Measurement最新文献_第4页

Machine learning-based online parameter estimation for noise-resilient process measurements using state-dependent models 基于状态相关模型的噪声弹性过程测量的机器学习在线参数估计

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-04 DOI: 10.1016/j.measurement.2025.119222

Behrouz Kiani Talaei, Mir Mohammad Khalilipour, Jafar Sadeghi

{"title":"Machine learning-based online parameter estimation for noise-resilient process measurements using state-dependent models","authors":"Behrouz Kiani Talaei, Mir Mohammad Khalilipour, Jafar Sadeghi","doi":"10.1016/j.measurement.2025.119222","DOIUrl":"10.1016/j.measurement.2025.119222","url":null,"abstract":"<div><div>Measurement noise remains a critical challenge in industrial process control, often leading to inaccurate estimations, actuator wear, and degraded control performance. Traditional data reconciliation filters often rely on fixed-parameter models and require large sets of input variables, limiting their adaptability to process variations. This study addresses these limitations by introducing a noise reduction framework that combines dynamic data reconciliation with online parameter estimation, using a nonlinear state-dependent parameter (SDP) modeling approach. The proposed framework adaptively updates model parameters based on past reconciled data, enhancing robustness and accuracy under dynamic and noisy operating conditions. The method was evaluated in two case studies. In an industrial debutanizer process, the framework significantly reduced the standard deviation of manipulated variables by up to 54 %, improving control smoothness and actuator stability. In a simulated benzene–toluene distillation column, it outperformed a Refined Instrumental Variable-based Kalman Filter (RIV-KF) by reducing measurement noise by 50 %, while maintaining reliable performance under process state changes (PSC), even when unmodeled inputs varied significantly. Furthermore, the proposed filter decreased benzene concentration variability by 17 % across trays and reduced reboiler energy consumption by approximately 0.1 million kilocalories over 3.5 h. These results demonstrate the practicality of using reconciled-data-based online model adaptation for improving both measurement reliability and control efficiency in complex industrial processes.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119222"},"PeriodicalIF":5.6,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved ICNN-LSTM model classification based on accelerometer sensor data for hazardous state assessment of magnetic adhesion climbing wall robots 基于加速度计传感器数据的改进ICNN-LSTM模型分类磁力附着爬墙机器人危险状态评估

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-04 DOI: 10.1016/j.measurement.2025.119147

Zhen Ma, He Xu, Jielong Dou, Yi Qin, Xueyu Zhang

{"title":"Improved ICNN-LSTM model classification based on accelerometer sensor data for hazardous state assessment of magnetic adhesion climbing wall robots","authors":"Zhen Ma, He Xu, Jielong Dou, Yi Qin, Xueyu Zhang","doi":"10.1016/j.measurement.2025.119147","DOIUrl":"10.1016/j.measurement.2025.119147","url":null,"abstract":"<div><div>The magnetic adhesive crawler-type climbing wall robot is widely used in high-altitude inspection, welding, and cleaning tasks. However, during operation, the influence of self-weight and payload may generate a flipping moment, leading to detachment of the magnetic pads and consequently posing safety hazards. To address this issue, this paper proposes a data acquisition strategy based on micro-electromechanical system (MEMS) accelerometer sensors, integrated with a deep learning-based classification approach for real-time monitoring of the attachment state recognition of the climbing wall robot and prevention of potential risks. First, a high-precision data acquisition strategy was developed for MEMS accelerometer sensors that is capable of effectively capturing subtle vibration information. Subsequently, an innovative feature extraction and classification model combining adaptive convolutional neural networks (ICNN) and long short-term memory networks (LSTM), referred to as ICNN-LSTM, was proposed. Experimental results indicate that the proposed method accurately extracts features from subtle vibrations and demonstrates superior classification accuracy compared to other models. This study provides an effective technical solution for ensuring the safe operation of magnetic-adhesion crawler-type climbing wall robots, showcasing significant practical value.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119147"},"PeriodicalIF":5.6,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DiffInpaint: line drawing guided murals restoration with diffusion model DiffInpaint：线条绘制引导壁画修复与扩散模型

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-04 DOI: 10.1016/j.measurement.2025.119223

Xin Tang , Yingyi Sui , Kexue Sun , Lingqi Xiang

{"title":"DiffInpaint: line drawing guided murals restoration with diffusion model","authors":"Xin Tang , Yingyi Sui , Kexue Sun , Lingqi Xiang","doi":"10.1016/j.measurement.2025.119223","DOIUrl":"10.1016/j.measurement.2025.119223","url":null,"abstract":"<div><div>The artistic value of murals is immensely precious, yet these murals are highly susceptible to damage from natural or anthropogenic factors. Mural images present challenges such as semantic loss and texture ambiguity, which limit the effectiveness of conventional deep learning methods. This study aims to develop an effective digital restoration framework addressing complex structural and textural issues characteristic of mural images. To achieve this, we propose a mural restoration method based on a diffusion model. The approach involves simulating damaged mural regions by repeatedly adding Gaussian noise during the forward process of the diffusion model. In the reverse generation phase, lines are utilized as conditional inputs to guide and enhance the U-net network’s structural and textural predictions. Additionally, a two-stage training strategy is introduced: first, a line encoder is pre-trained to generate conditional feature maps for the Latent Diffusion Model (LDM); subsequently, the LDM is trained based on these conditioned maps. Experimental results indicate that our method effectively repairs various mural defects and damages, maintaining overall stylistic consistency and detail. This approach contributes to the quality and efficiency of cultural heritage image restoration, providing a viable technical support for digital mural preservation and restoration.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119223"},"PeriodicalIF":5.6,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systems metrology for future cities at the smart metrology campus (SMC) 智能计量园区（SMC）面向未来城市的系统计量

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-04 DOI: 10.1016/j.measurement.2025.119197

Barbara Jung , Daniel Hutzschenreuter , Mona Wehming , Michael Ulbig , Vivien Peltason , Alexander Kammeyer

引用次数: 0

Multi-source deep transfer learning with stacked denoising autoencoder and Wasserstein distance for wind power prediction in new wind farm 基于叠置去噪自编码器和Wasserstein距离的多源深度迁移学习新风场风力预测

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119225

Haiyan Xu , Wenguang Hao , Yong Zhao , Hongda Tian

{"title":"Multi-source deep transfer learning with stacked denoising autoencoder and Wasserstein distance for wind power prediction in new wind farm","authors":"Haiyan Xu , Wenguang Hao , Yong Zhao , Hongda Tian","doi":"10.1016/j.measurement.2025.119225","DOIUrl":"10.1016/j.measurement.2025.119225","url":null,"abstract":"<div><div>High-precision wind power forecasting is essential for ensuring the secure and stable integration of wind energy into power grids. However, newly commissioned wind farms typically face data scarcity due to their limited operational history, making accurate power output prediction particularly challenging. To overcome the challenge of insufficient historical data in newly established wind farms, this paper proposes a transfer learning-based deep neural network for high-precision wind power point forecasting, integrating multi-source data assimilation. Firstly, a stacked denoising autoencoder is used to establish feature correlations between the source and target domain input data. Then, the parameters of a well-trained long short-term memory (LSTM) prediction model from a source wind farm are transferred to the target farm’s prediction model. Finally, multiple prediction models are integrated by calculating the Wasserstein distance between each source domain and the target domain to form the final wind power forecasting model. Experimental results demonstrate that the proposed transfer model outperforms other comparison models in prediction accuracy, offering strong adaptability and broad applicability for wind power forecasting in newly established wind farms.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119225"},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and comparison of a PI controller and an ANN controller for a three-phase three-wire grid-connected NPC inverter 三相三线并网型NPC逆变器PI控制器与ANN控制器的设计与比较

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119224

Yunus Emre Yağan

{"title":"Design and comparison of a PI controller and an ANN controller for a three-phase three-wire grid-connected NPC inverter","authors":"Yunus Emre Yağan","doi":"10.1016/j.measurement.2025.119224","DOIUrl":"10.1016/j.measurement.2025.119224","url":null,"abstract":"<div><div>In this study, two different control systems are proposed for a three-phase, three-level, three-leg, three-wire (3P3L3L-3 W), grid-connected (GC) neutral point clamped (NPC) voltage source inverter. The first controller is a proportional-integral (PI)-based technique developed using a detailed mathematical model, which includes a virtual closed loop created between the inverter and grid neutral points via Kirchhoff’s voltage law. This design offers decoupled <em>dq</em> axes current control (CC) as well as capacitor voltage balancing (CVB) combined with 0-axis CC. The second controller is an artificial neural network (ANN)-based technique trained with the simulation data of the PI-based controller. Three independent ANN controllers are constructed for <em>d</em>-axis, <em>q</em>-axis, and CVB control, respectively. Each ANN is designed with minimal structure to achieve low computational cost without compromising performance. The originality of the study lies in the unified modeling-based design of the PI-based controller including a novel CVB method, and the proposed low-complexity multi-ANN structure that replicates and enhances this control behavior. Both controllers are evaluated through nine test cases, including noise injection, sensor errors, grid disturbances, non-linear power sources, and component aging. The simulation results show that both approaches successfully regulate the inverter. The ANN-based controller outperforms the PI-based one in terms of steady-state accuracy and robustness under uncertain conditions, while also reducing computational burden compared to similar ANN controllers reported in the literature.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119224"},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLFI-YOLOv8s: An accurate and efficient model for bellows crack detection in air spring CLFI-YOLOv8s：一种准确、高效的空气弹簧波纹管裂纹检测模型

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119203

Junjie Chen , Jiahui Ai , Chengping Zhong , Zhengchao Liu , Gaoxu Wu

{"title":"CLFI-YOLOv8s: An accurate and efficient model for bellows crack detection in air spring","authors":"Junjie Chen , Jiahui Ai , Chengping Zhong , Zhengchao Liu , Gaoxu Wu","doi":"10.1016/j.measurement.2025.119203","DOIUrl":"10.1016/j.measurement.2025.119203","url":null,"abstract":"<div><div>Cracks in air spring bellows significantly impact their service life. However, the surface cracks of bellows often exhibit low contrast, poor image quality, and complex backgrounds. Traditional detection methods struggle to achieve high precision and efficient crack identification. To address this issue, this paper proposes a CLFI-YOLOv8s model specifically designed for detecting cracks in bellows. Firstly, the convolutional priority multi-space (CPMS) attention module is integrated into the backbone to refine multi-scale feature extraction and localization. Subsequently, the C2f-LarK module in the neck expands the receptive field with large kernels, thereby improving spatial perception and fine-grained feature capture. To optimize efficiency, Partial Convolution (PConv) is integrated into the head, forming the Faster Detect structure, which reduces computational cost while maintaining detection accuracy. Additionally, Inner-Shape IoU replaces CIoU to further improve detection accuracy and generalization. Experimental results demonstrate that the CLFI-YOLOv8s outperforms the YOLOv8s in detection performance. The model achieves improvements in precision, recall, [email protected], and [email protected]–0.95 by 3.3 %, 3.5 %, 1.8 %, and 6.1 %, respectively. Simultaneously, the weight size, parameters, and GFLOPs are reduced by 14.4 %, 14.2 %, and 26.4 %, respectively. The results indicate that the CLFI-YOLOv8s model excels in crack detection tasks, demonstrating significant potential and practical value as a monitoring tool for air spring bellows cracks.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119203"},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Micro-motion enhanced multi-person activity recognition with millimeter-wave radar 微运动增强了毫米波雷达对多人活动的识别

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119090

Haoming Feng, Huaqing Li, Wenwen Zhu, Denghao Li, Yukun Huang

{"title":"Micro-motion enhanced multi-person activity recognition with millimeter-wave radar","authors":"Haoming Feng, Huaqing Li, Wenwen Zhu, Denghao Li, Yukun Huang","doi":"10.1016/j.measurement.2025.119090","DOIUrl":"10.1016/j.measurement.2025.119090","url":null,"abstract":"<div><div>As a non-contact sensing device, millimeter-wave radar exhibits unique strengths in human activity recognition (HAR). Existing methods rely on micro-Doppler signatures for activity classification, but they often encounter feature aliasing in multi-person activity recognition (MPAR) scenarios. Although point cloud-based approaches can distinguish individual targets, they primarily extract static morphological features, neglecting the micro-motion information of human joints, which is crucial for accurate activity recognition. To address these limitations, we proposes an innovative MPAR framework that integrates spatial point clouds and micro-motion features. First, an improved point cloud data association algorithm is applied to achieve multi-target point cloud feature separation, followed by a dynamic projection mechanism to construct time–Doppler feature maps. Then, a torso micro-motion enhancement algorithm is designed to enhance the details of human body movements. Finally, a CNN-LSTM hybrid network architecture with a temporal-attention is constructed for action classification. Experimental results show that the proposed micro-motion enhancement algorithm improves recognition accuracy by 27.1% and 2.3%, compared to two traditional time–frequency analysis methods. Furthermore, MPAR task in occlusion scenarios achieves recognition accuracy of 93.5%. In summary, proposed framework not only retains the inherent advantages of millimeter-wave radar but also significantly enhances multi-person activity recognition in complex scenarios.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119090"},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpreting unit symbols in the language of science 解释科学语言中的单位符号

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119069

Blair D. Hall

引用次数: 0

Optimizing photoexcitation parameters to enhance salt stress features conveyed by light-induced bioelectrogenesis 优化光激发参数以增强光诱导生物电生成所传递的盐胁迫特征

IF 5.6 2区工程技术

Measurement Pub Date : 2025-10-03 DOI: 10.1016/j.measurement.2025.119238

Chang-Yong Tao, Jin-Hai Li, De-Hua Gao, Shu-Ming Yang, Yu-Tan Wang, Fu-Long Ma

{"title":"Optimizing photoexcitation parameters to enhance salt stress features conveyed by light-induced bioelectrogenesis","authors":"Chang-Yong Tao, Jin-Hai Li, De-Hua Gao, Shu-Ming Yang, Yu-Tan Wang, Fu-Long Ma","doi":"10.1016/j.measurement.2025.119238","DOIUrl":"10.1016/j.measurement.2025.119238","url":null,"abstract":"<div><div>Previous studies have demonstrated that light-induced bioelectrogenesis (LIB) can be a valuable tool for predicting crop salt tolerance. However, individual differences among plants lead to significant waveform variations under salt stress, which reduces the accuracy of LIB-based assessments of salt tolerance. To address this issue, this study optimized photoexcitation parameters to enhance LIB’s salt stress features. LIB were collected from wheat seedlings exposed to different salt stress levels, light qualities, and illumination/darkness cycles. The classification accuracy of deep learning models was compared across different excitation parameters to determine the optimal parameter combination. Additionally, SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of individual LIB features to classification performance. The results revealed that under a fixed photosynthetic photon flux density (PPFD) of 200 μmol·m<sup>−2</sup>·s<sup>−1</sup>, the combination of red light and a 10 min/10 min illumination/darkness cycle provided the best enhancement of LIB salt stress features, with classification accuracy reaching 92.00 % in the one-dimensional convolutional neural network (1D-CNN) representing a 10 % improvement compared to white light. SHAP analysis further revealed that the features with the most significant impact on classification performance were concentrated in four specific time intervals. Moreover, shorter darkness periods, and blue light excitation significantly reduced the mean potential difference (MPD) and peak value of LIB, which in turn decreased the model’s classification accuracy. This study not only provided new insights into the mechanisms underlying LIB generation under salt stress, but also advanced its application in breeding stress-resistant crops, thereby accelerating the development cycle of salt-tolerant varieties.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119238"},"PeriodicalIF":5.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0