamic behavior. To evaluate the performance of the model, 2000 data points were collected and preprocessed, of which 1897 data points were used for experiments. Based on these data, model performance was tested under different operating conditions. The research results show that the optimized LSTM model performs well in parameter recognition and fault diagnosis, especially under standard operating conditions, with a relative error rate of only 1.5 %. Considering different operating conditions and fault modes, the proposed model demonstrates good robustness and practicality in hydraulic system fault diagnosis, especially with an accuracy of over 90 % in leakage fault diagnosis, and remains stable under various operating conditions. This study provides strong support for the application of deep learning technology in hydraulic system fault diagnosis, and valuable insights for the performance optimization and application expansion of future models.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101813"},"PeriodicalIF":0.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on the application of intelligent sensors based on the Internet of Things in fault diagnosis of mechanical and electrical equipment

Measurement Sensors Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101811

Lingli Yao

{"title":"Research on the application of intelligent sensors based on the Internet of Things in fault diagnosis of mechanical and electrical equipment","authors":"Lingli Yao","doi":"10.1016/j.measen.2025.101811","DOIUrl":"10.1016/j.measen.2025.101811","url":null,"abstract":"<div><div>The purpose of this work to do is to solve the fault diagnosis of agricultural mechanical and electrical equipment and guarantee the smooth operation of production line and industrial process. The research begins by collecting operational data from electromechanical equipment based on Internet of Things (IoT) technology and utilizes Narrowband Internet of Things (NB-IoT) modules to achieve communication for terminal electromechanical devices. Subsequently, the Kernel Extreme Learning Machine (KELM) is introduced and combined with the Whale Optimization algorithm to construct a fault diagnosis model based on the Whale Optimization Kernel Extreme Learning Machine (WOKELM). Finally, the performance of the model is experimentally evaluated. The results indicate that, compared to other baseline algorithms, the proposed model algorithm achieves Accuracy values exceeding 90 %, with at least a 3.85 % improvement over the KELM baseline algorithm. Additionally, in the training a.nd testing sets, the F1 values of the proposed model algorithm reach 91.24 % and 85.85 %, respectively, which is at least 2.98 % higher than other model algorithms. Furthermore, an analysis of fault diagnosis error rates reveals that the Root Mean Squared Error (RMSE) for fault diagnosis is below 4.13 %. Therefore, the proposed fault diagnosis model demonstrates excellent performance in terms of accuracy and precision, providing robust support for improving the intelligence and accuracy of fault diagnosis in electromechanical equipment.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101811"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on performance optimization of agricultural intelligent energy meters driven by intelligent sensors under overload conditions

Measurement Sensors Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101812

Chengfei Qi, Yan Liu, Yaoyu Wang, Chaoran Bi, Wenwen Li

引用次数: 0

Application of electrical nonlinear load harmonic analysis method integrating intelligent sensor data in intelligent agricultural power management

Measurement Sensors Pub Date : 2025-01-17 DOI: 10.1016/j.measen.2025.101810

Jilei Qu, Meiying Niu, Qing Lin, Yanyan Li

{"title":"Application of electrical nonlinear load harmonic analysis method integrating intelligent sensor data in intelligent agricultural power management","authors":"Jilei Qu, Meiying Niu, Qing Lin, Yanyan Li","doi":"10.1016/j.measen.2025.101810","DOIUrl":"10.1016/j.measen.2025.101810","url":null,"abstract":"<div><div>In intelligent agricultural power management, the impact of harmonics on the power grid and its operating equipment cannot be ignored. The location of harmonic sources and the amplitude of harmonics injected into the power grid have significant randomness and nonlinearity. In order to accurately locate harmonic sources in the power grid, this paper proposes a method for detecting and locating harmonic sources based on nonlinear loads. This method constructs a judgment network by utilizing the load characteristics of each bus connected to the common connection point (PCC) and the characteristics of each type of load when running separately as training samples, and uses this standard to determine the position of the harmonic source, thereby achieving accurate localization of the harmonic source. In the experiment based on Matlab 2014a simulation platform, the results showed that adding the load characteristic data measured at PCC point in real-time operation to the judgment network can effectively determine the position of the harmonic source. Multiple load tests have shown that the judgment network has high accuracy. The experimental results show that among the 10 samples to be tested, only 2 load samples had misjudgments in their bus positions. In summary, the judgment network based on nonlinear loads can accurately detect and locate the location of harmonic sources in the power grid, and by increasing the number of training data sets, the judgment accuracy can be further improved. Therefore, this method, combined with intelligent sensor data, has high engineering application value for detecting and locating harmonic sources in intelligent agricultural power management.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101810"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of wireless data collection driven by intelligent sensors in bridge collision warning

Measurement Sensors Pub Date : 2025-01-13 DOI: 10.1016/j.measen.2025.101808

Canglong Zhao

{"title":"Application of wireless data collection driven by intelligent sensors in bridge collision warning","authors":"Canglong Zhao","doi":"10.1016/j.measen.2025.101808","DOIUrl":"10.1016/j.measen.2025.101808","url":null,"abstract":"<div><div>To address the challenges of ship deviation and bridge collision prevention in the water transportation industry, as well as to minimize missed alarms related to these incidents, the study develops an intelligent bridge collision and deviation warning system based on the analysis of ship communication characteristics. The system integrates radar and cameras for capturing and tracking ship navigation video images, followed by real-time communication feature analysis of the collected data. These results are transmitted via radio frequency signals to enable a real-time hull detection mechanism using ship detection algorithms. The system evaluates the ship's safety based on preset bridge warning positions and triggers sound and light alarms in case of superelevation or yaw events to prevent collisions and ensure safe navigation. Experimental results demonstrate that the system achieves a maximum missed alarm rate of only 6 %, with warning times under 1 ms across various environmental conditions. It provides precise warnings for ships within a range of approximately 300 m, maintaining an error margin of less than 1 m. The system showcases significant potential for practical applications and widespread adoption.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"38 ","pages":"Article 101808"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Expression of Concern: “Support vector machine and neural network for enhanced classification algorithm in ecological data”

Measurement Sensors Pub Date : 2025-01-01

引用次数: 0

Framework to analyze and exploit the smart home IoT firmware 框架分析和利用智能家居物联网固件

Measurement Sensors Pub Date : 2024-11-26 DOI: 10.1016/j.measen.2024.101406

Keshav Kaushik , Akashdeep Bhardwaj , Susheela Dahiya

{"title":"Framework to analyze and exploit the smart home IoT firmware","authors":"Keshav Kaushik , Akashdeep Bhardwaj , Susheela Dahiya","doi":"10.1016/j.measen.2024.101406","DOIUrl":"10.1016/j.measen.2024.101406","url":null,"abstract":"<div><div>Thousands of smart gadgets are linked to the internet each month, and due to various privacy and security issues, such devices may be vulnerable to evil attackers. Currently, there are around 8 billion devices online, and by the beginning of 2025, there will likely be 25 to 35 billion IoT devices globally. Despite this, the security of the devices is not receiving any more attention. Since operating system (OS) and hardware security have improved recently, researchers and hackers now seek vulnerabilities in other areas, such as firmware. When the firmware on many IoT devices isn't updated, it leaves them open to cyberattacks. The period when the Mirai Botnet was widely used is one of the situations in which we may have heard about firmware security. By gaining access to the equipment using the default credentials, the Mirai Botnet infects devices. Therefore, to analyze the firmware's contents for alteration during runtime, the authors of this research performed reverse engineering on it. Authors have exploited the smart home IoT firmware using our framework that identified ten critical network-based vulnerabilities within the firmware, with five vulnerabilities scoring a maximum CVSS score of 10.0 and the remaining five scoring 9.8, highlighting significant threats to smart home IoT devices. In addition, examining the firmware binaries demonstrates the widespread usage of dangerous functions like sprintf and strcpy in addition to the absence of critical security features like NX, PIE, RELRO, and stack protection. By offering a thorough analysis of the vulnerabilities and suggesting best practices for boosting the security of smart home IoT firmware, the results add to the body of information already in existence.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101406"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on on-site real-time detection technology of transmission line pulling force based on sensors 基于传感器的输电线路拉力现场实时检测技术研究

Measurement Sensors Pub Date : 2024-11-09 DOI: 10.1016/j.measen.2024.101219

Yang Shibiao , Zhong Yu , Huang Lei , Yuan Xing

{"title":"Research on on-site real-time detection technology of transmission line pulling force based on sensors","authors":"Yang Shibiao , Zhong Yu , Huang Lei , Yuan Xing","doi":"10.1016/j.measen.2024.101219","DOIUrl":"10.1016/j.measen.2024.101219","url":null,"abstract":"<div><div>During the transmission process, the stay wire tower plays an important role in supporting and stabilizing. By monitoring the tension of the stay wire, the deformation and abnormal force of the stay wire tower structure can be detected in a timely manner, and measures can be taken to adjust and repair it. In terms of wire tension monitoring, the main methods currently used include various monitoring technologies such as electronic, mechanical, and optical. However, traditional cable force monitoring methods have problems such as low monitoring accuracy and poor real-time performance, so it is necessary to develop a real-time detection technology based on sensors. This article uses computer technology to achieve real-time detection of transmission line pulling force based on sensors. Firstly, select a suitable sensor for measuring the pulling force. Then, the sensor data is transmitted to the computer system for real-time processing and analysis. Finally, the results are displayed through the interface, facilitating real-time monitoring and control by monitoring personnel. After experimental verification, the real-time detection technology based on sensors proposed in this article can accurately monitor the pulling force of transmission lines. The monitoring results show that this technology has high accuracy and good real-time performance, and can timely detect and handle abnormal wire force situations.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"37 ","pages":"Article 101219"},"PeriodicalIF":0.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmented and virtual reality based segmentation algorithm for human pose detection in wearable cameras 基于增强现实和虚拟现实的分割算法，用于可穿戴相机中的人体姿态检测

Measurement Sensors Pub Date : 2024-11-09 DOI: 10.1016/j.measen.2024.101402

Shraddha R. Modi , Hetalben Kanubhai Gevariya , Reshma Dayma , Adesh V. Panchal , Harshad L. Chaudhary

{"title":"Augmented and virtual reality based segmentation algorithm for human pose detection in wearable cameras","authors":"Shraddha R. Modi , Hetalben Kanubhai Gevariya , Reshma Dayma , Adesh V. Panchal , Harshad L. Chaudhary","doi":"10.1016/j.measen.2024.101402","DOIUrl":"10.1016/j.measen.2024.101402","url":null,"abstract":"<div><div>Pose graph optimization is a crucial method that helps reduce cumulative errors while estimating visual trajectories for wearable cameras. However, when the posture graph's size increases with each additional camera movement, the optimization's efficiency diminishes. In terms of ongoing sensitive applications, such as extended reality and computer-generated reality, direction assessment is a major test. This research proposes an incremental pose graph segmentation technique that accounts for camera orientation variations as a solution to this challenge. The computation only improves the cameras that have seen large direction changes by breaking the posture chart during these instances. As a result, pose graph optimization is essentially slowed down and optimized more quickly. For every camera that hasn't been optimized using a pose graph, the algorithm employs the wearable cameras at the start and end of each camera's trajectory segment. The final camera in attendance is then determined by weighted average the various postures evaluated with these wearable cameras; this eliminates the need for lengthy nonlinear enhancement computations, reduces disturbance, and achieves excellent accuracy. Experiments on the EuRoC, TUM, and KITTI datasets demonstrate that pose graph optimization scope is reduced while maintaining camera trajectories accuracy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"36 ","pages":"Article 101402"},"PeriodicalIF":0.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring EEG-Based biomarkers for improved early Alzheimer's disease detection: A feature-based approach utilizing machine learning 探索基于脑电图的生物标志物，以改进早期阿尔茨海默病的检测：基于特征的机器学习方法

Measurement Sensors Pub Date : 2024-11-07 DOI: 10.1016/j.measen.2024.101403

Hemlata Sandip Ohal, Shamla Mantri

{"title":"Exploring EEG-Based biomarkers for improved early Alzheimer's disease detection: A feature-based approach utilizing machine learning","authors":"Hemlata Sandip Ohal, Shamla Mantri","doi":"10.1016/j.measen.2024.101403","DOIUrl":"10.1016/j.measen.2024.101403","url":null,"abstract":"<div><div>This paper presents a comprehensive investigation into Electroencephalogram (EEG) signal processing and analysis techniques aimed at enhancing early diagnosis methods for Alzheimer's Disease (AD). Leveraging a dataset that has EEG data of individuals diagnosed with Mild Cognitive Impairment (MCI), AD, Healthy Controls, and the study explores Preprocessing Methods and Feature Extraction Techniques, with machine learning model notably Support Vector Machines (SVM).</div><div>In the preprocessing phase, a combination of high pass, lowpass, Savitzky–Golay, and median filters are applied, informed by a comprehensive review of filter comparison literature. Feature extraction encompasses three primary categories: ‘Statistical, ‘Frequency Domain’ and ‘Time Domain’. The scope of this work is to explore features in all these three domains and build SVM based model for efficient classification. In our investigation, we achieved a categorization accuracy of 92 % through the utilization of statistical features. Employing time domain features resulted in an accuracy of 87 %, while frequency domain features also yielded an 87 % accuracy rate in our study. The primary objective of this study is that it aims to enhance early AD diagnosis through advanced EEG signal processing and machine learning techniques, focusing on preprocessing methods, feature extraction, and classification accuracy.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"36 ","pages":"Article 101403"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0