• Bulletin of the Korean Chemical Society
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• v.5 no.2
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• pp.82-86
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• 1984

The anomalous behaviors of supercooled water are explained by using a two-solid-like structure model in which an equilibrium is assumed between open structures and closed structures. Besides these structures, small fraction of monomer exists in liquid water. The anomalies of liquid water are classified into two groups: structural and energetic. The structural anomalies appear in enlarged fashions in a supercooled state where the free volume is small.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.527-534
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• 2019

Although ground investigation had carried out prior to the construction, many problems have arisen during the civil-engineering works because of the presence of the unexpected underground utility lines or anomalies. In this study, a new geophysical exploration method was developed to solve those problems by improving and supplementing the existing methods. This new method was based on the difference of electrical resistance values between anomalies and surrounding ground medium. A theoretical expression was suggested to define the characteristics of the anomalies such as location, size and direction, by applying the electric field analysis. An inverse analysis algorithm was also developed to solve the theoretical expression using the measured electrical resistance values which were generated by the voltage flowing the subsurface medium. To verify the developed method, field applications were conducted at the sites under construction or planned. From the results of the field tests, it was found that not only the new method was more predictive than the existing methods, but its results were good agreed with the measured ones. Therefore, it is expected that application of the new exploration method reduces the unexpected accidents caused by the underground uncertainties during the underground construction works.

• Clinical and Experimental Pediatrics
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• v.53 no.2
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• pp.158-162
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• 2010

Purpose : Turner syndrome (TS) is a disorder in which various anomalies can be accompanied, especially cardiovascular, renal, thyroid and auditory problems. The aim of this study is to identify the incidence of these disorders in patients with TS according to karyotype. Methods : We reviewed medical records of 90 patients with TS diagnosed by chromosomal analysis in 4 hospitals from Jan 1998 to Dec 2007. We evaluated these cases by prepared protocol of 4 medical problems.Results : The distribution of karyotype was 45,X (47.8%), mosaic pattern (34.4%) and structural aberration group (17.8%). Renal anomalies, cardiovascular anomalies, thyroid disorders and auditory problems are accompanied in 4.4%, 10.0 %, 11.1% and 5.6%, respectively. 45,X group had renal anomalies (7.0%), cardiovascular anomalies (18.6%), thyroid disorders (9.3%) and auditory problems (11.6%). Mosaic group had renal anomalies (3.2%), thyroid disorders (12.9%), no cardiovascular anomalies and auditory problems. Structural aberration group had cardiovascular anomalies (6.3%), thyroid disorders (12.5%) and no other 2 problems. Patients with 45,X group had a significant higher incidence of cardiovascular anomalies (P =0.025). Conclusion : Our results indicate that there are differences clinically according to karyotype of TS, especially in incidence of cardiovascular anomalies.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.177-193
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• 2018

The business process management system that can integrate and manage a medical institution's processes has been increased importance to adapt to constantly changing medical environments and share information with various external medical institutions. The business process management system is an automated process tool that converts the graphic-based BPMN process model into a WS-BPEL, the execution language of the web service environment, and executes it through the process engine. However, the BPMN process model can be difficult to convert into WS-BPEL due to several ambiguities and structural inconsistencies. The process model may also contain structural anomalies that can lead to execution errors during process execution. In this paper, we present business process model formalization and structural anomaly verification techniques for facilitating integrated process management in medical institutions. Through the case study based on the IHE profile, we presented a formalized BPMN process model and verify the structural anomalies. We show the superiority of the proposed technique through comparative experiments with other related works.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.181-206
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• 2022

This paper presents experimental modal analysis and static load testing results to validate the accuracy of dynamic parameters-based damage locating indices in RC structures. The study investigates the accuracy of different dynamic-based damage locating indices compared to observed crack patterns from static load tests and how different damage levels and scenarios impact them. The damage locating indices based on mode shape curvature and mode shape fourth derivate in their original forms were found to show anomalies along the beam length and at the supports. The modified forms of these indices show higher sensitivity in locating single and multi-cracks at different damage scenarios. The proposed stiffness reduction index shows good sensitivity in detecting single and multi-cracks. The proposed anomalies elimination procedure helps to remove the anomalies along the beam length. Also, the adoption of the proposed weighting method averaging procedure and normalization procedure help to draw the overall crack pattern based on the adopted set of modes.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Childhood Kidney Diseases
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• v.19 no.2
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• pp.154-158
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• 2015

Purpose: This study aimed to evaluate the status of renal function and the presence of urinary abnormalities in early adult patients with Turner syndrome (TS). Methods: Sixty-three girls with TS, who are attending pediatric endocrine clinics in Busan Paik Hosp., were studied. Urine and blood chemistry tests were performed in every visiting times. Renal ultrasonography was performed in all patients at the initial diagnosis, and intravenous pyelography, DMSA renal scan and renal CT were also performed, if necessary. Results: Of the 63 patients, the karyotype showed 45,X in 32 (50.8%), mosaicism in 22 (34.9%) and structural aberration in 9 (14.3%). The renal function at the latest visit was shown as normal in all patients. Nephrotic syndrome had developed in one patient. Hematuria was observed in seven patients. Renal anomalies were observed in 20 of the 63 TS (31.7%). Of the 32 TS patients with 45,X karyotype, 13 (40.6%) had renal anomalies, while these were found in 7 (22.6%) of 31 TS patients with mosaicism/structural aberration. But there was no significant statistical difference between two karyotype groups. Conclusion: Based on this study, most of the patients with TS do not have any significant problems related to renal function until early adulthood, regardless of renal malformation or hematuria.