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Roberto De Fazio, Nicola Ivan Giannoccaro, Miguel Carrasco, Ramiro Velazquez, Paolo Visconti,roberto.defazio@unisalento.it,ivan.giannoccaro@unisalento.it,miguel.carrasco@uai.cl,rvelazquez@up.edu.mx,paolo.visconti@unisalento.it
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 11, Pages 1413-1442 doi: 10.1631/FITEE.2100085
Yanfei Chen, Jing Guo, Chunlei Chen, Ding Shi, Daiqiong Fang, Feng Ji, Lanjuan Li
Engineering 2021, Volume 7, Issue 4, Pages 507-514 doi: 10.1016/j.eng.2020.04.014
Several studies have indicated that the oral and gut microbiota may exhibit differences in patients with cirrhosis. Less is known about the microbiota in the stomach, which is located between the oral cavity and the intestinal tract. In this study, the gastric mucosal microbiota of patients with liver cirrhosis and controls were analyzed with 16S ribosomal RNA (rRNA) pyrosequencing. Cirrhotic patients had significantly
lower Helicobacter pylori (H. pylori) infection rates, as confirmed by both the histological method and the pyrosequencing method. In H. pylori-negative subjects, gastric bacterial communities of healthy and cirrhosis cohorts were clustered into four clusters based on bacterial compositions: Cluster_1 and Cluster_2 (mostly cirrhosis), Cluster_3 (mostly healthy), and Cluster_4 (around half of each). Compositional and functional differences were observed among these different clusters. At the genus level, Cluster_1 and Cluster_2 showed enrichment of Neisseria and Streptococcus, respectively. Functionally, Cluster_2 was characterized as depleted of genetic information processing, as well as of modules related to glycan biosynthesis and metabolism. Patients in Cluster_2 had more severe gastrointestinal symptoms and a higher rate of previous endoscopic variceal ligation (EVL) therapy than patients in other clusters. Our findings suggest that the colonization of both H. pylori and non-H. pylori is influenced in liver cirrhosis. Although the H. pylori-negative gastric mucosal microbiota showed considerable heterogeneity, associations between specific gastric microbiota and clinical characteristics could be observed. Previous EVL therapy might lead to a distinct structure of the gastric mucosal microbiota, thus aggravating the gastrointestinal symptoms in H. pylori-negative cirrhotic patients.
Keywords： Microbiome Liver cirrhosis Symptoms Varices Gastric endoscopy
Sonic General Non-destructive Testing Technique
Chen Jimao
Strategic Study of CAE 2000, Volume 2, Issue 4, Pages 64-69
This paper describes new general multi-mode non-destructive testing (NDT) of composite materials and bonded structures for detecting discontinuities (defects) , the only multi-mode one of its kinds. This technique which is based on sonic and ultrasonic testing performs five different modes of testing to detect disbond, unbond, delamination, porosity, crushed core and other defects in composite materials and bonded structures. It equally suits for applications in manufacturing, maintenance and repair of composite materials and structures which almost include them in common use now availability. Good repeatability and reliability have been found. This paper discusses the principles of the five sonic mode constitute multi-mode sonic general bondtester and demostrates the vast range of propect for general NDT from our and our internal and external comrades* much practical experinence.
Keywords： sonic general testing general non-destructive testing (NDT) non-destructive testing of composite materials mechanical impedance analysis (MIA) vibration analysis (VA) resonance testing
Nondestructive Testing Method to Assess and Detect Road Performance
Guo Chengchao,Xu Pengfei and Zhong Yanhui
Strategic Study of CAE 2017, Volume 19, Issue 6, Pages 72-79 doi: 10.15302/J-SSCAE-2017.06.011
At present, China is faced with major problems in infrastructure management, such as improper scheduling and belated implementation of maintenance work or huge maintenance budget outlays. As such, it is essential to implement rapid and effective management measures to ensure road safety, prevent catastrophic damage, and increase economic growth. In this paper, five nondestructive road testing methods and their associated testing equipment are introduced according to the American Society for Testing and Materials. These include the falling weight deflectometer, ground penetrating radar, macro texture depth, international roughness index, and spectral analysis of surface waves. The operating principles and applications of each testing method are elaborated to guide relevant personnel to make a reasoned choice according to their actual situation. The application of these testing methods will accelerate the assessment of projects without traffic closures, likely provide a new approach for establishing a high-efficiency intelligent road network monitoring system, and will provide a practical and feasible method for sustainable road development and the efficient utilization of capital.
Keywords： road detection road defects nondestructive testing
Extension Detecting Technology
Yu Yongquan
Strategic Study of CAE 2001, Volume 3, Issue 4, Pages 88-94
Detecting is a key technology which has widely been used in almost all fields. However, there are two problems in traditional detecting methods. First, some physical value can not be detected; Second, it is difficult to improve the accuracy according to the way currently in effect. In this paper, based on the transformation concept of the contradiction, a new detecting technology—extension detecting technology (EDT) , which is totally different from the existing ones is developed. EDT takes advantage of the concept of matter element and the related methods of extrinsic. It use the transformation of matter element to study detecting technology. In order to solve detecting problems of undetectable physical value the related concepts, basic principle and frame of extension detecting technology are introduced. An implementing way for its application to engineering is proposed, and an example is given as well.
Keywords： extension detecting technology extrinsic transform of matter element
Generating Available Scheme for Extension Detecting Technology
Yu Yongquan ,Zhang Jiwen
Strategic Study of CAE 2002, Volume 4, Issue 1, Pages 64-68
This work shows a method to obtain an available scheme for extension detecting technology. Based on the theory of extenics, some definitions and conceptions about extension detecting are described in this paper. At the same time, the procedures of optimization and evaluation are explored in order to get the better scheme.
Keywords： detecting matter-element detecting element extension set extension tree
Yongyue Wei, Liangmin Wei, Yue Jiang, Sipeng Shen, Yang Zhao, Yuantao Hao, Zhicheng Du, Jinling Tang, Zhijie Zhang, Qingwu Jiang, Liming Li, Feng Chen, Hongbing Shen
Engineering 2020, Volume 6, Issue 10, Pages 1141-1146 doi: 10.1016/j.eng.2020.04.008
The majority of cases infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China centered in the city of Wuhan. Despite a rapid increase in the number of cases and deaths due to the coronavirus disease 2019 (COVID-19), the epidemic was stemmed via a combination of epidemic mitigation and control measures. This study evaluates how the implementation of clinical diagnostics and universal symptom surveys contributed to epidemic control in Wuhan. We extended the susceptibles-exposed-infectious-removed (SEIR) transmission dynamics model by considering three quarantined compartments (SEIR+Q). The SEIR+Q dynamics model was fitted using the daily reported number of confirmed infections and unconfirmed cases by clinical diagnostic criteria up to February 14, 2020, in Wuhan. Applying the model to carry forward the pre-February 14 trend in Wuhan, the number of daily new diagnosed cases would be expected to drop below 100 by March 25, below 10 by April 29, and reach 0 by May 31, 2020. The observed case counts after February 14 demonstrated that the daily new cases fell below 100 by March 6, below 10 by March 11, and reached 0 by March 18, respectively, 19, 49, and 74 d earlier than model predictions. By March 30, the observed number of cumulative confirmed cases was 50 006, which was 19 951 cases fewer than the predicted count. Effective reproductive number R(t) analysis using observed frequencies showed a remarkable decline after the implementation of clinical diagnostic criteria and universal symptom surveys, which was significantly below the R(t) curve estimated by the model assuming that the pre-February 14 trend was carried forward. In conclusion, the proposed SEIR+Q dynamics model was a good fit for the epidemic data in Wuhan and explained the large increase in the number of infections during February 12–14, 2020. The implementation of clinical diagnostic criteria and universal symptom surveys contributed to a contraction in both the magnitude and the duration of the epidemic in Wuhan.
Keywords： COVID-19 Extended SEIR+Q dynamics model Clinical diagnostic criteria Universal symptom survey Evaluation of the intervention effect
Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain Article
Qianyun Zhang,Kaveh Barri,Saeed K. Babanajad,Amir H. Alavi
Engineering 2021, Volume 7, Issue 12, Pages 1786-1796 doi: 10.1016/j.eng.2020.07.026
This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) method in the image frequency domain. The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks. In order to improve the training efficiency, images are first transformed into the frequency domain during a preprocessing phase. The algorithm is then calibrated using the flattened frequency data. LSTM is used to improve the performance of the developed network for long sequence data. The accuracy of the developed model is 99.05%, 98.9%, and 99.25%, respectively, for training, validation, and testing data. An implementation framework is further developed for future application of the trained model for large-scale images. The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time. The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.
Keywords： Crack detection Concrete bridge deck Deep learning Real-time
Research on control strategy of real-time testing system for hydraulic cylinder
Gong Jin,Tan Qing,Zhao Yuming,Heng Baoli
Strategic Study of CAE 2011, Volume 13, Issue 3, Pages 71-76
Aiming at the disadvantage of traditional hydraulic cylinder testing system, such as old measure, low detection precision, etc.,a new control-strategy to checkout property of tested cylinder was proposed. The new strategy succeeded to modularize the tested property and organically combine tested properties of cylinder together during hydraulic cylinder moving. The new real-time testing system made use of the technic of PLC(programmable logic controller), CoDeSys configuration and Visual Basic 6.0. The test results indicated that the real-time testing system made hydraulic cylinder properties be inspected successfully; test data and curve could be printed out, and increased testing efficiency.
Keywords： hydraulic cylinder real-time testing modularization control strategy
Zhong Qunpeng,Wu Huaisheng,Zhang Zheng,Tian Yongjiang
Strategic Study of CAE 1999, Volume 1, Issue 1, Pages 43-48
This paper, from the viewpoint of epistemology, clarifies the position, role, and attributes of failure analysis and prevention in the development of science and technology. The discipline of failure analysis and prevention is multidisciplinary and inter-synthetical. It dependents on the development of both aplied sciences and pure sciences and has great positive effects on them.So, it is one of the most active factors in the conclusion of “Science and technology are the first productivity.” This paper also describes the dialectic relationship between failure analysis and failure prevention methodologically. While failure analysis is a process of converse thinking expost factor, which explores scientifically causes from results, the failure prevention is a process of prior thinking, which predicts results from causes.It is necessary to utilize the both thinking methods simultaneously and convert the afterwards analysis to the prior one for the development of science and technology. Finally, this paper summarizes the main contents, techniques, methodology, and future trends of failure analysis and prevention and the relationship between nondestructive testing and failure analysis and prevention.
Keywords： mechanical and electrical equipment failure analysis safety assessment nondestructive testing
Generative adversarial network based novelty detection usingminimized reconstruction error Article
Huan-gang WANG, Xin LI, Tao ZHANG
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 116-125 doi: 10.1631/FITEE.1700786
Keywords： Generative adversarial network (GAN) Novelty detection Tennessee Eastman (TE) process
The Application of Two Dimensions Wavelet in Image Edges Detection
Zhang Hongyan,Zhang Dengpan
Strategic Study of CAE 2003, Volume 5, Issue 4, Pages 61-64
Edges as the main characterization of image vision have been throught as the main content in obtaining image information. Wavelet transform has the capacity for detecting local signal mutation and detects information using multiscale character, so it is taken as the excellent tool to detect the edges of the image information. This paper analyzes the basic theory using two dimensions wavelet to detect image edges on the basis of wavelet transform and then designs the detecting algorithm of the multi-scale edge matching. On the basis of researching results,the application programme is made to analyse the true examples.
Keywords： wavelet transform multi-scale edges detection
A fast face detection algorithm using enhanced AdaBoostbased on walsh features
Guo Zhibo,Yang Jingyu,Liu Huajun,Yan Yunyang
Strategic Study of CAE 2008, Volume 10, Issue 7, Pages 125-131
A fast face detection algorithm using enhanced AdaBoost based on Walsh features is proposed in this paper, and its training process is fast and works well under fewer non-face training samples.Firstly,the utility of Walsh features, instead of Harr-Like features can reduce the redundancy among features largely. Then, an enhanced double threshold AdaBoost algorithm is developed, where double threshold makes training process faster ; and in the process of training cascaded detector, the next classifier can be guided by the former classifier,which enhances the performance of the cascaded detector ;moreover,the adjustment to the threshold of each classifier can separate the training result of face and on-face as far as possible. Finally, the trained detector is tested on MIT + CMU test set, and experimental results show that its training speed, precision and detection time exceeds the corresponding method.
Keywords： Walsh features enhanced AdaBoost cascaded detector face detection
AED-Net: An Abnormal Event Detection Network Article
Tian Wang, Zichen Miao, Yuxin Chen, Yi Zhou, Guangcun Shan, Hichem Snoussi
Engineering 2019, Volume 5, Issue 5, Pages 930-939 doi: 10.1016/j.eng.2019.02.008
It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd's situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework's generalization capacity.
Keywords： Abnormal events detection Abnormal event detection network Principal component analysis network Kernel principal component analysis
Study on Defect Detection Technology for Bridge Deck Pavements
Guo Chengchao,Xu Pengfei and Cui Can
Strategic Study of CAE 2017, Volume 19, Issue 6, Pages 38-43 doi: 10.15302/J-SSCAE-2017.06.006
The bridge deck is the most vulnerable part of a bridge during its entire life cycle. The deck pavement is often adversely affected by improper construction methods, the aging of concrete, and corrosion of steel bars. This then has an adverse effect on the structure and overall performance of the bridge. Therefore, it is necessary to determined defects related to the bridge deck and to conduct relevant quality evaluations. This article describes the mechanism, application methods, and testing equipment of four mainstream nondestructive testing technologies used worldwide: ground penetrating radar, half-cell potential, impact echo, and infrared thermography. The use of one or more of these methods can accurately assess the deterioration of the bridge deck and make a rapid, nondestructive evaluation, which provides technical support for rapid detection and accurate evaluation of the deck situation, leading to reduced structural defects and an extended bridge life cycle.
Keywords： bridge deck pavement defect nondestructive testing
Title Author Date Type Operation
Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey
Roberto De Fazio, Nicola Ivan Giannoccaro, Miguel Carrasco, Ramiro Velazquez, Paolo Visconti,roberto.defazio@unisalento.it,ivan.giannoccaro@unisalento.it,miguel.carrasco@uai.cl,rvelazquez@up.edu.mx,paolo.visconti@unisalento.it
Journal Article
Characterization of the Gastric Mucosal Microbiota in Patients with Liver Cirrhosis and Its Associations with Gastrointestinal Symptoms
Yanfei Chen, Jing Guo, Chunlei Chen, Ding Shi, Daiqiong Fang, Feng Ji, Lanjuan Li
Journal Article
Nondestructive Testing Method to Assess and Detect Road Performance
Guo Chengchao,Xu Pengfei and Zhong Yanhui
Journal Article
Generating Available Scheme for Extension Detecting Technology
Yu Yongquan ,Zhang Jiwen
Journal Article
Implementation of Clinical Diagnostic Criteria and Universal Symptom Survey Contributed to Lower Magnitude and Faster Resolution of the COVID-19 Epidemic in Wuhan
Yongyue Wei, Liangmin Wei, Yue Jiang, Sipeng Shen, Yang Zhao, Yuantao Hao, Zhicheng Du, Jinling Tang, Zhijie Zhang, Qingwu Jiang, Liming Li, Feng Chen, Hongbing Shen
Journal Article
Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain
Qianyun Zhang,Kaveh Barri,Saeed K. Babanajad,Amir H. Alavi
Journal Article
Research on control strategy of real-time testing system for hydraulic cylinder
Gong Jin,Tan Qing,Zhao Yuming,Heng Baoli
Journal Article
Failure Analysis, Safety Assessment, and Nondestructive Testing for Mechanical and Electrical Equipment
Zhong Qunpeng,Wu Huaisheng,Zhang Zheng,Tian Yongjiang
Journal Article
Generative adversarial network based novelty detection usingminimized reconstruction error
Huan-gang WANG, Xin LI, Tao ZHANG
Journal Article
The Application of Two Dimensions Wavelet in Image Edges Detection
Zhang Hongyan,Zhang Dengpan
Journal Article
A fast face detection algorithm using enhanced AdaBoostbased on walsh features
Guo Zhibo,Yang Jingyu,Liu Huajun,Yan Yunyang
Journal Article
AED-Net: An Abnormal Event Detection Network
Tian Wang, Zichen Miao, Yuxin Chen, Yi Zhou, Guangcun Shan, Hichem Snoussi
Journal Article
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