• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.298-306
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• 2005

Objectives : To develop measurement scales of Internet addiction, and propose a Korean Internet Addiction Index (K-IAI) and classification criteria for Internet addiction from the threshold scores developed. Methods : The identification of the concept of 'Internet addiction' was based on the literature review. To select the scales, an exploratory factor analysis was applied. A construct validation was tested by a confirmatory factor analysis (CFA) with a structured equation model (SEM). In testing the validity of the classification criteria, ANOVA and non-recursive models with SEM were applied. Results : Out of 1,080 questionnaires distributed, 1,037 were returned,; a response rate of 96%. The Cronbach-$\alpha$ of all items was over 0.75. Using an exploratory factor analysis in the condition of a 6 factor constrain as the study model proposed, 23 of the initial 28 items were identified. In testing the discriminant and convergent validity of the selected 23 scales using CFA with SEM, the Internet addiction model explained about 93% of all variances of the data collected, and all the latent variables significantly explained the designated scales. A K-IAI was proposed using the T-scores of the sum of all factor averages. In the classification of users, the basic concept was a twostandard deviation approach of the K-IAI as the criteria of MMPI. The addiction group had a score ${\geq}70$ in the K-IAI, the pre-addiction group between ${\geq}50$ and <70, and the average user group <50. The Internet use times of the classified groups were statistically different in the ANOVA and multiple comparisons. Conclusions : The K-IAI is a reliable and valid instrument for measuring Internet addiction. Moreover, the taxonomy of the groups was also verified using various methods.

• Korean Journal of Acupuncture
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• v.33 no.3
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• pp.102-113
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• 2016

Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.

• Toxicological Research
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• v.21 no.1
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• pp.1-14
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• 2005

Transgenic mouse models have been introduced and accepted by regulatory bodies as an alternative to carcinogenicity assay models to predict and evaluate chemical carcinogens. The recent research outcomes in transgenic mouse models have made progressive advances in the understanding of chemical carcinogenesis and the evaluation of potential human carcinogens. However, these models still remain to be insufficient assay systems although the insufficiencies have been recognised and are being resolved. Based on up to date information from literature, this review article intends to understand currently accepted transgenic mouse models, issues arising from study design, interpretation of the study, results of validation project and their cancer prediction rate, and further perspectives of cancer assay models from the regulatory view point.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1156-1156
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• 2001

This work aimed to prove the feasibility of NIR spectroscopy to detect vegetable protein isolates (soy, pea and wheat) in milk powder. Two hundred and thirty-nine samples of genuine and adulterated milk powder (NIZO, Ede, NL) were analysed by NIRS using an InfraAlyzer 500 (Bran+Luebbe). NIR spectra were collected at room temperature, and data were processed by using Sesame Software (Bran+Luebbe). Separated calibrations for each non-milk protein added, in the range of 0-5%, were calculated. NIR data were processed by using Sesame Software (Bran+Luebbe). Prediction and validation were made by using a set of samples not included into the calibration set. The best calibrations were obtained by the PLSR. The type of data pre-treatment (normalisation, 1$\^$st/ derivative, etc..) was chosen to optimize the calibration parameters. NIRS technique was able to predict with good accuracy the percentage of each vegetable protein added to milk powder (soy: R$^2$ 0.994, SEE 0.193, SEcv 0.301, RMSEPall 0.148; pea: R$^2$ 0.997, SEE 0.1498, SEcv 0.207, RMSEPall 0.148, wheat: R$^2$ 0.997, SEE 0.1418, SEcv 0.335, RMSEPall 0.149). Prediction results were compared to those obtained using other two techniques: capillary electrophoresis and competitive ELISA. On the basis of the known true values of non-vegetable protein contents, the NIRS was able to determine more accurately than the other two techniques the percentage of adulteration in the analysed samples.

• Journal of Information Technology Services
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• v.19 no.1
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• pp.103-112
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• 2020

Job seekers are making various efforts to find a good company and companies attempt to recruit good people. Job search activities through self-introduction essay are nowadays one of the most active processes. Companies spend time and cost to reviewing all of the numerous self-introduction essays of job seekers. Job seekers are also worried about the possibility of acceptance of their self-introduction essays by companies. This research builds a classification model and conducted an experiments to classify self-introduction essays into pass or fail using deep learning and decision tree techniques. Real world data were classified using stratified sampling to alleviate the data imbalance problem between passed self-introduction essays and failed essays. Documents were embedded using Doc2Vec method developed from existing Word2Vec, and they were classified using logistic regression analysis. The decision tree model was chosen as a benchmark model, and K-fold cross-validation was conducted for the performance evaluation. As a result of several experiments, the area under curve (AUC) value of PV-DM results better than that of other models of Doc2Vec, i.e., PV-DBOW and Concatenate. Furthmore PV-DM classifies passed essays as well as failed essays, while PV_DBOW can not classify passed essays even though it classifies well failed essays. In addition, the classification performance of the logistic regression model embedded using the PV-DM model is better than the decision tree-based classification model. The implication of the experimental results is that company can reduce the cost of recruiting good d job seekers. In addition, our suggested model can help job candidates for pre-evaluating their self-introduction essays.

• Journal of Pharmaceutical Investigation
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• v.37 no.2
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• pp.79-84
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• 2007

This study was aimed to establish analytical method of Bi to develop a guideline of the bioequivalence test of tripotassium dicitrato bismuthate (TDB). For this purpose, a simple, specific and sensitive inductively coupled plasma-mass spectrometry (ICP/MS) method were developed and validated in human plasma. Various concentrations of bismuth standard solution (0-25ng/mL) were prepared with distilled water and human blank plasma. To 10mL of the volumetric flasks, 2mL of blank plasma was added with 8ml of distilled water. Bi standard solution was added to prepare the calibration samples and injected into ICP-MS. The plasma samples obtained from volunteers given 3 tablets of bismuth (total 900mg as TDB) were analyzed as described above. As a result, the coefficients of variation were <20% in quantitation limit (0.2 ng/mL) and <15% at the rest of concentrations. The stability test by repeated freezing-thawing cycles showed that the samples were stable only for 24hr. The stability tested for samples with a short-term period of storage at room temperature and pre-treatment prior to the analysis showed very stable over 24hr. In 8 healthy Korean subjects received Denol tablets at the dose of 900mg bismuth, AUC, $C_{max},\;T_{max}$ and half-life $(t_{1/2})$ were determined to be $198.33{\pm}173.78 ng{\cdot}hr/mL,\;64.48{\pm}27.06 ng/mL,\;0.52{\pm}0.21 hr,\;and\;5.15{\pm}2.67 hr$, respectively, from the plasma bismuth concentration-time curves. In conclusion, the method was suitable for the determination of bismuth in human plasma samples and could be applied to bioequivalence test of bismuth tablet.

• Journal of KIISE
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• v.44 no.6
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• pp.559-565
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• 2017

A UAV(unmanned aerial vehicle) requires higher reliability for external effects such as electromagnetic interference because a UAV is operated by pre-designed programs that are not under human control. The design of a small UAV with a complete resistance against the external effects, however, is difficult because of its weight and size limitation. In this circumstance, a conventional small UAV dropped to the ground when an external effect caused the rebooting of the flight-control computer(FCC); therefore, this paper presents a novel algorithm for the improvement of the flight safety of a small UAV. The proposed algorithm consists of three steps. The first step comprises the calibration of the navigation equipment and validation of the calibrated data. The second step is the storage of the calibration data from the UAV take-off. The third step is the restoration of the calibration data when the UAV is in flight and FCC has been rebooted. The experiment results show that the flight-control system can be safely operated upon the rebooting of the FCC.

• Journal of Korean Home Economics Education Association
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• v.15 no.1
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• pp.81-94
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• 2003

This study tested students’ ICT application capability and Problems solving ability by ICT instructional environment when ICT teaching method is applied to $\boxDr$Understanding of Myself and Family$\boxUl$ unit of home economics. Following research Problems were formulated for this study : 1. Are there any differences of the effects on improvements of students’ ICT application capability by ICT instructional environment\ulcorner 2. Are there any differences of effects on improvements of students’ problems solving ability by ICT instructional environment\ulcorner 3. Are there any differences of effects on improvements of students’ Performance assessment results by ICT instructional environment\ulcorner The researcher developed a homepage for the ICT teaching-learning. and prepared Problems-based teaching-learning lesson plan. The students were divided into two groups (experimental group 1 and experimental group 2) by ICT instructional environment. The Pre-test and post-test were conducted before and after the experimental class. The ICT class experimental period was for 16 weeks. from March 10. 2002 to July 10. 2002. The experimental group 1 was given 16-weeks classwork under the classroom environment of 1 PC for each classroom(The classroom with advanced educational equipments) . while the experimental group 2 was given 16-weeks classwork under the classroom environment of 1 PC for each student(multimedia classroom) . The results of the study are as follows: 1 All of the ICT teaching methods under both instructional environments were found to be effective on the improvements of the ICT application capability. 2. There were statistically significant differences of problems solving ability between two groups in application and the measure of validation. 3. The experimental group 1(1 PC for each classroom) did not show any improvements of Performance assessment results. while the experimental group 2(1 PC for each student) showed some improvements.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.5
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• pp.436-443
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• 2020

The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

• Journal of the Korean Geotechnical Society
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• v.29 no.1
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• pp.171-181
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• 2013

The radial mapping rule in bounding surface model was experimentally investigated by analyzing the drained stress probe tests on Chicago clays. The experimental data obtained from 10 drained stress probe paths were analyzed to calculate the directions of the plastic strain increments. The anisotropic bounding surface model was adopted to represent a bounding yield surface which resides in the pre-consolidation yield stress of undisturbed clays. The projection origins were estimated by finding the interceptions of the straight lines passing through the current stress point and the imaginary yield stress point on the bounding surface. The results show that the projection origin is not fixed at a point but moves toward the direction of the stress probe path after it is established around the initial stress point.