• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.29-44
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• 2015

Digital Signage, a way of advertising or delivering information to viewers through digital displays, has expanded from being just an advertising channel in public places. Recently, it has become widely prevalent in restaurants and retail stores. Despite its wide expansion, digital signage is limited to specific usages and services and the devices it uses are also quite expensive. This study introduces a stick-type digital signage product that operates on Android OS, which addresses all the weaknesses of digital signage with much more reasonable pricing and stable operation. For stability, performance tests were executed on the hardware and applications. The results for hardware performance were extremely promising, as each scenario's maximum performance results, measured by Load Runner programs, reached target indexes. Also, as a result of the usability test, all participants, including non-digital signage system users (novices), were able to easily learn all the tasks. As a result of user satisfaction survey, positive responses were exhibited for ease of learning and usability (LEU), helpfulness and problem solving capabilities (HPSC), affective aspect and multimedia properties (AAMP), commands and minimal memory load (CMML), and control and efficiency (CE).

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.434-443
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• 2013

The purpose of this study is to investigate the effectiveness of the way-finding exercise using a map in rehabilitation of cognitive function and activities of daily living in patients with a stroke. For the seven patients diagnosed with hemiplegia from a stroke, we executed the way-finding exercise using a map in the hospital, three times a week for two weeks. Loewenstein Occupational Therapy Cognitive Assessment(LOTCA) and Functional Independence Measure(FIM) were used to measure the cognitive function and performance of activities of daily living before and after intervention. For the visual perception area and the spatial relations of the spatial perception area of LOTCA, scores were significantly higher than before intervention(p<.05). For the walk/wheelchair of locomotion area and the problem solving of the social cognition area of FIM, scores were significantly higher than before intervention(p<.05). The results of this study show that a way-finding exercise for patients with a stroke is a useful therapeutic approach by enhancing cognitive function and performance of activities of daily living.

• Journal of the Korean Society of Radiology
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• v.13 no.6
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• pp.857-864
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• 2019

Many countries, including Canada, operate a sonographer license system separately from a radiological technologist license. However, in Korea, radiological technologists perform ultrasound imaging under the guidance of doctors. Therefore, in order to have the opportunity to provide a systematic education by analyzing the job competency of the radiological technologist's ultrasound imaging, based on the Canadian National Competency Profile (NCP) lists, this study measured the job content validity of the job competences and detailed competencies required for performing ultrasonography in Korea. From the results of comparing and analyzing the importance of the core competencies included in the Korean radiological technologist's job competencies and the degree of job performance, the average overall importance was 4.087, the average of overall performance was 3.640, showing that the importance was higher than the performance and that there was a statistically significant difference. In conclusion, 'A Communication', 'B Professional responsibilities', 'D Operation of equipment' and 'G Workplace health and safety' showed high job content validity. However, it is said that as 'C Patient assessment and care', 'E Critical thinking and problem solving', and 'H Image' showed low job content validity, it is necessary to seek ways to strengthen and complement these competencies.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.1
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• pp.13-20
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• 2017

In this paper, we evaluate the basic test results of the 32 Gbps observational equipment introduced as the back-end system for the wideband VLBI (Very Long Baseline Interferometry) observation of KVN (Korean VLBI Network). Radio astronomers want to make a large radio telescope that has excellent performance in order to observe the superfine structure of a celestial body, but a lot of money is needed. Therefore, in order to increase the sensitivity, the performance improvement of the receiving system and the method of observing the wide frequency bandwidth are introduced. To do this, we adopted a wideband sampling method for converting analog signals to digital with ultra-fast speeds and a wideband sampler for performing digital filtering in order to observe a wide observational frequency bandwidth. The wideband sampler (OCTAD-K) supports up to 16 Gsps-2bits sampling and supports a variety of observational bandwidth using digital filtering techniques. In particular, it is designed to support KVN's 4-frequency simultaneous observation system and VERA(VLBI Exploration of Radio Astrometry)'s 2-beam observation system. It can also support polKVN(Korean VLBI Network), KaVA(KVN and VERA Array), 32Gbps Direct Sampler, Digital Filter, Widebandarization observations and supports the standard VDIF(VLBI Data Interchange Format) format of observed data. In this paper, the performance of the system and the problem solving are described in detail after performing the factory inspection and field test before the system is introduced.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.19-26
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• 2022

The Comprehensive in vitro Proarrhythmic Assay(CiPA) project was launched for solving the hERG assay problem of being classified as high-risk groups even though they are low-risk drugs due to their high sensitivity. CiPA presented a protocol to predict drug toxicity using physiological data calculated based on the in-silico model. in this study, features calculated through the in-silico model are analyzed for correlation of changing action potential in the near future, and features are verified through predictive performance according to drug datasets. Using the O'Hara Rudy model modified by Dutta et al., Pearson correlation analysis was performed between 13 features(dVm/dtmax, APpeak, APresting, APD90, APD50, APDtri, Ca_peak, Ca_resting, CaD90, CaD50, CaDtri, qNet, qInward) calculated at 100 pacing, and between dVm/d_{tmax_repol} calculated at 1,000 pacing, and linear regression analysis was performed on each of the 12 training drugs, 16 verification drugs, and 28 drugs. Indicators showing high coefficient of determination(R²) in the training drug dataset were qNet 0.93, AP resting 0.83, APDtri 0.78, Ca resting 0.76, dVm/dt_max 0.63, and APD90 0.61. The indicators showing high determinants in the validated drug dataset were APDtri 0.94, APD90 0.92, APD50 0.85, CaD50 0.84, qNet 0.76, and CaD90 0.64. Indicators with high coefficients of determination for all 28 drugs are qNet 0.78, APD90 0.74, and qInward 0.59. The indicators vary in predictive performance depending on the drug dataset, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug dataset, and the entire drug dataset.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.91-117
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• 2007

This study was to compare the contents and practical problems addressed, the process of teaching-learning method, and evaluation method of Korean Home Economics curriculum and of the Oregon and Ohio's Practical Problem Focused Family & Consumer Sciences Curricula. The results are as follows. First, contents of Korean curriculum are organized by major sub-concepts of Home Economics academic discipline whereas curricular of both Oregon and Ohio states are organized by practical problems. Oregon uses the practical problems which integrate multi-subjects and Ohio uses ones which are good for the contents of the module by integrating concerns or interests which are lower or detailed level (related interests). Since it differentiates interest and module and used them based on the basic concept of Family and Consumer Science, Ohio's approach could be easier for Korean teachers and students to adopt. Second, the teaching-learning process in Korean home economics classroom is mostly teacher-centered which hinders students to develop higher order thinking skills. It is recommended to use student-centered learning activities. State of Oregon and Ohio's teaching-learning process brings up the ability of problem-solving by letting students clearly analyze practical problems proposed, solve problems by themselves through group discussions and various activities, and apply what they learn to other problems. Third, Korean evaluation system is heavily rely on summative evaluation such as written tests. It is highly recommended to facilitate various performance assessment tools. Since state of Oregon and Ohio both use practical problems, they evaluate students mainly based on their activity rather than written tests. The tools for evaluation include project documents, reports of learning activity, self-evaluation, evaluation of discussion activity, peer evaluation in a group for each students for their performance, assessment about module, and written tests as well.

• Journal of Korean Academy of Nursing Administration
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• v.2 no.1
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• pp.97-114
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• 1996

INSTRODUCTION Service quality is, unlike goods quality, an abstract and elusive constuct. Service quality and its requirements are not easily understood by consumers, and also present some critical research problems. However, quality is very important to marketers and consumers in that it has many strategic benefits in contributing to profitability of marketing activities and consumers' problem-solving activities. Moreover, despite the phenomenal growth of medical service sector, few researchers have attempted to define and model medical service quality. Especially, little research has focused on the evaluation of medical service quality and patient satisfaction from the perspectives of both the provider and the patient. As competition intensifies and patients are demanding higher quality of medical service, medical service quality and patient satisfaction has emerged as a critical research topic. The major purpose of this article is to explore the concept of medical service quality and its evaluation from both nurse and patient perspectives. This article attempts to achieve its purpose by (1)classfying critical service attibutes into threecategories(satisfiers, hygiene factors, and performance factors). (2)measuring the relative importance of need criteria, (3)evaluating SERVPERF model and SERVQUAL model in medical service sector, and (4)identifying the relationship between perceived quality and overall patient satisfaction. METHOD Data were gathered from a sample of 217 patients and 179 nurses in Seoul-area general hospitals. From the review of previous literature, 50 survey items representing various facets of the medical service quality were developed to form a questionnaire. A five-point scale ranging from "Strongly Agree"(5) to "Strongly Disagree"(1) accompanied each statement(expectation statements, perception statements, and importance statements). To measure overall satisfaction, a seven-point scale was used, ranging from "Very Satisfied"(7) to "Very Dissatisfied"(1) with no verbal labels for scale points 2 through 6 RESULTS In explaining the relationship between perceived performance and overall satisfaction, only 31 variables out of original 50 survey items were proven to be statistically significant. Hence, a penalty-reward analysis was performed on theses 31 critical attributes to find out 17 satisfiers, 8 hygiene factors, and 4 performance factors in patient perspective. The role(category) of each service quality attribute in relation to patient satisfaction was com pared across two groups, that is, patients and nurses. They were little overlapped, suggesting that two groups had different sets of 'perceived quality' attributes. Principal components factor analyses of the patients' and nurses' responses were performed to identify the underlying dimensions for the set of performance(experience) statements. 28 variables were analyzed by using a varimax rotation after deleting three obscure variables. The number of factors to be extracted was determined by evaluating the eigenvalue scores. Six factors wereextracted, accounting for 57.1% of the total variance. Reliability analysis was performed to refine the factors further. Using coefficient alpha, scores of .84 to .65 were obtained. Individual-item analysis indicated that all statements in each of the factors should remain. On 26 attributes of 31 critical service quality attributes, there were gaps between actual patient's importance of need criteria and nurse perceptions of them. Those critical attributes could be classified into four categories based on the relative importance of need criteria and perceived performance from the perspective of patient. This analysis is useful in developing strategic plans for performance improvement. (1) top priorities(high importance and low performance) (in this study)- more health-related information -accuracy in billing - quality of food - appointments at my convenience - information about tests and treatments - prompt service of business office -adequacy of accommodations(elevators, etc) (2) current strengths(high importance and high performance) (3)unnecessary strengths(low importance and high performance) (4) low priorities(low importance and low performance) While 26 service quality attributes of SERPERF model were significantly related to patient satisfation, only 13 attributes of SERVQUAL model were significantly related. This result suggested that only experience-based norms(SERVPERF model) were more appropriate than expectations to serve as a benchmark against which service experiences were compared(SERVQUAL model). However, it must be noted that the degree of association to overall satisfaction was not consistent. There were some gaps between nurse percetions and patient perception of medical service performance. From the patient's viewpoint, "personal likability", "technical skill/trust", and "cares about me" were most significant positioning factors that contributed patient satisfaction. DISCUSSION This study shows that there are inconsistencies between nurse perceptions and patient perceptions of medical service attributes. Also, for service quality improvement, it is most important for nurses to understand what satisfiers, hygiene factors, and performance factors are through two-way communications. Patient satisfaction should be measured, and problems identified should be resolved for survival in intense competitive market conditions. Hence, patient satisfaction monitoring is now becoming a standard marketing tool for healthcare providers and its role is expected to increase.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.361-367
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• 2019

This exploratory study investigated the real time adaptability of industry training for college professors. For this study, the researcher whose college's professor in the department of early childhood education used collaborative action research. This research was conducted in a class of five year old children of a daycare center in D city. The researcher and the homeroom teacher interacted with each other through 4 steps during the researcher's training period. The first step was group formation between the researcher and the teacher. The second step was problem identification. The third step was a review of the literature. The forth step was problem-solving. The researcher and teacher finally developed a rhythm movement program that was based on fundamental motor performance of young children. Through this collaborative effort, the researcher and teacher had the opportunity to improve their professionalism. Especially, the researcher improved her understanding and knowledge of teaching young kids. The result of this study is meaningful in that it provided basic data to improve training of college professors.