• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.400-407
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• 2005

Reverse engineering is defined as the process where the internal structures and dynamics of a given system are inferred and analyzed from external observations and relevant knowledge. The first part of this paper surveys existing techniques for biosystem reverse engineering. Network structure inference techniques such as Correlation Matrix Construction (CMC), Boolean network and Bayesian network-based methods are explained. After the numeric and logical simulation techniques are briefly described, several representative working software tools were introduced. The second part presents our component-based software architecture for biosystem reverse engineering. After three design principles are established, a loosely coupled federation architecture consisting of 11 autonomous components is proposed along with their respective functions.

• Journal of Families and Better Life
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• v.27 no.3
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• pp.45-60
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• 2009

This study reviewed the influence of consumer innovativeness and consumer knowledge on external information search behavior for the purchase of high-tech products. The strategies for offering information effectively for high-tech products may be proposed as follows based on the results of this study. First, the advertisements of high-tech products which are generally encountered need to offer sufficient information about the products in addition to fulfilling the function of persuading consumers. Second, it is necessary to implement an information network so that neutral offerings of information about high-tech products may be made promptly by reliable institutions. Third, information about high-tech products should be offered with easier explanations so that there should be no information-underprivileged class in utilizing the function of high-tech products.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.79-85
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• 2007

Intelligent machines respond to external environments on the basis of decisions that are made by sensing the changes in the environment and analyzing the obtained information. This study focuses on the construction of a knowledge base which enables decision making with that information. Approximately 70% of all errors that occur in machine tools are caused by thermal error. In order to proactive deal with these errors, a system which measures the temperature of each part and predicts and compensates the displacement of each axis has been developed. The system was built in an open type controller to enable machine tools to measure temperature changes and compensate the displacement. The construction of a machining knowledge base is important for the implementation of intelligent machine tools, and is expected to be applicable to the network based intelligent machine tools which look set to appear sooner or later.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.401-403
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• 1996

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, one often bas only a small number of state measurements. This could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network is employed to solve this kind of problem. The neural network serves as a compensator without a prior knowledge about the system. The proposed control algorithm is applied to a class of uncertain nonlinear system. The robustness against parameter uncertainty, nonlinearity and external disturbances, and the effectiveness is verified by the simulation results.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.209-215
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• 2022

This paper proposes a novel approach for training an unsupervised depth estimation algorithm. The objective of unsupervised depth estimation is to estimate pixel-wise distances from camera without external supervision. While most previous works focus on model architectures, loss functions, and masking methods for considering dynamic objects, this paper focuses on the training framework to effectively use depth cue. The main loss function of unsupervised depth estimation algorithms is known as the photometric error. In this paper, we claim that direct depth cue is more effective than the photometric error. To obtain the direct depth cue, we adopt the technique of knowledge distillation which is a teacher-student learning framework. We train a teacher network based on a previous unsupervised method, and its depth predictions are utilized as pseudo labels. The pseudo labels are employed to train a student network. In experiments, our proposed algorithm shows a comparable performance with the state-of-the-art algorithm, and we demonstrate that our teacher-student framework is effective in the problem of unsupervised depth estimation.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.3
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• pp.393-412
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• 2015

The purpose of this study is to suggest development plan of the Regional knowledge information services through the case study analysis between Korea and Japan public libraries. For this purpose, we analyzed literature related to the Regional knowledge information services, performed case study, and carried out a investigation which focuses on a public libraries webpage. According to the analysis, the development plan for the Regional knowledge information services was suggested as follow; that is user-friendly service, internal cooperation, external network cooperative strategy, visible public relations and recognition improvement. This study has aimed to prepare for the basic data which will help Korea public libraries to make better Regional knowledge information services in the future.

• International Area Studies Review
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• v.12 no.3
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• pp.243-267
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• 2008

This research develops a theoretical model of the influence of firm's internal and external characteristics on foreign knowledge learning and international performance. To verify research hypotheses, 222 cases were collected as samples and the data was tested by structural equation modeling analysis. The result of the analysis shows that the CEO's foreign experience, entrepreneurship, technological innovation and use of foreign network have positive influence on the international performance. Besides CEO's foreign experience and use of foreign network affects the foreign knowledge learning, which shows the useful relationship of international performance and accordingly it is proved that the foreign knowledge learning has mediating effect between the firm's internal and external characteristics and international performance.

• International Commerce and Information Review
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• v.18 no.3
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• pp.205-226
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• 2016

As knowledge workers have increased and information interchanges become more diverse, only the resources of internal enterprise is not longer useful, and the practical use of external information through information public sharing network is even more crucial. But, few studies that use both internal and external information sharing systems together have been conducted. In this research, we carried on the depth interview to analyze the main factors that intend to utilize outside information as well as to share inside information voluntarily. In conclusion, sharing internal information is directly involved with the performance of their job exchanging simple data, past documents, and technical materials. On the other hand, using the external information belonged to several other organizations is mostly caused by the voluntary participants' satisfaction sharing their individual experience through the network systems. Based on the study, we propose the idea that can activate both internal and external information sharing network through the system with user feedback function regarding the customized information, and with the space expansion where people can freely raise their private opinion. We also introduce the plan of the construction of communication systems with various experts, and the development of compatible devices with diverse interfaces.

• The Journal of Economics, Marketing and Management
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• v.8 no.1
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• pp.36-49
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• 2020

Purpose - This study investigates factors that affected the utilization of trade support policies and further analyzed how the utilization of trade support policies affected export performance. Research design, data, and methodology - With a sample of 223 small and medium-sized export firms from South Korea, this study examines the determinants of the utilization level of trade support system such as export market orientation, learning orientation, network capability and environmental uncertainty by regression analysis. Results - Export market orientation have a positive effect on the utilization of the trade support system and there is positive relationship between learning orientation and the utilization of trade support system. And network capabilities have had a positive impact on the utilization of the trade support system but there is no relationship between environmental uncertainty and the utilization of trade support system. The utilization of the trade support system had a positive effect on export performance. Conclusions - The internal and external factors of the organization have affected small and medium-sized export firms use of trade support systems. The utilization of trade support system can enhance positive export performance by providing valuable information and resource to external knowledge and also to complementary resources from the external partners.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.91-100
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• 2020

Unlike the existing Visual Question Answering(VQA) problems, the new Visual Commonsense Reasoning(VCR) problems require deep common sense reasoning for answering questions: recognizing specific relationship between two objects in the image, presenting the rationale of the answer. In this paper, we propose a novel deep neural network model, KG_VCR, for VCR problems. In addition to make use of visual relations and contextual information between objects extracted from input data (images, natural language questions, and response lists), the KG_VCR also utilizes commonsense knowledge embedding extracted from an external knowledge base called ConceptNet. Specifically the proposed model employs a Graph Convolutional Neural Network(GCN) module to obtain commonsense knowledge embedding from the retrieved ConceptNet knowledge graph. By conducting a series of experiments with the VCR benchmark dataset, we show that the proposed KG_VCR model outperforms both the state of the art(SOTA) VQA model and the R2C VCR model.