• 대한임베디드공학회논문지
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• 제15권2호
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• pp.43-49
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• 2020

In this paper, we propose a stage-wise knowledge transfer method that uses block-wise retraining to transfer the useful knowledge of a pre-trained residual network (ResNet) in a teacher-student framework (TSF). First, multiple hint information transfer and block-wise supervised retraining of the information was alternatively performed between teacher and student ResNet models. Next, Softened output information-based knowledge transfer was additionally considered in the TSF. The results experimentally showed that the proposed method using multiple hint-based bottom-up knowledge transfer coupled with incremental block-wise retraining provided the improved student ResNet with higher accuracy than existing KD and hint-based knowledge transfer methods considered in this study.

• 컴퓨터교육학회논문지
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• 제18권6호
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• pp.71-79
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• 2015

클라우드와 빅네이터의 새로운 시대에서 필요한 데이터를 방대한 데이터 풀로부터 어떻게 찾아내고 활용하느냐는 매우 중요한 일이다. 이러한 빅데이터의 시대에는 무엇보다도 방대하고도 변화무쌍한 데이터를 잘 처리하고 유용한 정보를 신속하게 획득할 수 있는 진화된 형태의 효율적 지능적 지식시스템 설계를 필요로 한다. 따라서 본 연구에서는 진화된 지능 시스템 연구의 하나로서 구조적으로 재구성될 수 있는 동적 개인적 지식네트워크를 제안하고자 한다. 작은 공간에 큰 세계를 매핑하여 효율적으로 처리할 수 있는 인간 두뇌의 기능과 이 안에서 일어나는 뉴로다이나믹스 메커니즘에 착안하여 구조적 유연성을 갖는 지능 시스템을 설계하였다. 서로 다른 네트워크의 구조적-기능적 결합이 가능하도록 개인 지식네트워크를 구조화하고 핵심 영역에 속하는 공통 노드를 찾아 결합을 하며 재구성하는 기능을 부여하였다. 또한 시스템이 재구성된 지식네트워크로부터 최적 경로를 추출하며 추출된 경로를 가지고 추론 프로세스를 진행하는 기능 갖도록 구상하였다.

• 한국경영과학회지
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• 제19권2호
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• pp.129-151
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• 1994

Recently, Decision Support Systems (DSS) research has seen a more to combine Artificial Intelligence (AI) including neural network techniques with traditional DSS concepts and technologies to build an intelligent DSS or a knowledge-based DSS. This article proposes a Management Evaluation and its Support System (MESS) as a knowledge-based DSS. The management evaluation of a firm means the performance of all managerial operations is appraised by considering the situations of the firm. A neural network is used to represent the management evaluation structure as a suitable means of management knowledge representation. Finally a case study in a telecommunication corporation is presented.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.173-180
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• 2018

In this paper, we analyzed key keywords and research themes in the field of defense research using keyword network analysis and tried to grasp the whole knowledge structure. To do this, we extracted data from 2,165 research data from defense related research institutes from 2010 to 2017 and applied the Pareto rule to the number of abstracts of words and the number of links between words, We extracted a total of 2,303 words based on the criterion and extracted 204 final key words through component analysis. By analyzing the centrality and cohesiveness through these key words, we confirmed the concept of core research in the defense field and derived a total of 7 large groups and 16 small groups of each group in the knowledge structure of the defense area.

• 지식경영연구
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• 제22권4호
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• pp.41-70
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• 2021

소셜 미디어의 사용은 참여자들 간의 지식공유 활동, 사회 네트워크 형성, 다양한 사람들과의 소통기능 등과 같은 다양한 장점을 갖고 있다. 그러나 이러한 장점에도 불구하고 프라이버시와 사생활 침해, 피싱공격, 심리적 스트레스 등 다양한 부작용 또한 야기되고 있는데, 그 중 특히 소셜 네트워크 스트레스라는 새로운 개념의 스트레스가 주목받고 있다. 본 연구는 소셜 네트워크 스트레스의 개념을 정립하고, 소셜 미디어 환경에서 소셜 네트워크 스트레스가 전환행동에 미치는 영향을 살펴보려 한다. 이를 위해 본 연구에서는 소셜 네트워크 스트레스의 선행요인 및 결과요인으로 구성된 연구모델을 제시하고, 구조방정식 모델을 기반으로 하는 LISREL 8.7을 이용하여 연구모델을 실증적으로 검증하였다. 실증분석 결과, 소셜 미디어상에서 자기표출과 지식공유 활동은 소셜 네트워크 스트레스에 유의한 긍정적 영향을 주었으며 이는 소셜 미디어 전환의도에 유의한 영향을 미치는 것으로 나타났다. 끝으로 본 연구의 이론적, 실무적 시사점을 논의하였고 연구가 지닌 한계점을 제시하였다.

• 지식경영연구
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• 제17권4호
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• pp.77-104
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• 2016

The effective management of R&D employees is critical for a small or medium sized firm's sustainable growth. R&D employees have professional skills and choose expertise-oriented or management-oriented careers in the process of organizational socialization. This study synthetically verifies the direct and indirect effects of R&D employees' social network and self-esteem on their career orientation by organizational commitment based on social network theory and social recognition theory. The research model has been analyzed through structural equation modeling using survey responses from 220 R&D employees at small- and medium-sized firms in Korea. The analysis results show that internal network activities have direct and indirect impacts on organizational commitment and career orientation, but external network activities do not have significant effects on self-esteem, organizational commitment, or career orientation. There is no consensus in prior studies on whether expert orientation and management orientation are distinct concepts. In this study, these two types of orientation are verified as distinct concepts. It is also found that R&D employees' internal network activities are significant factors for a company's growth. A company should implement an educational system of roles and duties using which individuals can pursue career progression. In addition, it is necessary to provide career development programs such as job rotation, mentoring, and career counseling.

• Asian Journal of Innovation and Policy
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• 제4권2호
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• pp.242-262
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• 2015

The relationship between geographical proximity and academics' formal and informal knowledge-transfer activities in the network is analyzed with a mixed research method. With social network analysis as a basis, we have explored the networks between academics and firms in the 16 regions of South Korea. The result shows Seoul and Gyunggi are identified as central nodes, meaning that the academics in other regions tend to collaborate with firms in these regions. An econometric analysis is performed to confirm the localization of knowledge-transfer activities. The intensity of formal channels measured by the number of academic papers is negatively, but significantly associated with the geographical proximity. However, we have not found any significant relationship between the formality of the channels and geographical proximity. Possibly, the regional innovation systems in South Korea are neither big enough nor strong enough to show a localization effect.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• 한국멀티미디어학회논문지
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• 제13권6호
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.