High resolution bolt pre-load looseness monitoring using coda wave interferometry

This paper proposes a new concept, named the Detectable Resolution of Bolt Pre-load (DRBP), by using the coda wave interferometry (CWI) to quantitatively measure the pre-load looseness at a high resolution. Due to its characteristics of roughness, irregularity, and randomly distributed asperities, the contact surface of the bolted components can function as a natural interferometer to scatter the propagation waves. The multiply-scattered coda waves can amplify the slight changes in the travel path and show the visible perturbation in the time domain. By calculating the time-shifted correlation coefficient of coda waves before and after the slight pre-load looseness, the tiny pre-load changes can be clearly revealed. To evaluate the feasibility of the proposed method, a theoretical model considering the time shifts of coda waves and the variations of pre-load is established. Based on the acoustoelastic effect and the wave path summation theory of coda wave interferometry, the model shows that the time shifts of coda waves change linearly with the variations of pre-load. Verification experiments are conducted, and the results show that the R-square values of the fitting curves are larger than 0.9216. In addition, the proposed approach has the feature of high resolution. The ultimate pre-load resolution of the proposed approach is 0.331%, that is, when the variation of pre-load is larger than 0.331%, it can be detected. Therefore, theoretical analysis and experimental results prove that the CWI-based pre-load detection approach holds great potential for the detection of bolt pre-load looseness, especially during the initial stage.

Design and Analysis of Road Load Detection Machine Based on Computer Technology

In this paper, an experimental device is designed for measuring vehicle dynamic load, the structure and stress of the equipment are analyzed by computer technology. The device design mainly includes vehicle, road surface, vehicle transmission, and control [1]. The vehicle is designed based on a 2-DOF vehicle model, the road is designed based on the Pasternak foundation model, and the control mainly uses a single-chip microcomputer. The dynamic response of vehicles to the road at different speeds is analyzed through the experiment [2].

Non-intrusive Load Identification Method based on SAGOA-GMM Algorithm

Non-intrusive Load Identification play an important role in daily life. It can monitor and predict grid load while statistics and analysis of user electricity information. Aiming at the problems of low non-intrusive load decomposition ability and low precision when two electrical appliances are started and stopped at the same time, a new type of clustering and decomposition algorithm is proposed. The algorithm first analyses the measured power and use DBSCAN to filter out the noise of the collected data. Secondly, the remaining power points are clustered using the Adaptive Gaussian Mixture Model (AGMM) to obtain the cluster centres of the electrical appliances, and finally correlate the corresponding current waveform to establish a load characteristic database. In terms of load decomposition, a mathematical model was established for the magnitude of the changing power and current. The Grasshopper optimization algorithm (GOA) is optimized by introducing simulated annealing (SA) to identify and decompose electrical appliances that start and stop at the same time. The result of the decomposition is checked by the current similarity test to determine whether the result of the decomposition is correct, thereby improving the recognition accuracy. Experimental data shows that the combination of DBSCAN and GMM can can identify similar power characteristics. The introduction of SA makes up for the weakness of GOA and gives full play to the advantages of GOA's high identification efficiency. Finally, the test is carried out through the load detection data of the simultaneous start and stop of the two equipment. The test results show that the proposed method can effectively identify the simultaneous start and stop of two loads and can solve the problem of low recognition rate caused by the similar load power, which lays the foundation for the development of non-intrusive load identification in the future.

Viral Load, Detection Rate and Sensitivity of Nasopharyngeal and Oropharyngeal Swab Sampling for Diagnosis of COVID-19

The rapid spread of the COVID-19 pandemic has led to a major public health crisis. Accurate screening methods for COVID-19 infection is essential and crucial for case detection, isolation, prevention and control of the current pandemic. At present, nasopharyngeal and oropharyngeal swabs are typically used as the method of choice for the diagnosis of SARS-CoV-2 infection. We carried out a review on the accuracy of the two different sampling sites, the nasopharyngeal and oropharyngeal swab sampling, focusing on the viral load, detection of positive cases and sensitivity in real-time polymerase chain reaction (RT-PCR) assay in diagnosing COVID-19. A total of 25 articles related to the topic were selected out of 5221 articles searched online using Scopus, PubMed and Medline, Embase, Web of Science, and Google scholar with the keywords COVID-19, SARS-CoV-2, nasopharyngeal swab, oropharyngeal swab, nasal swab and throat swab. All full text original articles were obtained and reviewed. Nasopharyngeal swab had significantly higher SARS-CoV-2 load than oropharyngeal swab (mean Ct value ranging from 24.3-37.8, higher detection of positive rate (highest rate 62.5%) and sensitivity (highest sensitivity 98.3%, P<0.05) in RT-PCR assay compared to oropharyngeal swab. Based on the scientific literature review, both nasopharyngeal and oropharyngeal swabs were reported to have 30% probability of yielding false negative results; thus clinically suspicious patients with negative results should be viewed with concern. In conclusion, although several methods of COVID-19 screening and type of specimen are available, nasopharyngeal swab is the best option for large scale screening as it yields significantly higher viral load, higher detection of positive rate among cases and higher sensitivity in RT-PCR assay compared to oropharyngeal swab in detecting SARS-CoV-2.

Detection of SARS-CoV-2 using qRT-PCR in saliva obtained from asymptomatic or mild COVID-19 patients, comparative analysis with matched nasopharyngeal samples

Objectives The accurate detection of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is essential for the diagnosis of coronavirus disease 2019 (COVID-19). We compared the quantitative RT-PCR results between nasopharyngeal swabs and saliva specimens. Methods A COVID-19 outbreak occurred on a cruise ship at Nagasaki port, Japan. We obtained 123 nasopharyngeal swabs and saliva each from asymptomatic or mild patients in the late phase of infection. Results The intervals from the diagnosis to the sampling were 25.5 days for nasopharyngeal swabs and 28.9 days for saliva. The positive rate was 19.5% (24/123) for nasopharyngeal swabs and 38.2% (47/123) for saliva (P = 0.48). The quantified viral copies (mean ± SEM copies/5 μl) were 9.3±2.6 in nasopharyngeal swabs and 920±850 in saliva (P = 0.0006). Conclusions The advantages of saliva specimens include positive rate improvement and accurate viral load detection. Saliva may be used as a reliable sample for SARS-CoV-2 detection.