• Journal of Korean Library and Information Science Society
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• v.28
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• pp.93-135
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• 1998

The purpose of this study was to compare the effectiveness of programmed instruction versus lecture and discussion method on the knowledge of basic reference sources among undergraduate library and information science students. The hypotheses of the study were: 1. Programmed instruction will be more effective than the lecture/discussion method with regard to academic achievement. 2. There will be a significant difference in learning time between the experimental and the control groups. Seventy-eight library and information science students were participated m the study from the two universities in Chungchong Province. A programmed instruction manual, including 4-types of reference sources-dictionary, encyclopaedia, bibliography, indexes and abstracts, 40-item multiple choice post-test, and a questionnaire for the students' attitude toward programmed instruction were developed specifically for this research. The post-test only control-group design was selected for this experimental study. Students were given instruction on the specific reference titles in dictionary, encyclopedia, bibliography, indexes and abstracts. The control group was instructed by the lecture and discussion method while the experimental group completed a programmed instruction manual by themselves. Both the control and the experimental group were tested right after the instruction of 4-types of reference sources. In addition, a questionnaire asking students' attitude toward programmed instruction was administered to the experimental group. The findings from this study are summarized as follows: 1. The results showed that there were no significant difference in the mean of the post test score between the two groups. Therefore, programmed instruction is viable as an alternative method of instruction in the teaching of reference sources. 2. There was a significant difference in the mean of time spending for the leaning of bibliography, indexes and abstracts between the two groups. Accordingly, programmed instruction proved to be more efficient than the conventional lecture/discussion method in terms of learning time. 3. Students showed positive response to programmed instruction and evaluated it very interesting and challenging. In conclusion, the programmed instruction method was just as effective as the lecture/discussion method in the teaching of reference sources. And students' attitude toward the programmed instruction was favorable enough to secure a continued use of this method for the teaching of reference sources.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.529-535
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• 2023

Noise originating from incipient propeller cavitation is assumed to come from a limited number of sources emitting a broadband signal. Conventional methods for cavitation localization have limitations because they cannot distinguish adjacent sound sources effectively due to low accuracy and resolution. On the other hand, sparse Bayesian learning technique demonstrates high-resolution restoration performance for sparse signals and offers greater resolution compared to conventional cavitation localization methods. In this paper, an incipient propeller cavitation localization method using sparse Bayesian learning is proposed and shown to be superior to the conventional method in terms of accuracy and resolution through experimental data from a model ship.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.61-68
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• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• Journal of Korean Library and Information Science Society
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• v.42 no.3
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• pp.229-249
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• 2011

This study analyses the relationship between learning style and library skills. The components of learning style and library skills were extracted through literature review. The relationship between learning style and library skills was verified by using T-test. The results indicate that there was Sub-variables of library skills such as 'decimal classification', 'using reference sources', and 'recognizing the parts of a book' had a significant relationship with global variables of learning style. Thus, when the user education for each type of learning style will be considered.

• Journal of the Korean Geographical Society
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• v.36 no.5
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• pp.539-560
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• 2001

This paper aims to critically review the geographical literature on learning and proximity that stresses the role of the regions and geographical proximity in sustaining competitive advantage, and to conceptualize a relational/organizational perspective on the sources of knowledge and learning in the firm. In the first part of the paper, I argue that the geographical literature lacks the deliberate scrutiny of how learning occurs in the firm and where the sources of knowledge and learning come from. Secondly, I attempt to elaborate the concept of proximity through a relational/organizational perspective. Thirdly, I delve into how learning takes place and is realized in the firm through communities in the firm such as communities of practice, epistemic communities and task-force teams and how such communities in the firm generate knowledge and sustain loaming by drawing on relational/organizational proximity. This paper concludes by claiming that the sources of learning exist in organizational spaces, with complex geographies mobilizing distributed knowledge and competences and combining varied forms of knowledge beyond the simple demarcation of tacit and codified knowledge.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Proceedings of the KIEE Conference
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• 1994.07b
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• pp.1540-1542
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• 1994

This paper describes an experimental study of pattern recognition of partial discharge for three different discharge sources by using neural network(NN) system. The NN system is three layer feedforward connections and its learning method is a backpropagation algorithm incorporating an external teacher signal. Input information for NN is a statistical parameters of a discharge magnitude and the number of pulse count. After learning three typical input patterns, NN system offers good discrimination between different defects.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1173-1177
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• 1990

Cerebellar Model Linear Associator Net(CMLAN), a kind of neuro-net based adaptive control function generator, was applied to the problem of direct inverse calibration of three and six d.o.f. POMA 560 robot. Since CMLAN autonomously maps and generalizes a desired system function via learning on the sampled input/output pair nodes, CMLAN allows no knowledge in system modeling and other error sources. The CMLAN based direct inverse calibration avoids the complex procedure of identifying various system parameters such as geometric(kinematic) or nongeometric(dynamic) ones and generates the corresponding desired compensated joint commands directly to each joint for given target commands in the world coordinate. The generated net outputs automatically handles the effect of unknown system parameters and dynamic error sources. On-line sequential learning on the prespecified sampled nodes requires only the measurement of the corresponding tool tip locations for three d.o.f. manipulator but location and orientation for six d.o.f. manipulator. The proposed calibration procedure can be applied to any robot.

• Journal of KIEE
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• v.10 no.1
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• pp.7-15
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• 2000

Source separation is a statistical method, the goal of which is to separate the linear instantaneous mixtures of statistically independent sources without resorting to any prior knowledge. This paper addresses a source separation algorithm which is able to separate the mixtures of sub- and super-Gaussian sources. The nonlinear function in the proposed algorithm is derived from the generalized Gaussian distribution that is a set of distributions parameterized by a real positive number (Gaussian exponent). Based on the relationship between the kurtosis and the Gaussian exponent, we present a simple and efficient way of selecting proper nonlinear functions for source separation. Useful behavior of the proposed method is demonstrated by computer simulations.