查看更多>>摘要：? 2022 Elsevier LtdSensors, as an important part of structural health monitoring systems (SHMSs), will be abnormal sometimes due to their deterioration or environment effect, which will result in data loss during the health monitoring process of the structures. Data loss often happens in real monitoring applications, especially for wireless monitoring systems. Missing data, especially the long-term continuous missing data, will have a great impact on structural damage detection and condition evaluation. Usually, the long-term continuous missing data of the sensors are interpolated by traditional methods such as the correlation methods, which use a lot of normal monitoring data to build models and impute the missing data. However, in practice, many SHMSs in China have been in service for about 20 years or more, and many sensors installed have become faulty. It is usually difficult to obtain enough dataset fit for above methods. In this paper, a novel data imputation framework based on deep learning and data augmentation technique is therefore proposed, which enables the application of data modeling and missing data imputation based on the less remaining data when multiple sensors fail. Data imputation can be made between the same type of sensors (STSs) and also different types of sensors (DTSs). Generative adversarial network (GAN) based deep learning method and data augmentation technique are used for the imputation between the STSs; while long short-term memory (LSTM) network method is used for data imputation between the DTSs. The proposed methods are verified on the dataset of a real concrete bridge located in China, and results show that the proposed method achieves good performance.
查看更多>>摘要：? 2022 Elsevier LtdAggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95).
查看更多>>摘要：? 2022 Elsevier LtdA single-site lightning electromagnetic pulse (LEMP) localization approach based on deep learning was proposed and practiced. The approach was based on a large amount of ground-based lightning location and waveform data in the VLF/LF frequency band. A model for predicting the propagation distance of LEMP based on a deep learning method was proposed. The model used multiple types of LEMP waveform data as well as location data for parameter learning. The new model for detecting lightning activity has been validated against ADTD systems. The verification results for thunderstorms in the range of 1000 km show that the relative error of the model for the prediction of signal propagation distance was 2.75–13.08%. Affected by the azimuth calculation error, the relative error of single-site geolocation was 4.91–15.26%.
查看更多>>摘要：? 2022 Elsevier LtdThe porosity of construction materials has a direct impact on some of their properties such as sound absorption, heat transfer, and strength. Several traditional procedures have been developed to measure the porosity, however, their applications can be limited because of the low accuracy and measurement complexity among other drawbacks. This study evaluated an in-house constructed vacuum pycnometer that functions based on the ideal gas law. Its performance for determining skeletal density and porosity of a number of construction materials in the forms of powder and solid was evaluated against a commercial gas pycnometer, Archimedes’ method, CT scanning, and mercury intrusion (MIP) test. Compared with the commercial gas pycnometer using He, the maximum difference was less than 4% for powder materials and less than 6% for solid materials. The test results highlight the potentials of the proposed vacuum pycnometer for measuring the skeletal density and porosity of construction materials.
查看更多>>摘要：? 2022This paper proposes a temperature-controlled mid-wave infrared polarization radiation source with adjustable degree of linear polarization (DoLP), referred to as TMPSD, which will be useful for the polarization performance measurements of the thermal polarization imaging systems. The main parts of the TMPSD are a standard blackbody source, a variable temperature cell holder and a mid-wave infrared polarizer placed in it. The experimental results show that when the temperature range of the blackbody source changes between 20 °C and 100 °C, and that of the polarizer between ?120 °C and 20 °C, the DoLP of the radiation emitted from the TMPSD is 0.2683–0.9072 in the 3.0–5.2 μm bandwidth. The TMPSD exhibiting the large dynamic range and high accuracy can be readily adjusted. It is expected that the proposed TMPSD will effectively improve the quantitative accuracy of infrared polarization imaging systems during performance calibration and testing of mid-wave infrared polarization imaging systems.
查看更多>>摘要：? 2022 Elsevier LtdIn this work, a non-invasive heart rate (HR) and blood pressure (BP) monitoring system using a lead zirconate titanate (PZT) piezoelectric and photoplethysmographic (PPG) sensor has, preliminarily, been investigated. A modified pulse transit time (MPTT) is applied to demodulate HR, systolic blood pressure (SBP), and diastolic blood pressure (DBP) from obtained BP waveforms. In addition, an engineering application software written by LabView programming is, also, developed for dynamic signal monitoring and processing of desired parameters. Consequently, HR and BP measurements on 15 volunteers are performed and validated against a reference digital sphygmomanometer. The results show equivalent HR from both sensors with a mean absolute difference (MAD) of 1.78 beats per minute. Meanwhile, MADs of SBP and DBP measurements are 2.62 and 1.36 mmHg, complying with AAMI-BHS accuracy of “Grade A”, respectively. Therefore, PZT and PPG-based monitoring system could lead to development of accurate, non-invasive, and low-cost HR and BP devices.
查看更多>>摘要：? 2022 The AuthorsDifferent methods of alternative medicine may cause increased exposure to humans due to natural radioactivity. “Scalar energy medallions”, also known as “Quantum Pendants”, were investigated in the present work. Such objects are easily available in herbal stores or online. The elevated concentration of natural radionuclides from thorium series (232Th) and uranium series (238U) were measured in such pendants. The measured concentrations reached values up to 49 100 Bq·kg?1 for 228Ra, 51 600 Bq·kg?1 for 228Th, and 7 900 Bq·kg?1 for 238U and 226Ra. Radioactive disequilibrium among radionuclides was observed and this fact may be indirect evidence of the industrial (not natural) origin of these materials. Therefore, such medallions should be considered as NORM (Naturally Occurring Radioactive Materials) or TENORM (Technologically Enhanced NORM). According to the suppliers, the medallions should be carried directly on the skin of the human chest. Therefore, elevated radiation exposure is expected. Due to the presence of natural radionuclides in elevated concentrations, a mixed radiation field is the source of exposure to humans. Such conditions imply some metrological difficulties due to complex types of radiation. The article presents a proposal for a measurement approach in such cases. The effective dose from beta, X and gamma radiation for the human body was measured with the use of body phantoms and Panasonic thermoluminescent (TL) dosimeters. Various combinations of TL detectors, radiation filters and calculation protocols were applied to determine the doses from beta, X and gamma radiation. The obtained total effective dose exceeds the limit for the general population – 1 mSv.
查看更多>>摘要：? 2022 Elsevier LtdSemi-encapsulated microfiber Mach-Zehnder interferometer is designed and developed for temperature, salinity and pressure (TSP) sensing in seawater. Based on the theoretical analysis, sensing experiment is performed with typical sensitivities of ?2312 pm/℃, 631 pm/‰, and 3775 pm/MPa, respectively. To demodulate the signal with cross-sensitivity, sensitivity matrix method (SMM) and machine learning method (MLM) are used, respectively. By 25 tests under arbitrary TSP, relatively low errors of about 19.14 %, 4.01 % and 15.75 % are obtained based on the support vector regression (SVR) model. In addition, non-linear dependence of sensitivity on surrounding, SMM accuracy on sensing dips, stability of prediction and influence of the dataset used in training are also investigated. Finally, combination of SMM and MLM is realized, which shows relatively good performance for TSP measurement with errors of 10.67 %, 5.25 % and 16.76 %, respectively.
查看更多>>摘要：? 2022 Elsevier LtdEnergy shortage and excessive carbon dioxide emission caused by energy consumption in additive manufacturing (AM) have been increasingly severe and widely concerned. To address this issue, an additive manufacturing energy consumption (AMEC) measurement and prediction method for fabricating lattice structure based on Recallable Multimodal Fusion Network (RMFN) is proposed. The AMEC measurement model in 3D fabrication is first constructed according to the operating characteristics of 3D printer. As the backbone module of RMFN, the Multimodal Data Fusion Framework (MDFF) is then developed to predict AMEC by fusing the processing-, pixel- and geometric-level datasets, which are both generated during the design process of AM. In the light of the layer-wise fabrication principle in AM, a Laminated Context Recall Network (LCRN) is further designed to elegantly enforce the consistency of the contextual information among sliced layers, improving the regression accuracy of the AMEC prediction. Extensive numerical and physical experiments demonstrate that the proposed method performs better than state-of-the-art methods, motivating AM sustainability improvements and environmental performance.
查看更多>>摘要：? 2022 Elsevier LtdThis study focuses on dry wear of the surface of a cementitious materials loaded with a spinning wheel. This type of phenomenon is popular in surfaces when vehicles are starting to drive. The novelty of the article is to design and create a novel test stand to investigate this type of wear. In this study an automated portable noncontact three dimensional (3D) Light Amplification using the Stimulated Emission of Radiation (LASER) scanning technique and scanning electron microscopy (SEM) imaging were applied to determine the surface morphology during the analyzed wear mechanism. The most pertinent discoveries were visualized in the form of 3D isometric images and showed more intense destructive mechanism in the surfaces morphology while the wheel was spinning longer, which was represented by the decreasing volume of peaks at approximately 20%. This phenomena was strengthened with the extensive increase of the temperature of approximately 80 °C after 25 s. The model of the dry spinning wear mechanism was proved tests conducted by the SEM. Finally, the dry spinning wear process was divided to three stages distinguishing changes occurred in the near-surface area.
NSTL
Elsevier