• Clinical and Experimental Reproductive Medicine
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• v.48 no.1
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• pp.91-94
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• 2021

A 28-year-old woman presented with a 1-year history of severe progressive dysmenorrhea following suction evacuation and tubal ligation. Sonography showed a bicornuate uterus with hematometra in the left horn. Hysteroscopy ruled out a diagnosis of a congenital Müllerian anomaly, as both ostia appeared normal. Under laparoscopy, a mass was seen on the left fundal region near the insertion of the round ligament, and needle aspiration of a chocolate-colored fluid confirmed the diagnosis of an adenomyotic cyst. The cyst was excised. The patient recovered well and has been symptom-free since surgery. Adenomyotic cyst is a rare entity in young women and must be differentiated from obstructive Müllerian anomaly. Laparoscopy is the preferred minimally invasive modality for managing this rare disorder.

• Korean Journal of Head & Neck Oncology
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• v.33 no.1
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• pp.31-34
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• 2017

First branchial cleft anomaly is a very rare disease and exhibits various clinical presentations. Therefore, the diagnosis of first branchial cleft anomaly may be difficult; the condition is often misdiagnosed and mismanaged. Accurate diagnosis is very important, because if not diagnosed correctly, patients with first branchial cleft anomaly would be treated with local incision and drainage repeatedly. We report two cases of first branchial cleft anomaly. The first patient visited for recurrent swell and discharge in the infra-auricular area with a history of previous incision and drainage. The other patient showed a cystic mass in the infra-auricular area and all of them were misdiagnosed initially by their treating specialists elsewhere. The objective of this study is to share our experiences of first branchial cleft anomaly, and emphasize its various clinical patterns and the significance of accurate diagnosis.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.569-581
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• 2023

This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications.

• Smart Structures and Systems
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• v.11 no.1
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• pp.1-17
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• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• Journal of Genetic Medicine
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• v.5 no.2
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• pp.94-99
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• 2008

Congenital malformation is observed in about 2-5% of newborns and is a leading cause of infant mortality. The prognosis of malformation is dictated mainly by proper treatment followed by correct diagnosis at an early age. In practice, etiological consideration and classification of a malformation is critical for diagnosis. Malformations can be classified as belonged to minor or major anomaly. It is clinically important to clarify the pathogenesis of the anomalies among malformation, deformation, disruption, and dysruption. Genetic counseling aids this process by helping patients or family members understand and the nature of the malformation and risk assessment.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1441-1450
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• 2021

Anomalous origin of the coronary artery from the pulmonary artery is a rare and potentially fatal congenital heart defect. Up to 90% of infants with an anomaly involving the left coronary artery die within the first year of life if left untreated. Patients who survive beyond infancy are at risk of sudden cardiac death. Cardiac CT and MRI are increasingly being used for the accurate diagnosis of this anomaly for prompt surgical restoration of the dual coronary artery system. Moreover, life-long imaging surveillance after surgery is necessary for these patients. In this pictorial review, multimodal cardiac imaging findings of this rare and potentially fatal coronary artery anomaly are comprehensively discussed, and representative images are provided to facilitate the understanding of this anomaly.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1547-1556
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• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• 대한두경부종양학회:학술대회논문집
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• 2008.11a
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• pp.239-239
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• 2008