• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.120-121
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• 2008

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.118-128
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• 2018

In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.591-601
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• 2021

This study examined the effect of branding capability on business performance moderated by learning capability. This study was conducted with small- and medium-sized enterprises (SMEs) of coffee cafes in the ex-Besuki region, East Java, Indonesia, covering four regencies located around coffee-producing areas with geographical indication (GI) certification. 150 managers of coffee cafe were sampled using the census technique. Data were collected by questionnaires distributed to the coffee cafe managers. The data were then analyzed by using simple regression analysis, Moderation Regression Analysis (MRA) and Moderated MultiGroup Analysis (MMA). The results showed that learning capability positively and significantly affect business performance, and learning capability moderated/enhanced the effect of branding capability on business performance. The findings of this study suggest that branding capability and learning capability play a crucial role in the performance of coffee cafe business especially in the dynamic environment. Coffee cafe managers need to take concrete steps to improve their branding capability and learning capability and they also need to improve their ability to interact with their environment and be committed in managing the coffee cafe. Therefore, it is imperative that the role of branding capability and learning capability be optimized in order to improve the business performance of the coffee cafe.

• Journal of the Korean Institute of Educational Facilities
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• v.27 no.4
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• pp.3-10
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• 2020

Not only the education that follows the school curriculum but also the education that cultivates practical abilities, such as creative experiential activity, is becoming more and more important in the elementary education in order to accept the social demand about the change in the educational environment. Among these movements, 'village-coupled school' has been carried forward as a policy of the Seoul Metropolitan Office of Education since 2016, and it is operating various programs to establish the educational cooperation system of schools and villages. Particularly, each office of education is publishing the 'village resource sourcebook' and introducing the student experiential programs of public facilities that are located at each region in order to enable the experiencing of experiential activities that utilized educational resources of the region. However, the standard or basis about the composition of the regional experiential learning curriculum is currently missing. Regarding the operation of the regional experiential learning, it seems that the school curriculum and associated facility experience or others would mutually increase the educational synergy effect. This research is a proposal to increase the educational effect of regional experiential learning. In the aspect of the experience at a public facility located in the region, it aims at analyzing the use, characteristic, and others of public facilities that appear in the elementary school textbook and propose the base data of the composition of the experiential activity program related to the curriculum.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.3
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• pp.459-472
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• 2006

This paper examines the implication of competition and collaboration in the innovation process for regional economic development in an increasingly knowledge-based economy. While competition is an important force in securing the competitive advantage of firms, collaboration between firms and organizations should be necessary for promoting the innovative capacity of a region. This study shows that collaboration relations based on trust and stability is important for the long-term development of learning and innovation in competitive environment, and the way how spatial proximity plays an important role in interactive learning processes. It also discusses the reason why the innovative networks facilitating the exchange of tacit knowledge should be embedded in region. Finally, the paper examines the possibility of the networks based on collaboration relationship in less-favored regions such as Chonbuk, and suggests the policy implication of the result for achieving regional innovation systems in the region successfully.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.203-212
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• 2016

The purpose of this study was to determine the effects of simulation practice education applying problem-based learning on problem-solving ability, critical thinking disposition, and learning satisfaction of nursing students. 63 nursing students taking the practice subject for integrated simulation at U University in K region were asked to complete a self-administered questionnaire to collect data before and after the simulation practice. The simulation practice education applying problem-based learning was effective in improving problem-solving ability and learning satisfaction significantly and positive correlation was found among problem-solving ability, critical thinking disposition, and learning satisfaction; that is, the better problem-solving ability, the higher level of critical thinking disposition and learning satisfaction. While the results of this study conducted in nursing students at a single university cannot be generalized, it was confirmed that simulation practice education applying problem-based learning was an effective teaching method in improving problem-solving ability and learning satisfaction of nursing students. It is therefore necessary to give simulation practice education applying problem-based learning on a systematic and continuous basis with the objective of improving problem-solving ability and learning satisfaction and promoting critical thinking disposition.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• Journal of Information Technology Services
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• v.9 no.4
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• pp.109-121
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• 2010

Most organizations adopt their own performance measurement systems. Those organizations select performance measures to meet their goals. Organizations can give only limited description of what performance measures are. Kaplan and Norton suggest that the Balanced Scorecard (BSC) to complement the conventional performance measures. The BSC can provide management system with a comprehensive strategic vision and integrates non-financial measures with financial measures. The BSC is widely used for measuring corporate performance. This paper investigates how the BSC-based performance measures can be applied to Learning City. The Learning City's performance measures and strategy map on the basis of the BSC are suggested in this research. This paper adopt the AR(assurance region)-DEA model which could limit the range of weight on performance measures to prevent each viewpoint of BSC from having unlimited elasticity. The proposed model is based on CCR model including a property of unit invariance to use the data without normalization process.

• ETRI Journal
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• v.44 no.2
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• pp.232-243
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• 2022

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.