• Korean Journal of Culture and Social Issue
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• v.12 no.5_spc
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• pp.99-116
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• 2006

This paper discuss adolescent's a quality of life related with risk behavior. The purpose of this study investigate to influence on risk behavior(runaway, smoking, sexual behavior) of the protective factors that moderate adolescent's problem behavior(delinquency). The assumption of this study that the protective factors counterbalance the negative influence of risk factors and finally, diminish a the problem behavior including a delinquent. A total of 1,020 students of a vocational high schook and a 216 adolescents of a special groups(the public institution that consisted with a delinquent young man) completed the questionnaires(risk behavior, 5 protective factors) of compiled by this researcher. The protective factors have selected based on the various prior studies analyzed with adolescent's risk behavior a family functioning, a father(a mother) each and child communication, a self efficacy, and a social support. Statistics appled for the data analysis are Chisqure analysis, two-way ANOVA, and Standard Discrimination analysis. The results of this study are as follows. First, the special group is higher than the general group in the rate of runaway, smoking, and sexual deviant behavior. Second, the protective factors are not action in the special group have experienced delinquency, but are only action in the general group consisted with the students of a vocational high schools. This means that the protective factors discriminating the participation of the risk behaviors, and blocking out the intervention of a problem behavior in the general adolescents. Although each protective factor influence to different according to each risk behavior, a role of a parent-child communication, a family functioning, and self-efficacy high orderly. Finally, discussed based on the previous studies that the protective factors moderate the negative influence of risk factors, offset the connection between a risk behavior and a. problem behavior, and improve and a resilience and the quality of life of the adolescents.

• Journal of Venture Innovation
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• v.6 no.3
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• pp.21-42
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• 2023

The purpose of this study is to examine the structural relationships of empowerment, continuous learning activities, and collaboration in the effects of person-organization fit and person-job fit on task performance among employees of early-stage startups with less than 7 years of experience that have an agile organizational culture. To achieve this purpose, we developed a research model and established hypotheses based on theoretical review and the results of prior research. Data were collected from employees working in startups with less than 7 years of experience that have an agile organizational culture, and a total of 204 responses were utilized for the final analysis. Before hypothesis testing, we examined the characteristics of the sample, conducted confirmatory factor analysis to assess measurement model fit, tested convergent and discriminant validity and analyzed reliability. After confirming the goodness of fit of the structural equation model, it tested the hypotheses, including mediating effects, based on the results of the structural equation model analysis. The results show that person-organization fit has a significant positive effect on empowerment, continuous learning activities, and collaboration. Similarly, person-job fit was found to have a significant positive effect on empowerment, continuous learning activities, and collaboration. However, it was found that empowerment did not have a statistically significant effect on task performance, while continuous learning activities had a significant and negative effect on task performance. Finally, collaboration was found to have a significant positive effect on task performance, and the mediation analysis results indicated that collaboration had a mediating effect on the relationship between person-job fit and task performance. Based on the findings of this study, it discussed the significance of the study and theoretical and practical implications. It also discussed limitations of the research and suggested directions for future research.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.49-95
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• 2024

Polygraph examination, statement validity analysis and P300-based concealed information test are major three examination tools, which are use to determine a person's truthfulness and credibility in criminal procedure. Although polygraph examination is most common in criminal procedure, but it has little admissibility of evidence due to the weakness of scientific basis. In 1990s to support the weakness of scientific basis about polygraph, Farwell and Donchin proposed the P300-based concealed information test technique. The P300-based concealed information test has two strong points. First, the P300-based concealed information test is easy to conduct with polygraph. Second, the P300-based concealed information test has plentiful scientific basis. Nevertheless, the utilization of P300-based concealed information test is infrequent, because of the quantity of probe stimulus. The probe stimulus contains closed information that is relevant to the crime or other investigated situation. In tradition P300-based concealed information test protocol, three or more probe stimuli are necessarily needed. But it is hard to acquire three or more probe stimuli, because most of the crime relevant information is opened in investigative situation. In addition, P300-based concealed information test uses oddball paradigm, and oddball paradigm makes imbalance between the number of probe and irrelevant stimulus. Thus, there is a possibility that the unbalanced number of probe and irrelevant stimulus caused systematic underestimation of P300 amplitude of irrelevant stimuli. To overcome the these two limitation of P300-based concealed information test, one-probe P300-based concealed information test protocol is explored with various machine learning algorithms. According to this study, parameters of the modified one-probe protocol are as follows. In the condition of female and male face stimuli, the duration of stimuli are encouraged 400ms, the repetition of stimuli are encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. In the condition of two-syllable word stimulus, the duration of stimulus is encouraged 300ms, the repetition of stimulus is encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. It was also conformed that the logistic regression (LR), linear discriminant analysis (LDA), K Neighbors (KNN) algorithms were probable methods for analysis of P300 amplitude. The one-probe P300-based concealed information test with machine learning protocol is helpful to increase utilization of P300-based concealed information test, and supports to determine a person's truthfulness and credibility with the polygraph examination in criminal procedure.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.3
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• pp.273-291
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• 2020

The strength characteristics of the three orthogonal splitting planes, known as rift, grain and hardway planes in granite quarries, were examined. R, G and H specimens were obtained from the block samples of Jurassic granites in Geochang and Hapcheon areas. The directions of the long axes of these three specimens are perpendicular to each of the three planes. First, The chart, showing the scaling characteristics of three graphs related to the uniaxial compressive strengths of R, G and H specimens, were made. The graphs for the three specimens, along with the increase of strength, are arranged in the order of H < G < R. The angles of inclination of the graphs for the three specimens, suggesting the degree of uniformity of the texture within the specimen, were compared. The above angles for H specimens(θ^H, 24.0°~37.3°) are the lowest among the three specimens. Second, the scaling characteristics related to the three graphs of RG, GH and RH specimens, representing a combination of the mean compressive strengths of the two specimens, were derived. These three graphs, taking the various N-shaped forms, are arranged in the order of GH < RH < RG. Third, the correlation chart between the strength difference(Δσ_t) and the angle of inclination(θ) was made. The above two parameters show the correlation of the exponential function with an exponent(λ) of -0.003. In both granites, the angle of inclination(θ^RH) of the RH-graph is the lowest. Fourth, the six types of charts, showing the correlations among the three kinds of compressive strengths for the three specimens and the five parameters for the two sets of microcracks aligned parallel to the compressive load applied to each specimen, were made. From these charts for Geochang and Hapcheon granites, the mean value(0.877) of the correlation coefficients(R²) for total density(Lt), along with the frequency(N, 0.872) and density(ρ, 0.874), is the highest. In addition, the mean values(0.829) of correlation coefficients associated with the mean compressive strengths are more higher than the minimum(0.768) and maximum(0.804) compression strengths of three specimens. Fifth, the distributional characteristics of the Brazilian tensile strengths measured in directions parallel to the above two sets of microcracks in the three specimens from Geochang granite were derived. From the related chart, the three graphs for these tensile strengths corresponding to the R, G and H specimens show an order of H(R1+G1) < G(R2+H1) < R(R1+G1). The order of arrangement of the three graphs for the tensile strengths and that for the compressive strengths are mutually consistent. Therefore, the compressive strengths of the three specimens are proportional to the three types of tensile strengths. Sixth, the values of correlation coefficients, among the three tensile strengths corresponding to each cumulative number(N=1~10) from the above three graphs and the five parameters corresponding to each graph, were derived. The mean values of correlation coefficients for each parameter from the 10 correlation charts increase in the order of density(0.763) < total length(0.817) < frequency(0.839) < mean length(Lm, 0.901) ≤ median length(Lmed, 0.903). Seventh, the correlation charts among the compressive strengths and tensile strengths for the three specimens were made. The above correlation charts were divided into nine types based on the three kinds of compressive strengths and the five groups(A~E) of tensile strengths. From the related charts, as the tensile strength increases with the mean and maximum compressive strengths excluding the minimum compressive strength, the value of correlation coefficient increases rapidly.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.30-39
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• 1999

Objectives. This study was carried out to evaluate hearing impairment judgement and to investigate the differences in various diagnostic criteria for noise-induced hearing loss (NIHL) among workers who required for close observation (C). Methods. Out of 731,029 workers who had taken the specific periodic health examination in 1994, we used the audiometric data on 37,999 workers (C) eliminating the employees who had previous otologic problems. Many investigators have being using different criteria for the evaluation of hearing impairment. In this study, we used the criteria of early (1989-1994), current, compensation for NIHL in Korea, 2-, 3-, 4-divided classification and hearing loss at 4,000 Hz and compared the evaluation results. Results. The prevalences of C and workers who had occupational disease $(D_1)$ diagnosed for NIHL were 11.1 % and 0.44 %. There were significant difference in the prevalences of C and $D_1$, depending on different province of Korea. Pure tone averages (PTAs) were not appropriately applied in their evaluation 97% of workers whom we studied on were below the level of mild hearing loss judged by ISO standard. However, there were wide variations in the prevalence rate of mild hearing loss by diagnostic criteria. Thus, there were different judgements in determining the degree of NIHL depending on which diagnostic criteria were utilized. PTAs were found 20.54 (Rt) and 20.74 (Lt) when the method of 3-divided classification was applied for audiometric data. The degree of hearing impairment of the left ear was more severe than that of right ear. The prevalence of normal hearing threshold below 20 dB was 75.4% and the range of difference in both ear was below 10 dB. Right sided hearing threshold levels were 21.08 dB (500 Hz), 18.44 dB (1,000 Hz), 22.09 (2,000 Hz) and 52.36 dB (4,000 Hz). There was typical high frequency loss (C5-dip at 4,000 Hz) above 30 - 40 dB in normal hearing level. The increasing trend in hearing threshold level was gradually decreased by the increase of PTAs. The difference between PTAs and threshold at 4,000 Hz was about 10 dB. Conclusions. We could found that PTAs in the previous examination were not appropriately evaluated. This study revealed that they did not use unique criteria for managing the workers of NIHL. For the prevention of NIHL, it was found that the quality control on diagnosis and comprehensive management program were required, especially for those of hearing loss (C).

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.7 no.1
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• pp.31-39
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• 2004

Purpose: Although acute nonspecific mesenteric lymphadenitis (ANML) is probably common cause of abdominal pain in children, which can be severe enough to be an abdominal emergency, the clinical features of mesenteric lymphadenitis are not clear. Also, a differential diagnosis with acute appendicitis (APPE) is indispensable to avoid serious complications. The clinical features of ANML were determined, and the risk factors for differential diagnosis with APPE were analyzed. Methods: Between November 2000 and May 2001, data from 26 patients (aged 1 to 11 years) with ANML and 21 patients (aged 2 to 13 years) with APPE were reviewed. ANML was defined as a cluster of five or more lymph nodes measuring 10 mm or greater in their longitudinal diameter in the right lower quadrant (RLQ) without an identifiable specific inflammatory process on the ultrasonographic examination. There were risk factors on patient's history, physical examination, and laboratory examination; the location of abdominal pain, abdominal rigidity, rebound tenderness, fever, nocturnal pain, the vomiting intensity, the diarrhea intensity, the symptom duration, and the peripheral blood leukocytes count. Results: Of the 26 ANML patients and 21 APPE patients, abdominal pain was noted on periumbilical (76.9% vs 14.2%), on RLQ (11.5% vs 71.4%), with abdomen rigidity (7.6% vs 80.9%), with rebound tenderness (0.0% vs 76.1%)(p<0.05), in the lower abdomen (11.5% vs 14.2%), and at night (80.8% vs 100.0%) (p>0.05). The clinical symptoms were vomiting (38.4% vs 90.4%), the vomiting intensity ($1.5{\pm}0.7$ [1~3]/day vs $4.5{\pm}2.9$ [1~10]/day), diarrhea (65.3% vs 28.5%) (p<0.05), and fever (61.5% vs 76.2%)(p>0.05). The period to the subsidence of abdominal pain in the ANMA patients was $2.5{\pm}0.5$ (2~3) days. The laboratory data showed a significant difference in the peripheral blood leukocytes count ($8,403{\pm}1,737[5,900{\sim}12,300]/mm^3\;vs\;15,471{\pm}3,749[5,400{\sim}20,800]/mm^3$)(p<0.05). Discriminant analysis between ANML and APPE showed that the independent discriminant factors were a vomiting intensity and the peripheral blood leukocytes count and the discriminant power was 95.7%. Conclusion: The clinical characteristics of ANML were abrupt onset of periumbilical pain without rigidity or rebound tenderness, a mild vomiting intensity, normal peripheral leukocytes count, and relatively short clinical course. If the abdominal pain persist for more than 3 days, and/or the vomiting intensity is more than 3 times/day, and/or the peripheral leukocytes count is over $13,500/mm^3$, abdominal ultrasonography is recommended to rule out APPE.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Radiation Oncology Journal
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• v.19 no.1
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• pp.53-65
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• 2001

Purpose : To improve the local control of patients with nasopharyngeal cancer, we have implemented 3-D conformal radiotherapy and forward intensity modulated radiation therapy (IMRT) to used of compensating filters. Three dimension conformal radiotherapy with intensity modulation is a new modality for cancer treatments. We designed 3-D treatment planning with 3-D RTP (radiation treatment planning system) and evaluation dose distribution with tumor control probability (TCP) and normal tissue complication probability (NTCP). Material and Methods : We have developed a treatment plan consisting four intensity modulated photon fields that are delivered through the compensating tilters and block transmission for critical organs. We get a full size CT imaging including head and neck as 3 mm slices, and delineating PTV (planning target volume) and surrounding critical organs, and reconstructed 3D imaging on the computer windows. In the planning stage, the planner specifies the number of beams and their directions including non-coplanar, and the prescribed doses for the target volume and the permissible dose of normal organs and the overlap regions. We designed compensating filter according to tissue deficit and PTV volume shape also dose weighting for each field to obtain adequate dose distribution, and shielding blocks weighting for transmission. Therapeutic gains were evaluated by numerical equation of tumor control probability and normal tissue complication probability. The TCP and NTCP by DVH (dose volume histogram) were compared with the 3-D conformal radiotherapy and forward intensity modulated conformal radiotherapy by compensator and blocks weighting. Optimization for the weight distribution was peformed iteration with initial guess weight or the even weight distribution. The TCP and NTCP by DVH were compared with the 3-D conformal radiotherapy and intensitiy modulated conformal radiotherapy by compensator and blocks weighting. Results : Using a four field IMRT plan, we have customized dose distribution to conform and deliver sufficient dose to the PTV. In addition, in the overlap regions between the PTV and the normal organs (spinal cord, salivary grand, pituitary, optic nerves), the dose is kept within the tolerance of the respective organs. We evaluated to obtain sufficient TCP value and acceptable NTCP using compensating filters. Quality assurance checks show acceptable agreement between the planned and the implemented MLC(multi-leaf collimator). Conclusion : IMRT provides a powerful and efficient solution for complex planning problems where the surrounding normal tissues place severe constraints on the prescription dose. The intensity modulated fields can be efficaciously and accurately delivered using compensating filters.