• Geomechanics and Engineering
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• v.19 no.1
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• pp.93-104
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• 2019

Anomalies and/or fractured grounds not detected by the surface geophysical and geological survey performed during design stage may cause significant problems during tunnel excavation. Many studies on prediction methods of the ground condition ahead of the tunnel face have been conducted and applied in tunneling construction sites, such as tunnel seismic profiling and probe drilling. However, most such applications have focused on the drill and blast tunneling method. Few studies have been conducted for mechanized tunneling because of the limitation in the available space to perform prediction tests. This study aims to predict the ground condition ahead of the tunnel face in TBM tunneling by using an electrical resistivity tomography survey. It compared the characteristics of each electrode array and performed an investigation on in-situ tunnel boring machine TBM construction site environments. Numerical simulations for each electrode array were performed, to determine the proper electrode array to predict anomalies ahead of the tunnel face. The results showed that the modified dipole-dipole array is, compared to other arrays, the best for predicting the location and condition of an anomaly. As the borehole becomes longer, the measured data increase accordingly. Therefore, longer boreholes allow a more accurate prediction of the location and status of anomalies and complex grounds.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1C
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• pp.11-20
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• 2009

A new alternative method based on HWAW method to detect underground anomaly was introduced. The location of underground anomaly can be estimated by using 2-dimensional image of phase velocity image with position and wavelength based on distortion phenomena of surface wave due to underground anomaly. Overall procedure of proposed method such as field testing, signal processing and interpretation of the result was introduced. Numerical verification study was performed by using various ground models containing underground anomaly. According to the condition of anomaly, the propagation and reflection characteristics of surface wave were different and this could be more easily shown in the image of phase velocity. Some rules of distortion phenomena were found and these become clues for estimating underground anomaly in interpreting real field data. Field verification tests were performed with conventional geophysical methods such as DC resistivity method and GPR. Though field condition is not homogeneous like numerical models, similar distortion phenomena were found in the testing results and estimated location of underground anomaly was agreed well with the results of another geophysical methods.

• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.29-42
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• 1995

A typhoon center location and its intensity from the 54.96GMz channel of Microwave Sounding Unit(MSU) on board the NOAA satellite is analyzed. NOAA satellite MSU channel 3 data may delineate the development and dissipation of the upper tropospheric warm core associated with a typhoon. The typhoon warm core is related to microwave imagery of 250hPa temperature field (54.96GMz). The typhoon center intensity, surface center pressure and maximum wind speed at the eye well, correlate to horozontal Laplacian of an upper tropospheric temperature field. The typhoon center is found from the analysis of 250hPa temperature field. The excellent correlation is found between the horizontal Laplacian of an tropospheric temperature field and surface maximum wind speed, another correlation is found between the warm temperature anomaly and surface pressure anomaly.

• Journal of Ocean Engineering and Technology
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• v.9 no.2
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• pp.167-174
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• 1995

The anomlous signal, anomaly, recorded by a sub-bottem profiler is analized for exploration of buried objects in the ocean, This anomaly is known as a signal diffracted from the edge of the buried object. Signals obtained from model that and numerical simulation are analized for investigating characteristics of the diffracted signal. From this study a diffracted signal and a non-diffracted signal can be identified, and the location of the object can be obtained. In order to identify an object in the seafloor the dimension of the object should be greater than the wave length used for exploration, and the acoustic impedance should be much greater than that of sediments. A 2-trace stacking of the signals can enhance the feature of strongly diffracted signals whereas it can diminish weak signals.

• Journal of Chest Surgery
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• v.23 no.1
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• pp.205-212
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• 1990

A 17 year old female patient with Ebstein`s anomaly received surgical treatment for WPW syndrome and AV nodal reentrant supraventricular tachycardia[SVT] Electrophysiologic study revealed that an anomalous pathway was located in the right posterolateral portion and antegrade dual AV nodal pathway responsible for AV nodal reentrant tachycardia. The patient was underwent surgery on February 18, 1987. Intraoperative mapping was used to define the location of accessory pathway. The accessory pathway was cryoablated through the epicardium. Simultaneously discrete cryoablation around the perinodal area was performed to prevent AV nodal reentrant SVT. The atrialized right ventricle of Ebstein`s anomaly was plicated with 11 pledget mattress sutures under the cardiopulmonary bypass. Two and half years after surgery, the patient has no evidence of WPW syndrome or supraventricular tachycardia.

• Korean Journal of Head & Neck Oncology
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• v.21 no.1
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• pp.53-56
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• 2005

The branchial anomaly is a lateral neck mass commonly seen by otolaryngologists. Depending on its anatomic location, branchial anomaly can be classified into first, second, third and fourth. The fourth branchial cleft anomaly is very rare entity and until now, only 35cases have been reported worldwide. It may present as neck cyst, recurrent neck abscess, thyroiditis. Combined with barium swallow esophagogram and computed tomography scan can aid in diagnosis of this rare disease entity. Complete excision of the entire epithelial tract combined with ipsilateral thyroid lobectomy remains the mainstay of treatment. Authors experienced a case of lateral neck mass which was anatomically presumed to be the fourth branchial cleft cyst. We report this case with the related literature.

• Smart Structures and Systems
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• v.30 no.6
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• Geosystem Engineering
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• v.21 no.5
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• pp.262-272
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• 2018

The U-statistic method is one of the most important structural methods to separate the anomaly from background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, 3D U-statistic method has been applied for the first time through the three-dimensional (3D) modeling of an ore deposit. In order to achieve this purpose, 3D U-statistic is applied on the data (Fe grade) resulted from the drilling network in Baghak mine, central part of the Sangan iron mines (in Khorassan Razavi Province, Iran). Afterward, results from applying 3D U-statistic method are used for 3D modeling of the iron mineralization. Results show that the anomalous values are well separated from background so that the determined samples as anomalous are not dispersed and according to their positioning, denser areas of anomalous samples could be considered as anomaly areas. And also, final results (3D model of iron mineralization) show that output model using this method is compatible with designed model for mining operation. Moreover, seen that U-statistic method in addition for separating anomaly from background, could be very efficient for the 3D modeling of different ore type.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2298-2315
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• 2024

This paper introduces a method aimed at diagnosing the presence or absence of lesions by detecting anomalies in Chest X-ray images. The proposed approach is based on the PatchCore anomaly detection method, which extracts a feature vector containing location information of an image patch from normal image data and calculates the anomaly distance from the normal vector. However, applying PatchCore directly to medical image processing presents challenges due to the possibility of diseases occurring only in specific organs and the presence of image noise unrelated to lesions. In this study, we present an image alignment method that utilizes affine transformation parameter prediction to standardize already captured X-ray images into a specific composition. Additionally, we introduce a region-specific abnormality detection method that requires affine-transformed chest X-ray images. Furthermore, we propose a method to enhance application efficiency and performance through feature map hard masking. The experimental results demonstrate that our proposed approach achieved a maximum AUROC (Area Under the Receiver Operating Characteristic) of 0.774. Compared to a previous study conducted on the same dataset, our method shows a 6.9% higher performance and improved accuracy.