• Smart Structures and Systems
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• v.29 no.3
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• pp.421-431
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• 2022

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the sub-blocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

• 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.

• Journal of Korean Dental Science
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• v.3 no.1
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• pp.17-24
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• 2010

Objective : To investigate the prevalence of dental anomalies and to determine the associations between dental anomalies in permanent teeth Materials and methods : The samples were 1,240 patients (760 females and 480 males, mean age=15.1 years) who visited the Samsung Medical Center. Dental anomalies were diagnosed using pre-treatment dental casts, radiographs, clinical examinations, and medical/dental histories. Prevalence and association were investigated according to gender and sidedness. The Chi-square test was performed for statistical analysis. Results : The most common missing tooth was the lower lateral incisor, followed by the lower and upper second premolars. This particular dental anomaly is characteristic of the East Asian population (prevalence of congenital missing tooth=12.3%). The upper anterior area was the most frequently affected area (prevalence of supernumerary tooth was 1.5%). The presence of a supernumerary tooth was more prevalent in males than in females (p<.05, odds ratio=3.2). The most frequently affected tooth was the upper canine (prevalence of impacted tooth=4.3%). Unilateral impaction of the upper canine occurred significantly more often compared to bilateral impaction (p<.001). The prevalence of peg lateralis was 2.7%. The presence of congenital missing tooth was closely associated with peg lateralis (p<.01). If children aged 7~8 years have peg lateralis, the rest of the teeth should be checked for congenital absences. Conclusion : The early detection of dental anomalies and understanding of their associations help clinicians determine the appropriate treatment timing and methods of dealing with these anomalies.

• Toxicological Research
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• v.17
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• pp.11-16
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• 2001

The development of the central nervous system is a continuous process during the embryonic and fetal periods. For a better understanding of congenital anomalies of central nervous system, three major events of normal development, i.e., neurulation (3 to 4 weeks), brain vesicle formation (4 to 7 weeks) and mantle formation (over 8 weeks) should be kept in mind. The first category of anomalies is neural tube defect. Neural tube defects encompass all the anomalies arise in completion of neurulation. The second category of central nervous system anomalies is disorders of brain vesicle formation. This is anomaly that applies for "the face predicts the brain". Holoprosencephaly covers a spectrum of anomalies of intracranial and midfacial development which result from incomplete development and septation of midline structures within the forebrain or prosencephalon. The last category of central nervous system malformation is disorders involving the process of mantle formation. In the human, neurons are generated in two bursts, the first from 8 to 10 weeks and next from 12 to 14 weeks. By 16 weeks, most of the neurons have been generated and have started their migration into the cortex. Mechanism of migration disorders are multifactorial. Abnormal migration into the cortex, abnormal neurons, faulty neural growth within the cortex, unstable pial-glial border, degeneration of neurons, neural death by exogenous factors are some of the proposed mechanism. Agyria-pachygyria are characterized by a four-layerd cortex. Polymicrogyria is gyri that are too numerous and too small, and is morphologically heterogeneous. Cortical dysplasia is characterized by the presence Q[ abnormal neurons and glia arranged abnormally in focal areas of the cerebral cortex. Neuroglial malformative lesions associated with medically intractable epilepsy are hamartia or hamartoma, focal cortical dysplasia and microdysgenesis.ysgenesis.

• Advances in pediatric surgery
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• v.8 no.2
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• pp.107-112
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• 2002

Branchial anomaly is a frequently occurring congenital abnormality in childhood. It is important for the pediatric surgeon alike to be familiar with the embryology and differentiation of head and neck structure to accurately diagnose and treat these lesions. Eighty-five patients with branchial anomaly treated at Hanyang University Hospital between 1980 and 2001 were reviewed to determine relative frequency, clinical classification and appropriate treatment. The male to female ratio of branchial anomaly was 1.2:1. The most commonly presenting age was before 1 year (32%) and the age group between 1 and 3 year (22%) followed it. According to the classification of branchial anomalies, 73 of 85 cases were second branchial anomaly, 9 had the first type and 3 did fourth type. One patient showed combined anomalies of the first and the second type. Infection sign were seen in 70% of patients at the time of the first visit to our hospital and also patients' symptoms were frequently related with the infection. Forty-one cases (48%) were fistula, 21 (25%) were cysts, 21 (25%) were sinuses, and two were only cartilage remnants. The most common type of the branchial anomalies is the second branchial fistula and the most common symptoms of the anomalies are related with infection. Initial proper diagnosis and anatomical classification of the anomalies are very important in managing the lesions. The efforts to find the exact anatomical location of the fistula or sinus tract are necessary because total excision of the lesions including those tracts is the only way to prevent recurrence.

• Archives of Craniofacial Surgery
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• v.22 no.3
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• pp.141-147
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• 2021

Background: Arteriovenous malformation (AVM) which is a high-blood-flow lesion with connections between arteries and veins without an intervening capillary bed, is difficult to manage. The ear is the second most common site of extracranial AVM. However, studies regarding the management of this condition remain lacking. The purpose of this study was to share managing experiences in our center and to investigate the treatment effect through a retrospective analysis of cases. Methods: Among 265 patients with AVM treated in our vascular anomalies center between January 2008 and January 2021, 10 patients with auricular AVM were included in the study to investigate the lesion distribution, clinical stage, and treatment methods by performing a retrospective evaluation. Results: Among 10 patients, five patients had AVMs distributed in the upper half of the ear, one patient in the lower half of the ear, and four patients in whole ear, respectively. Seven patients had Schobinger stage II, and three had stage III. One patient received surgical treatment only, four patients received sclerotherapy only, and five patients received both surgical treatment and sclerotherapy. The posttreatment status was checked as controlled in two patients, improved in seven patients, persistent in one patient. There were no worsening patients. Conclusion: Auricular AVM is a disease that is difficult to manage by one specific department, thus requiring a collaborative management effort from multidisciplinary team.

• 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.

• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.157-168
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• 2002

Improved procedures were implemented in the production of the lithospheric magnetic anomaly map from Magsat satellite magnetometer data of East Asia between $90^{\circ}E-150^{\circ}E$ and $10^{\circ}S-50^{\circ}N$. Procedures included more effective selection of the do·it and dawn tracks, ring current correction, and separation of core field and external field effects. External field reductions included an ionospheric correction and pass-by-pass correlation analysis. Track-line noise effects were reduced by spectral reconstruction of the dusk and dawn data sets. The total field magnetic anomalies were differentially-reduced-to-the-pole to minimize distortion s between satellite magnetic anomalies and their geological sources caused by corefield variations over the study area. Aeromagnetic anomalies were correlated with Magsat magnetic anomalies at the satellite altitude to test the lithospheric veracity of anomalies in these two data sets. The aeromagnetic anomalies were low-pass filtered to eliminate high frequency components that may not be shown at the satellite altitude. Although the two maps have a low CC of 0.243, there are many features that are directly correlated (peak-to-peak and trough-to-trough). The low CC between the two maps was generated by the combination of directly- and inversely-correlative anomaly features between them. It is very difficult to discriminate directly, inversely, and nully correlative features in these two anomaly maps because features are complicatedly correlated due to the depth and superposition of the anomaly sources. In general, the lithospheric magnetic components were recovered successfully from satellite magnetometer observations and correlated well with aeromagnetic anomalies in the study area.

• Clinical and Experimental Pediatrics
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• v.58 no.7
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• pp.256-262
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• 2015

Purpose: Kabuki syndrome is a multiple congenital malformation syndrome, with characteristic facial features, mental retardation, and skeletal and congenital heart anomalies. However, the cardiac anomalies are not well described in the Korean population. We analyzed the cardiac anomalies and clinical features of Kabuki syndrome in a single tertiary center. Methods: A retrospective analysis was conducted for a total of 13 patients with Kabuki syndrome. Results: The median age at diagnosis of was 5.9 years (range, 9 days to 11 years and 8 months). All patients showed the characteristic facial dysmorphisms and congenital anomalies in multiple organs, and the diagnosis was delayed by 5.9 years (range, 9 days to 11 years and 5 months) after the first visit. Noncardiac anomalies were found in 84% of patients, and congenital heart diseases were found in 9 patients (69%). All 9 patients exhibited left-side heart anomalies, including hypoplastic left heart syndrome in 3, coarctation of the aorta in 4, aortic valve stenosis in 1, and mitral valve stenosis in 1. None had right-side heart disease or isolated septal defects. Genetic testing in 10 patients revealed 9 novel MLL2 mutations. All 11 patients who were available for follow-up exhibited developmental delays during the median 4 years (range, 9 days to 11 years 11 months) of follow-up. The leading cause of death was hypoplastic left heart syndrome. Conclusion: Pediatric cardiologist should recognize Kabuki syndrome and the high prevalence of left heart anomalies with Kabuki syndrome. Genetic testing can be helpful for early diagnosis and counseling.