• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.164-170
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• 2024

The light buoy, a floating structure at sea, is prone to drifting due to external factors such as oceanic weather. This makes it imperative to monitor for any loss or displacement of buoys. In order to address this issue, the Ministry of Oceans and Fisheries aims to issue alerts for buoy displacement by analyzing historical buoy position data to detect patterns. However, periodic lifting inspections, which are conducted every two years, disrupt the buoy's location pattern. As a result, new patterns need to be analyzed after each inspection for location monitoring. In this study, buoy position data from various periods were analyzed using convex hull and distance-based clustering algorithms. In addition, the optimal data collection period was identified in order to accurately recognize buoy location patterns. The findings suggest that a nine-week data collection period established stable location patterns, explaining approximately 89.8% of the variance in location data. These results can improve the management of light buoys based on location patterns and aid in the effective monitoring and early detection of buoy displacement.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.2
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• pp.96-105
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• 2017

Objectives: The purpose of this study is to investigate the professional knowledge and perceptions of the early screening of Autism Spectrum Disorder (ASD) in practitioners who have contact with patients with ASD. Methods: A survey was carried out among 674 practitioners in total, where practitioners are defined as those who work at primary medical centers, public institutions, educational institutions and treatment institutions. The survey was carried out both online and offline, and it mainly focused on 1) knowledge about ASD symptoms, 2) knowledge about the early screening of ASD, 3) measures taken after ASD detection, 4) thoughts on the development of early screening tools for ASD, and 5) the current status of ASD treatment. The data collected were analyzed through descriptive statistics, analysis of frequency and cross tabulation analysis using SPSS WIN 22.0. Results: The results of this study suggest that the practitioners were not aware of the exact symptoms of ASD and their professional knowledge and the environment for early screening were insufficient. Furthermore, very few and inappropriate measures were taken after the detection of ASD. In addition, there was a high demand for early ASD screening tools to be used on site and, regarding treatment, the significance of the implementation of evidence based treatments as well as the continuity of relevant research came to the fore. Conclusion: It seems that there is a lack of knowledge and perception of the early screening of ASD and that education and training among practitioners is urgently required. This issue is discussed in more detail in the paper.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.109-116
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• 2020

With the advent of high-speed railways, railways are one of the most frequently used means of transportation at home and abroad. In addition, in terms of environment, carbon dioxide emissions are lower and energy efficiency is higher than other transportation. As the interest in railways increases, the issue related to railway safety is one of the important concerns. Among them, visual abnormalities occur when various obstacles such as animals and people suddenly appear in front of the railroad. To prevent these accidents, detecting rail tracks is one of the areas that must basically be detected. Images can be collected through cameras installed on railways, and the method of detecting railway rails has a traditional method and a method using deep learning algorithm. The traditional method is difficult to detect accurately due to the various noise around the rail, and using the deep learning algorithm, it can detect accurately, and it combines the two algorithms to detect the exact rail. The proposed algorithm determines the accuracy of railway rail detection based on the data collected.

• Analytical Science and Technology
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• v.26 no.4
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• pp.228-234
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• 2013

Methamphetamine is an amine-containing illegal drug and is distributed unlawfully in South Korea. Finding a rapid, convenient and semi-quantitative determination method for methamphetamine is a very important issue in the area of forensic drug testing. As an effort to develop new screening method, the reactions between three organic compounds which are structurally similar to methamphetamine and N-(9-fluorenylmethoxycarbonyloxy) succinimide (FMOC-NHS) were performed on silica gel ($SiO_2$) TLC plates. Three reference compounds were synthesized and used for the identification, comparison and study of the limit of detection (LOD) of the products obtained from a direct reaction on a TLC plate. As a result, FMOC-NHS as a derivatization reagent generated compounds containing highly UV-active functional groups on the TLC plate after reacting with primary- and secondary amines. In the experiment 2D the LOD of amines was in the range of 0.045 and 0.01 mg/mL ($2{\mu}L/spot$), and in 1D the LOD was in the range of 0.002 and 0.007 mg/mL ($2{\mu}L/spot$). The LODs of the compounds tested were dependent on the concentration of the derivatizing reagent.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.46-52
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• 2017

Along with the expansion of areas in which ICT and Internet of Things (IoT) devices are utilized, open source software has recently expanded its scope of applications to include computers, smart phones, and IoT devices. Hence, as the scope of open source software applications has varied, there have been increasing malicious attempts to attack the weaknesses of open source software. In order to address this issue, various secure coding programs have been developed. Nevertheless, numerous vulnerabilities are still left unhandled. This paper provides some methods to handle newly raised weaknesses based on the analysis of histories and patterns of previous open source vulnerabilities. Through this study, we have designed a weaknesses analysis system that utilizes weakness histories and pattern learning, and we tested the performance of the system by implementing a prototype model. For five vulnerability categories, the average vulnerability detection time was shortened by about 1.61 sec, and the average detection accuracy was improved by 44%. This paper can provide help for researchers studying the areas of weaknesses analysis and for developers utilizing secure coding for weaknesses analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9773-9779
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• 2014

Background: Colorectal cancer (CRC) is one of the prime causes of mortality around the globe, with a significantly rising incidence in the Middle East region in recent decades. Since detection of CRC in the early stages is an important issue, and also since to date there are no comprehensive epidemiologic studies depicting the Middle East region with special attention to the average risk group, further investigation is of significant necessity in this regard. Aim: Our aim was to investigate the prevalence of preneoplastic and neoplastic lesions of the colon in an average risk population. Materials and Methods: A total of 1,208 eligible asymptomatic, average- risk adults older than 40 years of age, referred to Firuzgar Hospotal in the years 2008-2012, were enrolled. They underwent colonoscopy screening and all polypoid lesions were removed and examined by an expert gastrointestinal pathologist. The lesions were classified by size, location, numbers and pathologic findings. Size of lesions was measured objectively by endoscopists. Results: The mean age of participants was $56.5{\pm}9.59$ and 51.6% were male. The overall polyp detection rate was 199/1208 (16.5 %), 26 subjects having non-neoplastic polyps, including hyperplastic lesions, and 173/1208 (14.3%) having neoplastic polyps, of which 26 (2.15%) were advanced neoplasms. The prevalence of colorectal neoplasia was more common among the 50-59 age group. Advanced adenoma was more frequent among the 60-69 age group. The majority of adenomas were detected in the distal colon, but a quarter of advanced adenomas were found in the proximal colon; advance age and male gender was associated with the presence of adenoma. Conclusions: It seems that CRC screening among average-risk population might be recommended in countries such as Iran. However, sigmioidoscopy alone would miss many colorectal adenomas. Furthermore, the 50-59 age group could be considered as an appropriate target population for this purpose in Iran.

• Journal of Microbiology and Biotechnology
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• v.30 no.10
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• pp.1516-1524
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• 2020

Climate change is expected to affect not only availability and quality of water, the valuable resource of human life on Earth, but also ultimately public health issue. A six-year monitoring (total 20 times) of Escherichia coli O157, Salmonella enterica, Legionella pneumophila, Shigella sonnei, Campylobacter jejuni, and Vibrio cholerae was conducted at five raw water sampling sites including two lakes, Hyundo region (Geum River) and two locations near Water Intake Plants of Han River (Guui region) and Nakdong River (Moolgeum region). A total 100 samples of 40 L water were tested. Most of the targeted bacteria were found in 77% of the samples and at least one of the target bacteria was detected (65%). Among all the detected bacteria, E. coli O157 were the most prevalent with a detection frequency of 22%, while S. sonnei was the least prevalent with a detection frequency of 2%. Nearly all the bacteria (except for S. sonnei) were present in samples from Lake Soyang, Lake Juam, and the Moolgeum region in Nakdong River, while C. jejuni was detected in those from the Guui region in Han River. During the six-year sampling period, individual targeted noxious bacteria in water samples exhibited seasonal patterns in their occurrence that were different from the indicator bacteria levels in the water samples. The fact that they were detected in the five Korea's representative water environments make it necessary to establish the chemical and biological analysis for noxious bacteria and sophisticated management systems in response to climate change.