• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.112-126
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• 2022

Purpose To determine the value of 3 Tesla (T) MRI texture analysis for predicting tumor margin infiltration in soft tissue sarcomas. Materials and Methods Thirty-one patients who underwent 3T MRI and had a pathologically confirmed diagnosis of soft tissue sarcoma were included in this study. Margin infiltration on pathology was used as the gold standard. Texture analysis of soft tissue sarcomas was performed on axial T1-weighted images (WI) and T2WI, fat-suppressed contrast-enhanced (CE) T1WI, diffusion-weighted images (DWI) with b-value of 800 s/mm², and apparent diffusion coefficient (ADC) was mapped. Quantitative parameters were compared between sarcomas with infiltrative margins and those with circumscribed margins. Results Among the 31 patients with soft tissue sarcomas, 23 showed tumor margin infiltration on pathology. There were significant differences in kurtosis with the spatial scaling factor (SSF) of 0 and 6 on T1WI, kurtosis (SSF, 0) on CE-T1WI, skewness (SSF, 0) on DWI, and skewness (SSF, 2, 4) on ADC between sarcomas with infiltrative margins and those with circumscribed margins (p ≤ 0.046). The area under the receiver operating characteristic curve based on MR texture features for identification of infiltrative tumor margins was 0.951 (p < 0.001). Conclusion MR texture analysis is reliable and accurate for the prediction of infiltrative margins of soft tissue sarcomas.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Clinical and Experimental Reproductive Medicine
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• v.23 no.1
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• pp.51-59
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• 1996

Controlled Ovarian hyperstimulation(COH) is generally used to obtain synchronous high quality oocytes in in vitro fertilization-embryo transfer(IVF-ET). Many investigators have studied the relationship between serum hormone levels and outcomes of IVF-ET because there is no accurate estimation method of oocyte quality. Early premature luteinization of follicles before oocyte retrieval is the most troublesome problem in COH for IVF-ET. Gonadotropin-releasing hormone agonists(GnRH-a) are used as adjuncts with gonadotropins for COH in patients undergoing in IVF. The possible benefits of GnRH-a pretreatment include improving oocyte quality, allowing a more synchronous cohort of follicles to be recruited, and preventing premature lueinization hormone surges. In COH of IVF cycles, we investigated whether an elevated progesterone(P4) level on the day of human chorionic gonadotropin(hCG) administration indicates premature luteinization and is associated with a lower fertilization rate. Many investigators have studied that the lower fertilization rates seen in patients with elevated P4 levels might result from an adverse effect of P4 on the oocytes. We hypothesizes that serum P4 levels around the day of hCG may be helpful prediction of out come in IVF-ET cycles. Success rates after COH of IVF-ET cycles are dependent upon many variable factors. Follicular factors including the number of follicles, follicular diameters and especially serum estradiol(E2) levels as an indirect measurement of follicular function and guality have been thought to influence the outcomes of IVF-ET. To assess whether serum P4 and E2 levels affect the fertilization and pregnancy rate, we reviewed the stimulation cycles of 113 patients (119 cycles) undergoing IVF-ET with short protocol with GnRH-a, from March 1993 to August 1994 retrospectively. The serum P4 and E2 levels were compared on the day of hCG in the pregnant group, 45 patients(47 cycles) and in the non-pregnant group, 68 patients (72 cycles) respectively. The serum E2 level in non-pregnant group was $1367{\pm}875.8$ pg/ml which was significantly lower than that of pregnant group, $1643{\pm}987.9$ pg/ml( p< 0.01 ). And the serum P4 level in non-pregnant group was $2.1{\pm}1.4$ ng/ml which was significantly higher than that of pregnant group, $1.0{\pm}0.7$ ng/ml( p< 0.001 ). The fertilization rate was $61.3{\pm}21.3%$ in pregnant group which was higher than that of non-pregnant group, $41.1{\pm}20.2%$ (p< 0.01). We suggest that the serum levels of P4 and E2 on the day of hCG administration are additional parameters that predict the outcomes of IVF-ET cycles.

• Journal of radiological science and technology
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• v.31 no.2
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• pp.161-169
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• 2008

Since measuring the size of kidney with sonography becomes an important index for diagnosis, treatment, and prognostic prediction in kidney disease, the accurate measurement and evaluation on this are clinically very important. Accordingly, the purpose of this study was to increase reproducibility and objectivity in measuring the size of kidney by enumerating factors that have an impact for measurement. It targeted 44 adults in Korea at the age of 21-27. It measured in order for both kidneys to be seen most largely while changing a subject-examiner's position in a state of fasting for 8 hours and a transducer's approaching direction. It compared a size of kidney by measuring, respectively, with the same method in 30 minutes and in 1 hour after drinking water in 700-1,000cc. In case of the lateral approach scan in decubitus position, the average length of the kidney both to the right and the left and the deviation of measurement to be the largest. In NPO(None Per Oral) state, the average length in the right kidney was 10.19cm, and the average length in the left kidney was 10.33cm. In 60 minutes after taking moisture, the average length in the right kidney was 10.94cm, and the average length in the left kidney was 11.13cm. In comparing the average length of the kidney in NPO state and its average length in 60 minutes after taking moisture, the size swelled by 7.3% for the length in the right kidney and by 7.7% in the left, thereby having been indicated to be statistically significant(P<0.003). The measurement in a size of kidney by using ultrasound may be measured differently depending on a patient's state of taking moisture and a transducer's approaching direction. It is thought that when the measurement in a size of kidney is especially important clinically, the intake and intake time in moisture need to be considered and that measuring with the posterior approach in prone position is a good method aiming to increase reproducibility in measuring length of the kidney.

• Journal of Nutrition and Health
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• v.53 no.5
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• pp.476-487
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• 2020

Purpose: This study evaluated the accuracy of the 24-hour diet recall method for estimating energy intakes in elderly women using the doubly labeled water (DLW) method. Methods: The subjects were 23 elderly women with a mean age of 70.3 ± 3.3 years and body mass index (BMI) of 23.9 ± 2.8 kg/㎡. The total energy expenditure (TEE_DLW) was determined by using the DLW and used to validate the 24-hour diet recall method. The total energy intake (TEI) was calculated from the 24-hour diet recall method for three days. Results: TEI (1,489.6 ± 211.1 kcal/day) was significantly lower than TEE_DLW (2,023.5 ± 234.9 kcal/day) and was largely under-reported by -533.9 ± 228.0 kcal/day (-25.9%). The accurate prediction rate of elderly women in this study was 8.7%. The Bland-Altman plot, which was used to evaluate the TEI and the TEE_DLW, showed that the agreement between them was negatively skewed, ranging from -980.8 kcal/day to -86.9 kcal/day. Conclusion: This study showed that the energy intake of elderly women was underreported. Strategies to increase the accuracy of the 24-hour diet recall methods in the elderly women should be studied through analysis of factors that affect underreporting rate. Further studies will be needed to assess the validity of the 24-hour diet recall method in other population groups.

• Journal of the korean academy of Pediatric Dentistry
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• v.28 no.1
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• pp.16-24
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• 2001

The purpose of this study was to evaluate the specificity, sensitivity, and diagnostic power of caries activity test using laser fluorescence. The subjects of this study were 50 children of $7\sim9$ years old. Fluorescence from initial carious lesion of teeth illuminated by an argon laser(480nm) was observed and photographed with barrier filter. Visual examination for the dDfFtT rate and Streptococcus mutans colony counting was done to evaluate correlation with caries activity test using laser fluorescence. Data analysis was accomplished by Axelsson's method. The results from the present study can be summarized as follows: 1. There was positive correlation $(\gamma=0.48)$ between laser fluorescence test and Streptococcus mutans count. And also positive correlation $(\gamma=0.39)$ exists between laser fluorescence test and dDfFtT rate (P<0.01). 2. Positive correlation $(\gamma=0.27)$ between Streptococcus mutans colony count and dDfFtT rate was found(P<0.05). 3. When dDfFtT rate was defined to standard testing method, the specificity, senstivity, and diagnostic power of laser fluorescence test were 44.4%, 85.7%, and 87.8%. 4. When dDfFtT rate was defined to standard testing method, the specificity, senstivity, and diagnostic power of S. mutans colony counting were 77.8%, 92.9%, 84.8%. 5. When S. mutans colony counting was defined to standard testing method, sensitivity, specificity and diagnostic power of laser fluorescence test were 40.0%, 84.8%, 95.1%. In regard to above results, laser fluorescence test considered to be accurate and reliable method for determining caries activity because of it's close relationship with caries susceptibility test and caries experience measurements. And it was also considered to be practical because it would be simple, inexpensive, and time saving method.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.89-103
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• 2013

National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.