• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.25-37
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• 2017

The purpose of this study is to analyze the effects on particulate concept formation based on abductive reasoning model for elementary science class. For this study, an author selected two groups in the sixth grade. One group is an ordinary textbook-based control group (N=26) and the other group is an abductive reasoning model-based treatment group (N=26). After twelve lessons, the scores of Concepts Test for Gas were analyzed by t-test and two-way ANOVA. The result of t-test showed both the control and treatment groups have higher score than before they take the lesson. But after the lesson, an author found out that the treatment group had higher score than that of the control group. And compared to the number of particles expressed, the number of the treatment group were higher than that of the control class. The two-way ANOVA result revealed that the interaction effect between their cognitive level and treatment was not significant. And regardless of the level of cognition, the scores of treatment group are higher than those of control group. Therefore, abductive reasoning model-based elementary science class were found to be more effective for particulate concept formation. Based on the results, an author concluded that abductive reasoning model is very effective in teaching particulate concepts to elementary students.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1701-1707
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• 2010

This study is intended for implementing the system environment that can help integrate and retrieve various types of data in real-time by providing semantic interoperability among distributed heterogeneous information systems. The semantic interoperability is made possible by providing a TMDR(Topicmaps Metadata Registry), a set of ontologies. TMDR, which has been made by combining MDR(MetaData Registry) and TopicMaps and storing them in the database, is able to generate distributed query and provide efficient knowledge. MDR is a metadata management technique for distributed data management. TopicMaps is an ontology representation technique that takes into consideration the hierarchy and association for accessing knowledge data. We have created TMDR, a kind of ontology, that is fit for any system and able to detect and resolve semantic conflicts on the level of data and schema. With this system we propose a query-processing technique to integrate and access heterogeneous information sources. Unlike existing retrieval methods this makes possible efficient retrieval and reasoning by providing association focusing on subjects.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.9-17
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• 2009

A face database has contained a large amount of facial information data since face recognition was widely used. With the increase of facial information data, the face recognition based on distributed processing method has been noticed as a major topic. In existing studies, there were lack of discussion about the transferring method for large data. So, we proposed a fuzzy logic-based bit compression rate selection method for distributed face recognition. The proposed method selects an effective bit compression rate by fuzzy inference based on face recognition rate, processing time for recognition, and transferred bit length. And, we compared the facial recognition rate and the recognition time of the proposed method to those of facial information data with no compression and fixed bit compression rates. Experimental results demonstrates that the proposed method can reduce processing time for face recognition with a reasonable recognition rate.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.1-16
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• 2021

The purpose of this study was to investigate the needs of middle school students for the practical reasoning in a distance learning environment, to verify the needs differences based on the learner's personal characteristics, student-teacher interaction, and student-student interaction, and to investigate the relationships among student-teacher interaction, voluntary participation of students, and the students' perception of the extent to which practical reasoning is implemented in distance learning. For this purpose, 1,842 middle school students from seven schools in Gyeonggi, Daejeon, Chungbuk, and Sejong areas were surveyed online to investigate the importance of the practical reasoning questions and the how much practical reasoning is implemented in current distance learning. Among them, 1,095 responses were used for final analysis and descriptive statistics, independent sample t-test, one-way ANOVA, and path analysis were conducted. As a result of the study, first, middle school students acknowledged that the practical reasoning was important with the importance average 3.76. Based on the locus for focus model, the priorities of the needs in home economics class were examined, and the values and importance of the problem, and the ramification of the solution were considered to be of high priority. Second, characteristics of middle school students, student-teacher interaction and student-student interaction were found to have positive influence on needs for practical reasoning, while no difference were found by gender or voluntary participation in distance learning. Third, the voluntary participation of students and the student-teacher interaction in distance learning had a positive (+) significant effect on perceived implementation of practical reasoning, yet negative (-) significant effect on needs for practical reasoning.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.349-360
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• 2006

PROC MIXED in SAS can be utilized to make inferences on parameters in a mixed model by use of Restricted Maximum Likelihood Estimation Method or Maximum Likelihood Estimation Method which has more merits than ANOVA method. A regression model with unbalanced nested error structure that belongs to a mixed model is used to construct confidence intervals on variances among groups, within groups, and regression coefficients in the model. PROC MIXED is applied to three different sample sizes for simulation. As a result of the simulation study, PROC MIXED generates confidence intervals on parameters that maintain the stated confidence coefficient in a large sample size. However, it does not generate confidence intervals that maintain the stated confidence coefficient for variance components among groups and intercept in a small sample size.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.293-305
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• 2011

The volatility is one of most important parameters in the areas of pricing of financial derivatives an measuring risks arising from a sudden change of economic circumstance. We propose a Bayesian approach to estimate the volatility varying with time under a linear model with ARMA(p, q)-GARCH(r, s) errors. This Bayesian estimate of the volatility is compared with the ML estimate. We also present the probability of existence of the unit root in the GARCH model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.206-218
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• 2015

This single-blinded, nonequivalent control group pretest-posttest study was undertaken to evaluate the effectiveness of simulation education on clinical judgement, collaboration, communication skills, and perceived education practices and simulation design characteristics among student nurses in Korea. Participants were 47 students (19 in the experimental group and 28 in the control group) recruited by convenience sampling. The simulation based clinical reasoning education consisted of seven weekly, 120-minute high fidelity simulations. All participants completed the pretest and 7-week post measurements of a clinical judgment, collaboration, and communication skills with 4-week post measurement of collaboration, and participants in the experimental group provided a measurements of perceived education practices and simulation design characteristics. Data were analyzed using repeated measured ANOVA, and mixed linear model with SAS 9.2. Significant improvements were found in the experimental group for clinical judgment, collaboration, communication skill, and perceived education practices and simulation design characteristics. The study results show the impact of the perceived education practices and simulation design characteristics on facilitating the effectiveness of simulation education. The findings suggest a feasible and sound teaching method for student nurses and the need for further studies with a larger sample.