• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.18 no.6
• /
• pp.497-505
• /
• 1985

We studied the annual and interannual fluctuations of sea surface temperature (SST) for 30 years ($1941{\sim}1970$) at 9 coastal stations in the Tsushima Current and the Kuroshio regions by means of harmonic analysis, correlation analysis, and spectral analysis. The fluctuations of annual mean and amplitude are 0.3 to $0.7^{\circ}C$, and those of annual phase are 3 to 4 days. The SST anomalies are about $1^{\circ}C$, and they are relatively large in summer and winter than in spring and fall. The SST anomalies in the Tsushima Current and the Kuroshio regions are related with each other. The predominant periods of SST anomalies differ slightly from station to station. The quasi-biennial (26 months) and pole tide (14 months) oscillations are found in the spectra of SST anomalies.

• Economic and Environmental Geology
• /
• v.38 no.3 s.172
• /
• pp.299-304
• /
• 2005

Geoid undulation and gravity anomaly were calculated from GRACE satellite data on the eastern Asia including Korean peninsula. Geoid undulation varies from -60m in the China to 60m toward the Pacific Ocean across the Korean Peninsula. Calculated gravity anomalies are in the range of -60 and 60 mgal except the subduction zone showing -100 mgal. High positive values are observed at Mt. Baekdu, Kaema highland and Taebaek mountains, and low values at Ulleung, Japan and Yamato basins in the East sea. We removed regional components below the spherical harmonic degree of 10 from gravity anomaly to get the residual anomaly for crust components. Residual gravity anomaly shows high anomalies at the northern mountainous area and Kyungsang basin in the Korean Peninsula. And low anomalies appears at the western Korea bay basin, Kunsan basin, Cheju basin, and Ulleung basin in the marine. Anomalies separated by the spherical harmonic degree as well as the residual anomalies are useful for the study of large crustal structure about geologic scale and depth distribution and for the survey of natural resources.

• Advances in pediatric surgery
• /
• v.3 no.1
• /
• pp.15-23
• /
• 1997

During a 6-year period, from January 1990 to December 1995, 101 neonates with congenital anomalies were admitted to the division of Pediatric Surgery of Youngdong Severance Hospital. All of them had prenatal screening more than once with ultrasound. Fifty eight of them had prenatally detectable anomalies by ultrasonography. However abnormalities were prenatally detected in 24 neonates(41%). The detection rate was 70% in patientws who had the prenatal screening at our hospital, whereas, the rate was 24% when it was performed at other medical facilities. Duodenal and jejuno-ileal atresia showed the highest detection rate(86%) followed by abdominal mass. Esophageal atresia was suggested by maternal polyhydramnios in 3 patients (25%). Only one patient with diaphragmatic hernia(1.75%) was prenatally detected and none with gastroschisis. The mean interval from birth to operation was 32 hours in the prenatally detected patients and 50 hours in the non detected. The complication rate and the mortality after emergency operation were 20% and 7% in the detected group, and 58% and 23% in the nondetected, respectively. The average period of the hospitalization was 20 days in the detected group and 39 days in the nondetected. We conclude that the prenatal detection of anomalies is necessary to ensure adequate care for the mothers and the babies with congenital anomalies. This includes early transfer, timing of optimal delivery and operation.

• Clinical and Experimental Reproductive Medicine
• /
• v.13 no.2
• /
• pp.137-144
• /
• 1986

A reveiw of 85 patients with uterine anomalies was made in respect to the incidence, chief complaints, the reason of infertility, fetal wastage rate, pregnancy complications, fetal presentations and obstetric outcome after metroplasty from 1980 to 1985. The results were summarized as follows: 1. Incidence of uterine anomaly was 0.18% among all outpatients (85/48,240). 2. Of the 85 patients, there were 36 with bicornuate deformities (42.3%), 21 septate (24.7%), 18 uterus didelphys (21.2%), 8 arcuate (9.4%) and 2 patients with unicornuate anomalies (2.4%). 3. Uterine anomalies were diagnosed by hysterosalpingogram (54.1%), pelvic examination (14.2%) and other operative procedures. 4. Chief complaints were primary infertility (41.2%), secondary infertility (15.3%), repeated pregnancy loss (12.9%), antenatal care (11.8%) and menstural disturbance (10.6%), etc. 5. Twenty-nine patients with uterine anomalies had primary infertility. The cause of infertility was proved nonuterine in 26 cases and remained unknown in 3 cases. 6. The obstetric outcome of 104 pregnancies was spontaneous abortion in 51.0%, premature delivery in 11.50/0 and fetal loss in 57.7%. 7. Complications of 41 present pregnancies were threatened abortion (22%), premature rupture of membrane (12%) and premature labor (10%), etc. The frequency of abnormal presentation was 35.3% and 64.7% of deliveries was made by Cesarian section. 8. Metroplasty was performed in 13 patients who didn't have a baby because of repeated miscarriage and unknown cause of infertility. Subsequently 8 patients had 9 successful pregnancies: 6 patients had 7 healthy babies and 2 patients are now in pregnancy without any complications.

• Journal of Veterinary Science
• /
• v.22 no.4
• /
• pp.52.1-52.8
• /
• 2021

Background: The normal vertebral anatomy of Korean raccoon dogs and their variants require research attention as a prerequisite for identifying pathologies and anomalies. Objectives: This retrospective study aimed at describing the vertebral formula and congenital vertebral anomalies in Korean raccoon dogs (Nyctereutes procyonoides koreensis). Methods: Radiographs of 82 raccoon dogs (42 males, 40 females) acquired from May 2013 to June 2020 in the Gangwon Wildlife Medical Rescue Center were reviewed to evaluate the cervical, thoracic, and lumbar vertebrae of the spine. Results: Normal morphology of all vertebrae was observed in 50 of the 82 raccoon dogs, and the vertebral formula was cervical 7, thoracic 13, and lumbar 7. Congenital vertebral anomalies were found in 32 raccoon dogs: transitional vertebrae (TV) in 31 and block vertebrae in 2. Two raccoon dogs had 2 types of vertebral anomalies: one had TV and block vertebra, and the other had 2 types of TV. Twenty-nine raccoon dogs had thoracolumbar TV (TTV) and 3 had lumbosacral TV. TTV was morphologically classified into 4 different types: unilateral extra-rib in 5 raccoon dogs, bilateral extra-ribs in 14, bilateral elongated transverse processes in 4, and an asymmetric mixed formation of extra-rib with elongated transverse process in 6. Conclusions: This study showed that TTV is common in Korean raccoon dogs, and that the vertebral formula is relatively diverse. The bilateral extra-ribs type TTV is the most common variant, which is almost similar to normal rib to be confused the radiographic evaluation.

• Archives of Plastic Surgery
• /
• v.48 no.6
• /
• pp.622-629
• /
• 2021

Background Venous malformations (VMs) are the most common type of vascular malformations. Intramuscular venous malformations (IMVMs) are lesions involving the muscles, excluding intramuscular hemangiomas. The purpose of this study was to compare clinical outcomes between patients with IMVMs who were treated with sclerotherapy and those who were treated with surgical excision. Methods Of 492 patients with VMs treated between July 2011 and August 2020 at a single medical center for vascular anomalies, 63 patients diagnosed with IMVM were retrospectively reviewed. Pain, movement limitations, swelling, and quality of life (QOL) were evaluated subjectively, while radiological outcomes were assessed by qualified radiologists at the center. Complication rates were also evaluated, and radiological and clinical examinations were used to determine which treatment group (sclerotherapy or surgical excision) exhibited greater improvement. Results Although there were no significant differences in pain (P=0.471), swelling (P=0.322), or the occurrence of complications (P=0.206) between the two treatment groups, the surgical treatment group exhibited significantly better outcomes with regard to movement limitations (P=0.010), QOL (P=0.013), and radiological outcomes (P=0.017). Moreover, both duplex ultrasonography and magnetic resonance imaging showed greater improvements in clinical outcomes in the surgical excision group than in the sclerotherapy group. Conclusions Although several studies have examined IMVM treatment methods, no clear guidelines for treatment selection have been developed. Based on the results of this study, surgical excision is strongly encouraged for the treatment of IMVMs.

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

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.2
• /
• pp.103-111
• /
• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of the Korean GEO-environmental Society
• /
• v.24 no.8
• /
• pp.13-20
• /
• 2023

Tunnelling through the geological anomalies has widely known to have many difficulties such as bottom heave, crack of lining, squeezing and so on. To stabilize the tunnel during the construction or after construction, various reinforcing methods have been introduced and applied such as micropiling at the bottom of tunnel to prevent the bottom heave. In this study, long-term behavior of tunnel in the presence of geological anomalies was predicted using numerical analyses. To this end, material properties for swelling rock model capable of representing the rock swelling behavior was obtained using matching process with measured data to validate the adopted model. After the model validation, simulations were performed to predict the long-term behavior of tunnel in the geological anomalies.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.3
• /
• pp.130-139
• /
• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.