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🎉🎉🎉Clarivate released the 2024 Journal Citation Reports. Sensors got its 2024 Impact Factor of 3.5. https://lnkd.in/guRd29q Many thanks to authors and their institutions, reviewers, academic editors, readers. Thank you for your supports and cooperation!

🗺️ Multi-Sensor HR Mass Data Models toward Multi-Temporal-Layered Digital Twins: Maintenance, Design and XR Informed Tour of the Multi-Stratified Appian Way (PAAA) 🧑 Raffaella Brumana, Simone Quilici, Luigi Oliva, Mattia Previtali, Marzia Gabriele and Chiara Stanga* 🏫 Politecnico di Milano, Parco Archeologico dell'Appia Antica 🔎 The article provides an overview of the digitisation project conducted by the Parco Archeologico dell’Appia Antica (PAAA) in Rome, focusing on an 11.7 km section of the Appian Way. This effort is part of the “Appia Regina Viarum” project, supporting the UNESCO heritage site candidacy of the Appian Way. Advanced #sensor technologies, including the Mobile #Mapping System (MMS), 360° Cameras, Terrestrial Laser Scanner (TLS), digital #cameras, and drones, are employed to collect extensive data sets. The primary goal is to create highly accurate three-dimensional (3D) models for knowledge enhancement, conservation, and communication purposes. Innovative tools are introduced to manage High Resolution 3D textured models, improving maintenance, management, and design processes over traditional CAD methods. The project aims to develop multi-temporal Digital Twins integrated with historical documentation, such as Piranesi’s imaginary views and architect Canina’s monument reconstructions. These informative models function as nodes within the DT, serving the PAAA’s geographic hub by means of an eXtended Reality (XR) platform: the paper proposes bridging the physical object and virtual models, contributing to supporting the operators in the maintenance planning as well as information dissemination and public awareness, offering an immersive experience beyond conventional reality. https://lnkd.in/gGH8CRNT

🗺️ Genetically Encoded Biosensors for the Fluorescence Detection of O2 and Reactive O2 Species 🧑 Marialaura Marchetti, Luca Ronda, Monica Cozzi, Stefano Bettati* and Stefano Bruno 🏫 University of Parma, Italian National Research council, Italy 🔎 The intracellular concentrations of #oxygen and #reactiveoxygenspecies (ROS) in living cells represent critical information for investigating physiological and pathological conditions. Real-time measurement often relies on genetically encoded proteins that are responsive to fluctuations in either oxygen or ROS concentrations. The direct binding or chemical reactions that occur in their presence either directly alter the #fluorescence properties of the binding protein or alter the fluorescence properties of fusion partners, mostly consisting of variants of the green fluorescent protein. Oxygen sensing takes advantage of several mechanisms, including (i) the oxygen-dependent hydroxylation of a domain of the hypoxia-inducible factor-1, which, in turn, promotes its cellular degradation along with fluorescent fusion partners; (ii) the naturally oxygen-dependent maturation of the fluorophore of green fluorescent protein variants; and (iii) direct oxygen binding by proteins, including heme proteins, expressed in fusion with fluorescent partners, resulting in changes in fluorescence due to conformational alterations or fluorescence resonance energy transfer. ROS encompass a group of highly reactive chemicals that can interconvert through various chemical reactions within biological systems, posing challenges for their selective detection through genetically encoded sensors. However, their general reactivity, and particularly that of the relatively stable oxygen peroxide, can be exploited for ROS sensing through different mechanisms, including (i) the ROS-induced formation of disulfide bonds in engineered fluorescent proteins or fusion partners of fluorescent proteins, ultimately leading to fluorescence changes; and (ii) conformational changes of naturally occurring ROS-sensing domains, affecting the fluorescence properties of fusion partners. In this review, we will offer an overview of these genetically encoded biosensors. https://lnkd.in/gEkuAdwf

📢 📢 📢 Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings 🧑🔬 Salman Saeed, Sikandar H. Sajid, and Luc Chouinard 🏫 University of Engineering & Technology, Peshawar, Pakistan; McGill University, Montreal, Canada 💥 Output-only modal analysis using ambient vibration testing is ubiquitous for the monitoring of structural systems, especially for civil engineering structures such as buildings and bridges. Nonetheless, the instrumented nodes for large-scale structural systems need to cover a significant portion of the spatial volume of the test structure to obtain accurate global modal information. This requires considerable time and resources, which can be challenging in large-scale projects, such as the seismic vulnerability assessment over a large number of facilities. In many instances, a simple center-line (stairwell case) topology is generally used due to time, logistical, and economic constraints. The latter, though a fast technique, cannot provide complete modal information, especially for torsional modes. In this research, corner-line instrumented nodes layouts using only a reference and a roving sensor are proposed, which overcome this issue and can provide maximum modal information similar to that from 3D topologies for medium-rise buildings. Parametric studies are performed to identify the most appropriate locations for sensor placement at each floor of a medium-rise building. The results indicate that corner locations at each floor are optimal. The proposed procedure is validated through field experiments on two medium-rise buildings. https://lnkd.in/gMB8KFAM

📢 📢 📢 Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors 🧑🔬:  Simone Mari*,Giovanni Bucci,Fabrizio Ciancetta,Edoardo Fiorucci and Andrea Fioravanti 🏫:Università degli Studi dell'Aquila 👓:Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions. 👉:https://lnkd.in/g6BRYwk7

📢 📢 📢 Crack Detection Method for Wind Turbine Tower Bolts Using Ultrasonic Spiral Phased Array 🧑🔬:  Hongyu Sun,Jingqi Dong,Xi Diao,Xincheng Huang,Ziyi Huang and Zhichao Cai* 🏫:北京交通大学 and 华东交通大学 and 英国诺丁汉大学 👓:High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry. 👉:https://lnkd.in/gkBa8qJC

📢 📢 📢 LPDi GAN: A License Plate De-Identification Method to Preserve Strong Data Utility 🧑🔬:  Xiying Li,Heng Liu,Qunxiong Lin*,Quanzhong Sun,Qianyin Jiang and Shuyan Su 🏫:中山大学 and Key Laboratory of Video and Image Intelligent Analysis and Application Technology, Ministry of Public Security and Guangdong Provincial Key Laboratory of Intelligent Transportation System and Guangzhou Maritime University 👓:License plate (LP) information is an important part of personal privacy, which is protected by law. However, in some publicly available transportation datasets, the LP areas in the images have not been processed. Other datasets have applied simple de-identification operations such as blurring and masking. Such crude operations will lead to a reduction in data utility. In this paper, we propose a method of LP de-identification based on a generative adversarial network (LPDi GAN) to transform an original image to a synthetic one with a generated LP. To maintain the original LP attributes, the background features are extracted from the background to generate LPs that are similar to the originals. The LP template and LP style are also fed into the network to obtain synthetic LPs with controllable characters and higher quality. The results show that LPDi GAN can perceive changes in environmental conditions and LP tilt angles, and control the LP characters through the LP templates. The perceptual similarity metric, Learned Perceptual Image Patch Similarity (LPIPS), reaches 0.25 while ensuring the effect of character recognition on de-identified images, demonstrating that LPDi GAN can achieve outstanding de-identification while preserving strong data utility. 👉:https://lnkd.in/gzibxtmT

📢 📢 📢 SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples 🧑🔬:  Oybek Eraliev,Kwang-Hee Lee and Chul-Hee Lee* 🏫:인하대학교 👓:Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time–frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability. 👉:https://lnkd.in/g_tmqFEF

📢 📢 📢  Research on High-Stability Composite Control Methods for Telescope Pointing Systems under Multiple Disturbances 🧑🔬 Rui Zhang, Kai Zhao, Sijun Fang, Wentong Fan, Hongwen Hai, Jian Luo, Bohong Li, Qicheng Sun, Jie Song, and Yong Yan 🏫 Sun Yat-sen University 💥 During the operation of space gravitational wave detectors, the constellation configuration formed by three satellites gradually deviates from the ideal 60° angle due to the periodic variations in orbits. To ensure the stability of inter-satellite laser links, active compensation of the breathing angle variation within the constellation plane is achieved by rotating the optical subassembly through the telescope pointing mechanism. This paper proposes a high-performance robust composite control method designed to enhance the robust stability, disturbance rejection, and tracking performance of the telescope pointing system. Specifically, based on the dynamic model of the telescope pointing mechanism and the disturbance noise model, an H∞ controller has been designed to ensure system stability and disturbance rejection capabilities. Meanwhile, employing the method of an H∞ norm optimized disturbance observer (HODOB) enhances the nonlinear friction rejection ability of the telescope pointing system. The simulation results indicate that, compared to the traditional disturbance observer (DOB) design, utilizing the HODOB method can enhance the tracking accuracy and pointing stability of the telescope pointing system by an order of magnitude. Furthermore, the proposed composite control method improves the overall system performance, ensuring that the stability of the telescope pointing system meets the 10 nrad/Hz1/2 @0.1 mHz~1 Hz requirement specified for the TianQin mission. https://lnkd.in/gGDPhvHi

📢 📢 📢  RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN 🧑🔬 Yuan Li, Mengmeng Zhang, Hongyuan Jing, and Zhi Liu 🏫 North China University of Technology, North China University of Technology, Beijing Union University 💥 Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity. https://lnkd.in/gc5f8KpR