• Asian Journal of Innovation and Policy
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• v.11 no.1
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• pp.87-109
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• 2022

The study attempts to examine the system-building activities of scientific institutions in developing the Molecular Diagnostic (MDs) Innovation System in India. Scientific Institutions are the precursor of any technological development with their capabilities in generating new ideas. MDs are advanced and accurate diagnostic technology with considerable scope to serve the diagnostic needs and requirements of the healthcare system. We adopted a System framework and analyzed the development of MDs in terms of the Technological Innovation System (TIS) functions, and the systematic challenges are assessed through the System Failure Framework (SFF). Based on the secondary and primary survey of prominent science base actors, the study finds that the role of government is crucial for facilitating technological development within a science base through the mobilization of resources. In India, the MDs technological development gained significant momentum over the last decade with the development of specialized human resources and dedicated research institutes. However, we do find that the innovative capabilities in attaining need-based TIS are sub-optimal owning to the specific diagnostic needs of highly burdened diseases in the society. The system analysis reveals that the TIS functions are underperforming because of the absence of a well-defined funding mechanism and goal-oriented targeted policy regime of the government. Since MDs have a transformative effect on the present healthcare system, we argue that the government has to address the system-based challenges and issues for developing a need-based technological innovation system for MDs in the country.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.279-290
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• 2013

In digital forensics log files have been stored as a form of large data for the purpose of tracing users' past behaviors. It is difficult for investigators to manually analysis the large log data without clues. In this paper, we propose a text mining technique for extracting intrusion logs from a large log set to recommend reliable evidences to investigators. In the training stage, the proposed method extracts intrusion association words from a training log set by using Apriori algorithm after preprocessing and the probability of intrusion for association words are computed by combining support and confidence. Robinson's method of computing confidences for filtering spam mails is applied to extracting intrusion logs in the proposed method. As the results, the association word knowledge base is constructed by including the weights of the probability of intrusion for association words to improve the accuracy. In the test stage, the probability of intrusion logs and the probability of normal logs in a test log set are computed by Fisher's inverse chi-square classification algorithm based on the association word knowledge base respectively and intrusion logs are extracted from combining the results. Then, the intrusion logs are recommended to investigators. The proposed method uses a training method of clearly analyzing the meaning of data from an unstructured large log data. As the results, it complements the problem of reduction in accuracy caused by data ambiguity. In addition, the proposed method recommends intrusion logs by using Fisher's inverse chi-square classification algorithm. So, it reduces the rate of false positive(FP) and decreases in laborious effort to extract evidences manually.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.153-169
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• 2020

A new way of shopping based on virtual assistant, so called voice shopping, is drawing attention. The voice shopping market is growing around the world, and Korea is on the verge of full-scale commercialization of this new shopping. For the development of voice shopping-related industries, it is necessary to research on specific issues related to this new shopping methods, such as the quality of services, efficient processes tailored to new ways, and ways to build customer relationships. As part of such an attempt, the study seeks to determine the factors that affect consumers' perception and attitudes toward voice shopping. The study conducted the analysis based on survey response data of 171 online shopping users. In addition to the typical factors of the technology acceptability model(TAM) such as perceived usefulness and ease of use, the impact of perceived playfulness was included for analyzing the intention on the acceptance of voice shopping. In particular, this study focuses on the impact of user attributes. For the spread of voice shopping, it is necessary to set up a valid target customer and understand users for establishing an effective customer relationship. Therefore, this study tries to analyze how the perceptions on the voice shopping(perceived usefulness, ease of use, and perceived playfulness) are affected by users' attributes, such as user innovativeness and user knowledge level. The result of analysis shows that user innovativeness have a positive relationship with all of perceived usefulness, ease of use, and perceived playfulness. The user knowledge base, however, was not significant to all these three variables. The user knowledge base is shown to have a positive effect on user innovativeness which is the source of positively significant factor for the variable of the perceptions on the voice shopping. Meanwhile, among the variables of extended technology acceptance model, perceived usefulness and perceived playfulness have positive effects on the acceptance of voice shopping, while ease of use has no significant impact on the voice shopping acceptance. Ease of use has a positive relationship with perceived usefulness and playfulness. This study is meaningful in providing implications on the development of voice shopping platforms and related services, and establishment of customer relationship.

• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.437-448
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• 2008

As the importance of knowledge is emphasized and the environment of battlefields is changing, the military also demands competent people equipped with creativity, cooperativeness and communication ability, and in this situation it is required to apply PBL to education in the navy. The present study went through three stages in order to develop a prototype to implement a naval e PBL support system for knowledge creation. First, databases in Korea Education and Research Information Service, National Assembly Library, etc. were searched using keywords such as PBL, e-PBL, knowledge creation and knowledge ecosystem. In addition, we selected and analyzed frequently quoted literature and recent research reports related to this study among domestic and foreign theses, books, research papers, etc. recommended by specialists in contents, and derived the key values of a knowledge creation type e-PBL support system and design strategies. Second, we developed a primary prototype based on the contents of analysis and, revising it according to teaching design specialists' opinions, we proposed the final prototype of knowledge creation type naval e PBL support system and it has values as follows. First, the knowledge creation type naval e PBL support system provides learners with opportunities to apply e PBL and helps them improve their creativity, cooperativeness and communication ability and accumulate know how of services. Second, it improves work efficiency by circulating knowledge through sharing among individuals or groups, and produces synergy that promotes the organizational culture of learning. Third, the knowledge creation type naval e-PBL support system enables teachers who apply PBL to school education to find new applications of PBL in constructing knowledge bases.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.111-127
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• 2004

In this paper we describe an application of the lexical resource JurWordNet and of the Core Legal Ontology as a descriptive vocabulary for modeling legal domains. It can be viewed as the semantic component of a global standardisation framework for digital governments. A content description model provides a repository of structured knowledge aimed at supporting the semantic interoperability between sectors of Public Administration and the communication processes towards citizen. Specific conceptual models built from this base will act as a cognitive interface able to cope with specific digital government issues and to improve the interaction between citizen and Public Bodies. As a Case study, the representation of the click-on licences for re-using Public Sector Information is presented.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.1
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• pp.97-103
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• 2009

The process design of forward Extrusion and Upsetting of Axi-symmetric part has been studied in this paper. During the cold forging product; auto transmission Solenoid Valve part, the defects such as folding and under-fill can be appeared by the improperly controlled metal flow. In this study, to reduce the folding and under-fill the design of experiments has been used to find out the significant design variables in the design of forging process. This paper deals with an Process Planning with which designer can determine operation sequences even after only a little experience in Process Planning of Multi-Former products by multi-stage former working. The approach is based on knowledge-based rules, and a process knowledge-base consisting of design rules is built. Based on the systematic procedure of process sequence design, the forming operation of cold forged auto transmission Solenoid Valve part is analyzed by the commercial Finite Element program, DEFORM/2D.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.296-301
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• 1998

The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.337-340
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• 1991

The most difficult, time-consuming, and expensive task in building an ES (Expert System) is constructing and debugging its knowledge base. Our goals are to eliminate the knowledge-acquisition bottle-neck for ES creation in data rich situations and to make an ANN (Artificial Neural Network) model behave as much as possible like an ES. The ANN ES has many benifits: Once it has been learned, inference time is very short. It can provide a reasonable conclusion for insufficient input data. But it has also several demerits : Learning time is too long to converge. We cannot guarantee the convergence of its weights. We introduce an ANN ES model which makes most of its benefits and compensates its shortcomings.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.123-137
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• 2007

The information space of semantic web comprised of various resources, properties, and relationships is more complex than that of WWW comprised of just documents and hyperlinks. Therefore, ranking methods in the semantic web should be modified to reflect the complexity of the information space. In this paper we propose a method of ranking query results from RDF(Resource Description Framework) knowledge bases. The ranking criterion is the importance of a resource computed based on the link structure of the RDF graph. Our method is expected to solve a few problems in the prior research including the Tightly-Knit Community Effect. We illustrate our methods using examples and discuss directions for future research.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.1
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.