• Journal of Chest Surgery
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• v.42 no.1
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• pp.87-91
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• 2009

The surgical management of complete transposition of the great arteries, ventricular septal defect, and pulmonary stenosis still remain a significant challenge. The Rastelli (REV procedure) remains the most widely applied procedure for surgical repair of these lesions. Although the Rastelli procedure can be performed with good early results, the intermediate- and long-term results have been less than satisfactory because of deterioration of the hemodynamic performance of the LVOT or RVOT. We performed a modified Nikaidoh procedure as an alternative surgical procedure in a 19-month-old boy weighing 10.4 kg with this anomaly. Aortic translocation with biventricular outflow tract reconstruction resulted in a more "normal" anatomic repair and postoperative echocardiography showed straight, direct, and unobstructed ventricular outflow.

• Management & Information Systems Review
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• v.37 no.1
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• pp.41-53
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• 2018

This study validates the trading rules based market anomalies and technical analysis in the Korean stock market. For the analysis, we built decile portfolios on the basis of corporate characteristics factors that clearly demonstrate specific patterns of stock returns including the firm size, book-to-market equity, and accruals. This portfolio was used to develop a portfolio based on the moving average trading strategy which was used for popular technical analysis tools, and then that was evaluated using the Sharpe ratio. We also created a zero-cost portfolio to identify the profitability and success rate of the moving average trading strategy. We lastly sought to ensure a more robust evaluation by calculating the Sortino ratio of the portfolio based on the moving average trading strategy with various lags. Key findings from this validation are as follows. First, a smaller firm size, a higher book-to-market equity, and lower accruals led to larger average returns. Second, the risk-adjusted performance of the moving average trading strategy was the highest in terms of the firm size, followed by book-to-market equity and accruals. Third, the returns of the zero-cost portfolios all had a positive value, with its overall success rate hovering over 68.8%, demonstrating the successfulness of the moving average trading strategy. Fourth, various evaluations revealed the economic usefulness of our trading strategy that used market anomalies and technical analysis.

• Tuberculosis and Respiratory Diseases
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• v.65 no.2
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• pp.131-136
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• 2008

Cholethorax (bilious pleural effusion) is an extravasation of bile into the thoracic cavity via a pleurobiliary fistula (and also a bronchobiliary fistula). It is an extremely rare complication of thoraco-abdominal injuries. It can be caused by congenital anomaly and also by hepatobiliary trauma, severe infection or iatrogenic procedures. The definitive diagnosis is made with aspiration of bilious fluid from the pleural space during thoracentesis, by finding a fistulous tract during endoscopic retrograde cholangiopancreatography (ERCP) or cholagioscopy, or with finding an abnormal pleural accumulation of radioisotope during hepatobiliary nuclear imaging. Its symptoms include coughing, fever, dyspnea and pleuritc chest pain. Herein we report on a case of cholethorax following performance of percutaneous transhepatic cholangioscopy (PTCS) to remove incidentally discovered common bile duct (CBD) stones.

• Clinical and Experimental Reproductive Medicine
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• v.7 no.1_2
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• pp.11-20
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• 1980

Hysterosalpingograms (H.S.G.) have been done for several decades to determine causative factors in female infertility. However, the H.S.G. only reverals uterine cavity and tubal patency or inpatency. The author prefers to find more details in regard to the status and condition of the female reproductive organs and their surrounding tissue as they pertain to infertility. H.S.G. in combination with laparoscopic examination reveals the following results. Preparation and method of performance of H.S.G. during laparoscopy in a healthy reproductive age women are as follows. When laparoscopy is not contraindicated, NPO is ordered with routine bowel preparation. Analgesics administered by injection prior to procedure are valium 10mgs and pethidine 50-100mgs. The radiographic procedure is the same as for any HSG technique. During laparoscopy a solution of 3 to 10 ccs. of 60% hypaque sodium is used. Fluroscopic scout films are obtained A-P and oblique views as well as a delayed check film. 1. Age distribution of primary and secondary infertility in this studies involving tubal factors was as follows: 20-29 age group showed 46% incidence and in the 30-39 age group, 50% incidence. Duration of infertility in this study group was the following: 1-2 years showed 26.7%, 3-5 years 53.8%, and 6-9 years 13.3%. 2. Indications of laparoscopic examination were as follows: Secondary infertility in 35% of the cases, obscure tubal occlusion on previous H.S.G. in 25%, unknown origin in 11.7%, and the remaining cases included pelvic pain, small masses, dysmenorrhea, and uterine anomaly. The laparoscopic examination showed clearly the reproductive organs and the surrounding tissues in the pelvic cavity. The abnormal tubal findings there revealed were tuberculous salpingitis and hydrosalpinx in 10% each, endometriosis and peritubabl adhesions in 6.7% each, biconuate uterus in 3.3%. The remaining 58.3% of the cases showed normal findings. Laparoscopic observation for possible myoma nodules, streak ovary, and peritubal adhesions was also done at this time. 3. Comparative tubal findings in combined H.S.G. and laparoscopic examination revealed the following. Bilateral tubal occlusion was present in 14% (7cases) on laparoscopic examination but on H.S.G. 38% (19 cases) were noted. However, tubal occlusion and peritubal adhesions were found in 26% (13 cases) upon laparoscopy and only 8% (4 cases) on H.S.G. examination alone. Normal pelvic findings were present in 60% (27 cases).

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.