• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of Chest Surgery
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• v.55 no.1
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• pp.49-54
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• 2022

Background: We compared the safety and effectiveness of robotic anatomical resection and video-assisted thoracoscopic surgery (VATS). Methods: A retrospective analysis was conducted of the records of 4,283 patients, in whom an attempt was made to perform minimally invasive anatomical resection for lung cancer at Seoul National University Hospital from January 2011 to July 2020. Of these patients, 138 underwent robotic surgery and 4,145 underwent VATS. Perioperative outcomes were compared after propensity score matching including age, sex, height, weight, pulmonary function, smoking status, performance status, comorbidities, type of resection, combined bronchoplasty/angioplasty, tumor size, clinical T/N category, histology, and neoadjuvant treatment. Results: In total, 137 well-balanced pairs were obtained. There were no cases of 30-day mortality in the entire cohort. Conversion to thoracotomy was required more frequently in the VATS group (VATS 6.6% vs. robotic 0.7%, p=0.008). The complete resection rate (VATS 97.8% vs. robotic 98.5%, p=1.000) and postoperative complication rate (VATS 17.5% vs. robotic 19.0%, p=0.874) were not significantly different between the 2 groups. The robotic group showed a slightly shorter hospital stay (VATS 5.8±3.9 days vs. robotic 5.0±3.6 days, p=0.052). N2 nodal upstaging (cN0/pN2) was more common in the robotic group than the VATS group, but without statistical significance (VATS 4% vs. robotic 12%, p=0.077). Conclusion: Robotic anatomical resection in lung cancer showed comparable early outcomes when compared to VATS. In particular, robotic resection presented a lower conversion-to-thoracotomy rate. Furthermore, a robotic approach might improve lymph node harvesting in the N2 station.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Membrane Journal
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• v.32 no.4
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• pp.253-263
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• 2022

This study suggests a guideline for designing unit process of wastewater reuse in terms of a maintenance of the process based on critical parameters to draw a high quality performance of RO unit. Defining the parameters was done by applying membrane integrity test (MIT) in pretreatment process utilizing lab-scale MF. SDI is utilized for judging whether permeate is suitable to RO unit. However, result said TOC concentration matching with particle count analysis is better for judging the permeate condition. When membrane test pressure (P_test) was measured to derive log removal value in PDT, virgin state of membrane fiber was used to measure dynamic contact angle utilizing surface tension of the membrane fiber. Actually, foulant affects to the state of membrane surface, and it decreases the P_test value along with time elapsed. Consequently, LRV_DIT is also affected by P_test value. Thus, sensitivity of direct integrity test descends with result of P_test value change, so P_test value should be considered not the virgin state of the membrane but its current state. Overall, this study focuses on defining design parameters suitable to MF pretreatment for RO process in wastewater reuse by assessing its impact. Therefore, utilities can acknowledge that the membrane surface condition must be considered when users conduct the direct integrity test so that P_test and other relative parameter used to calculate LRV_DIT are adequately measured.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1139-1147
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• 2023

The purpose of this paper is to present and evaluate the performance of a method for controlling the dose for optimal image acquisition while minimizing patient exposure by applying a small-sized Photomultiplier(SiPM) sensor inside a portable detector. Portable detectors have the advantage of being able to quickly access the patient's location for rapid diagnosis, but this mobility comes with the challenge of dose control. This paper presents a method to identify the dose that can have the DQE and optimal image quality of the detector through image evaluation based on IEC62220-1-1, an international standard for X-ray imaging devices, and to identify the optimal dose by matching the ADU of the image and the output of the SiPM Sensor. The Skull AP image was acquired by implementing the detector manufacturer's reference dose. The optimal dose was 342.8 µGy, and the optimal controlled dose was 148.3 µGy, which is 57 % of the manufacturer's reference dose. The Chest AP image was 81.9 µGy and the optimal controlled dose was 27.9 µGy, which is a high dose reduction effect of 66 %. In addition, the two images were analyzed by five radiologists and found to have no clinically significant difference in anatomical delineation.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.130-144
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• 2023

Recently, various positioning technologies are being researched based on signal-based positioning and image-based positioning to obtain accurate indoor location information. Among these, various studies are being conducted on image positioning technology that determines the location of a mobile terminal using images acquired through cameras and sensor data collected as needed. For video-based positioning, a method of determining indoor location is used by matching mobile terminal photos with virtual landmark images, and for this purpose, it is necessary to build indoor spatial information about various landmarks such as billboards, vending machines, and ATM machines. In order to construct indoor spatial information on various landmarks, a panoramic image in the form of a road view and accurate 3D survey results were obtained through c 13 buildings of the Electronics and Telecommunications Research Institute(ETRI). When comparing the 3D total station final result and the terrestrial lidar panoramic image coordinates, the coordinates and distance performance were obtained within about 0.10m, confirming that accurate landmark construction for use in indoor positioning was possible. By utilizing these terrestrial lidar achievements to perform 3D landmark modeling necessary for image positioning, it was possible to more quickly model landmark information that could not be constructed only through 3D modeling using existing as-built drawings.

• KDI Journal of Economic Policy
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• v.32 no.2
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• pp.55-92
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• 2010

In terms of labor market accomplishments, such as income, size of the company, and the matching quality between one's job and college major (specialization), a very large discrepancy is observed between the graduates from colleges located in Seoul and those outside Seoul. But, when the department average score of the Scholastic Aptitude Test (SAT) at the time of college entrance is controlled for, the discrepancy is found to be reduced to a considerable degree. In the case of wage gap, at least two third can be explained by the SAT score gap. The remaining wage gap seems to reflect the characteristics of workplace. In other words, graduates with high SAT scores enter colleges located in Seoul and thus tend to find better jobs leading to earning differences. This result that confirms the importance of aptitude test scores suggests that in the labor market, one of the major reasons behind a lower accomplishment of the graduate from local colleges is due to a lower competitiveness of local colleges in attracting the brightest students. But, this should not be viewed as only an internal problem of local colleges. This is because the growth of local economies tends to haul the advancement of local colleges in that area rather than being the other way around. The agglomeration effect in Seoul where headquarters of large corporations and financial institutions gather is the factor that has elevated the status of colleges located in Seoul since this provides highly preferred job choices of graduates. When the competitiveness of college is significantly influenced by exogenous factors, such as the vicinity to Seoul, the effort being made by colleges alone would not be enough to improve the situation. However, the central government, too, is not in the position to carry out countermeasure policies for such problems. The regional development strategy boosted through supportive policies for local colleges, such as financial support, is not based on the persuasive and empirical grounds. It is true that college education is universal and that the government''s intervention in assisting local colleges to secure basic conditions, such as tenure faculty and adequate facilities is necessary. However, the way of intervention should not be a support-only type. In order to improve the efficiency and effect of financial support, restructuring programs, including the merger and integration of insolvent colleges, should be underway prior to providing support. In addition, when the policy is focused on education recipients-local college students, and not on education providers-local colleges, the importance of regional gap in compulsory education (elementary and junior high schools) turns out to be much important as the gap between metropolitan area colleges and local colleges. Considering the educational gap before college entrance shown from the discrepancies of aptitude test scores among different regions, the imbalance between regions in terms of human resources is apparently derived from compulsory education, and not from college education. Therefore, there is a need to double the policy efforts to reduce the educational gap among different regions. In addition, given the current situation where it is difficult to find appropriate ex post facto policy measures to solve the problem of income gap between the graduates from metropolitan colleges and local colleges, it can be said that improving the environment for compulsory education in local areas is a growing necessity for bridging the educational gap among different regions.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Progress in Medical Physics
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• v.27 no.1
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• pp.31-36
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• 2016

The paper discusses radiation dose of dual energy CT on which copper modulation layer, is mounted in order to improve diagnostic performance of the dual energy CT. The radiation dose is estimated using MCNPX and its results are compared with that of the conventional dual energy CT system. CT X-ray spectra of 80 and 120 kVp, which are usually used for thorax, abdominal, head, and neck CT scans, were generated by the SPEC78 code and were used for the source specification 'SDEF' card for MCNPX dose modeling. The copper modulation layer was located 20 cm away from a source covering half of the X-ray window. The radiation dose was measured as changing its thickness from 0.5 to 2.0 mm at intervals of 0.5 mm. Since the MCNPX tally provides only normalized values to a single particle, the dose conversion coefficients of F6 tally for the modulation layer-based dual energy CBCT should be calculated for matching the modeling results into the actual dose. The dose conversion coefficient is $7.2*10^4cGy/output$ that is obtained from dose calibration curve between F6 tally and experimental results in which GAFCHORMIC EBT3 films were exposed by an already known source. Consequently, the dose of the modulation layer-based dual energy cone beam CT is 33~40% less than that of the single energy CT system. On the basis of the results, it is considered that scattered dose produced by the copper modulation layer is very small. It shows that the modulation layer-based dual energy CBCT system can effectively reduce radiation dose, which is the major disadvantage of established dual energy CT.