• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Pediatric Infection and Vaccine
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• v.23 no.3
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• pp.194-201
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• 2016

Purpose: Monitoring pneumococcal carriage rates is important. We developed and evaluated the accuracy of a real-time reverse transcription polymerase chain reaction (RT-PCR) protocol for the detection of Streptococcus pneumoniae. Methods: In October 2014, 157 nasopharyngeal aspirates were collected from patients aged <18 years admitted to Chung-Ang University Hospital. We developed and evaluated a real-time PCR method for detecting S. pneumoniae by comparing culture findings with the results of the real-time PCR using genomic DNA (gDNA). Of 157 samples, 20 specimens were analyzed in order to compare the results of cultures, real-time PCR, and real-time RT-PCR. Results: The concordance rate between culture findings and the results of real-time PCR was 0.922 (P<0.01, Fisher exact test). The 133 culture-negative samples were confirmed to be negative for S. pneumoniae using real-time PCR. Of the remaining 24 culture-positive samples, 21 were identified as S. pneumonia -positive using real-time PCR. The results of real-time RT-PCR and real-time PCR from 20 specimens were consistent with culture findings for all S. pneumoniae -positive samples except one. Culture and real-time RT-PCR required 26.5 and 4.5 hours to perform, respectively. Conclusions: This study established a real-time RT-PCR method for the detection of pneumococcal carriage in the nasopharynx. Real-time RT-PCR is an accurate, convenient, and time-saving method; therefore, it may be useful for collecting epidemiologic data regarding pneumococcal carriage in children.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.379-399
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• 2020

The detection of illegal ship is one of the key factors in building a marine surveillance system. Effective marine surveillance requires the means for continuous monitoring over a wide area. In this study, the possibility of ship detection monitoring based on satellite SAR, HF radar, UAV and AIS integration was investigated. Considering the characteristics of time and spatial resolution for each platform, the ship monitoring scenario consisted of a regular surveillance system using HFR data and AIS data, and an event monitoring system using satellites and UAVs. The regular surveillance system still has limitations in detecting a small ship and accuracy due to the low spatial resolution of HF radar data. However, the event monitoring system using satellite SAR data effectively detects illegal ships using AIS data, and the ship speed and heading direction estimated from SAR images or ship tracking information using HF radar data can be used as the main information for the transition to UAV monitoring. For the validation of monitoring scenario, a comprehensive field experiment was conducted from June 25 to June 26, 2019, at the west side of Hongwon Port in Seocheon. KOMPSAT-5 SAR images, UAV data, HF radar data and AIS data were successfully collected and analyzed by applying each developed algorithm. The developed system will be the basis for the regular and event ship monitoring scenarios as well as the visualization of data and analysis results collected from multiple platforms.

• The Korean Journal of Nuclear Medicine
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• v.38 no.1
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• pp.52-61
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• 2004

Purpose: Cervical lymph node metastasis is the most important factor of the prognosis and therapeutic planning in head and neck cancer. With increasing interest of minimally invasive neck surgery, more accurate preoperative assessment of cervical lymph node becomes more essential. We evaluated the diagnostic accuracy of $^{18}F$ FDG-PET in the assessment of lymph node metastasis in patients with primary head and neck cancer and compared the results with those of CT/MRI. Materials and Methods: Thirty-two patients (M/F=27/5, $56{\pm}10yr$) with biopsy proven head and neck cancer (16 supraglottic cancer, 9 tongue cancer, 7 others) underwent FDG-PET and CT/MRI (25/7) within 1 month before neck dissection. Based on lymph node level, the diagnostic sensitivity and specificity of FDG PET and CT/MRI for the metastasis of cervical lymph node were compared. Results: Of 153 lymph node levels dissected in 32 patients, 32 lymph node levels of 19 patients were positive for metastasis by histopatholologic examination. The overall sensitivity and specificity of FDG-PET were 88% (28/32) and 93% (113/121), whereas those of CT/MRI were 56% (18/32) (p=0.002) and 92% (112/121), respectively. The diagnostic sensitivity and specificity of FDG-PET were different according to location of lymph node levels, and those of ipsilateral level 11 were lower than those of other levels. Conclusion: FDG-PET is more sensitive in detecting metastatic cervical lymph node in head and neck cancer than CT/MRI. FDG-PET might be useful in guiding the extent of neck dissection.

• Tuberculosis and Respiratory Diseases
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• v.50 no.5
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• pp.599-606
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• 2001

Background : Since the advent of AIDS, tuberculosis has become a major public health problem in the western society. Therefore, it is essential that pulmonary tuberculosis be rapidly diagnosed. Light microscopic detection of acid-fast organisms in sputum has traditionally been used for rapidly diagnosing tuberculosis. However positive smears are only observed in about one-half to three-quarters of cases. Studies using PCR for diagnosing pulmonary tuberculosis disclosed several shortcomings suggesting an inability to distinguish between active and treated or inactive tuberculosis. In this study, the clinical significance of a PCR-based rapid technique for detecting Mycobacterium tuberculosis DNA in peripheral blood was investigated. Materials and Methods : From July 1, 1998 through to August 30, 1999, 59 patients with presumed tuberculosis, who had no previous history of anti-tuberculosis medication use within one year prior to this study were recruited and followed up for more than 3 months. AFB stain and culture in the sputum and/or pleural fluids and biopsies when needed were performed. Blood samples from each of the 59 patients were obtained in order to identify Mycobacterium Tuberculosis DNA by a PCR test. Results : 1) Forty five out of 59 patients had a final diagnosis of tuberculosis ; Twenty eight were confirmed as having active pulmonary tuberculosis by culture or biopsy. Four were clinically diagnosed with pulmonary tuberculosis. The other 13 patients were diagnosed as having tuberculous pleurisy (9) and extrapulmonary tuberculosis (4). 2) Fourteen patients showed a positive blood PCR test. The PCR assay correctly identified active tuberculosis in 13 out of 14 patients. The overall sensitivity and specificity of this blood peR assay for diagnosing tuberculosis were 29% and 93%, respectively. The positive predictive value was 93%, the negative predictive value was 29% and the diagnostic accuracy was 44%.3) Six out of 14(43%) patients with blood PCR positive tuberculosis were immunologically compromised hosts. 4) A simple chest radiograph in blood PCR positive tuberculosis patients showed variable and inconsistent findings. Conclusion : A peripheral blood PCR assay for Mycobacterium tuberculosis is not recommended as a screening method for diagnosing active tuberculosis. However, it was suggested that the blood PCR assay could contribute to an early diagnostic rate due to its high positive predictive value.

• Tuberculosis and Respiratory Diseases
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• v.61 no.1
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• pp.34-40
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• 2006

Background: Extubation failure was associated with poor prognosis and high hospital mortality. Cuff leak test (CLT) has been proposed as a relatively simple method for detecting laryngeal obstruction that predispose toward postextubation stridor (PES) and reintubation. We examined the risk factors of extubation failure and evaluated the usefulness and limitation of CLT for predicting PES and reintubation. Methods: Thirty-four consecutive patients intubated more than 24 hours were examined. The subjects were evaluated daily for extubation readiness, and CLT was performed prior to extubation. Several parameters in the extubation success and failure group were compared. The accuracy and limitation of CLT were evaluated after choosing the thresholds values of the cuff leak volume (CLV) and percentage (CLP). Results: Of the 34 patients studied, 6 (17.6%) developed extubation failure and 3 (8.8%) were accompanied by PES. The patients who had extubation failure were more likely to have a longer duration of intubation and more severe illness. The patients who developed PES had a smaller cuff leak than the others: according to the CLV ($22.5{\pm}23.8$ vs $233.3{\pm}147.1ml$, p=0.020) or CLP ($6.2{\pm}7.3$ vs $44.3{\pm}24.7%$, p=0.013). The best cut off values for the CLV and CLP were 50ml and 14.7%, respectively. The sensitivity, negative predictive value, and specificity of CLT were relatively high, but the positive predictive value was low. Conclusion: The likelihood of developing extubation failure increases with increasing severity of illness and duration of intubation. A low CLV or CLP (<50ml or 14.7%) is useful in identifying patients at risk of PES, but the CLT is not an absolute predictor and should not be used an indicator for delaying extubation.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.