• 지식경영연구
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• 제7권2호
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• pp.69-96
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• 2006

The purpose of this study is to find success factors of knowledge management in Korean firms, confirm them empirically, and verify their relative importance in terms of company size and industry type. The major studies on the knowledge management were deliberately selected and interpretively analyzed to find the success factors of Korean firms. As a result of the analysis, five success factors(top management's will, evaluation reward, organizational culture, knowledge management system, organizational structure) have been found. The empirical researches to make certain whether the above five factors derived are actually true or not have been separately performed by using questionnaire method. Based on the data collected, it is found that all five factors are significant. The degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure. In company size, large firm's degree of relative importance among the success factors are: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v) organizational structure. And medium-small firm's degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii) evaluation-reward, (iv)knowledge management system, (v)organizational structure. Finally, in type of industry, manufactural firm's degree of relative importance among the success factors of knowledge management in Korean firms has been found as: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure. And non-manufactural firm's degree of relative importance among the success factors of knowledge management in Korean firms are: (i)top management's will, (ii)organizational culture, (iii)evaluation-reward, (iv)knowledge management system, (v)organizational structure.

• 정보관리학회지
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• 제20권4호통권50호
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• pp.67-90
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• 2003

지식경영을 도입함에 있어서 조직 구성원들의 자발적인 지식 공유활동을 활성화시키기 위해서는 무엇보다도 구성원들의 동기유발 방식을 변화시킬 필요가 있다. 이를 위해 지식 기여에 대한 공정한 평가와 적절한 보상체계의 수립은 매우 중요하다. 이 연구에서는 지식경영을 도입한 국내 기업체 및 전문 연구기관에서의 지식 기여에 따른 평가와 보상과 관련한 문헌분석 및 실제 사례분석을 토대로 지식 기여에 대한 바람직한 평가 및 보상 모형, 즉 가설을 설정하였다. 아울러 설정된 가설을 토대로 한국원자력연구소를 대상으로 설문조사를 실시함으로써 전문 연구기관에서의 지식 기여에 대한 평가 및 보상에 대한 구성원들의 반응도를 조사 분석하였다. 본 연구의 분석결과는 지식경영을 도입하고자 하는 전문 연구기관에서 매우 유용하게 활용될 수 있을 것으로 기대된다.

• 한국초등과학교육학회지:초등과학교육
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• 제21권1호
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• pp.101-109
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• 2002

A cooperative teaming has been taken to consolidate the autonomous motivation of students and to develop a desirable attitude in a mutual cooperative atmosphere. Some studies on the reward effect showed that the reward after the evaluation, in the processes of cooperative learning, worked on students' learning motive directly, and the group reward was effective in learning attitude and the individual reward in academic achievement, respectively. Assuming that the group reward and the individual reward are organized and applied as a complex reward, the effects of rewards will appear, this study examined the effect of the complex reward on academic achievement and teaming attitude. For this study. 2 classes were randomly selected out of a elementary school in Gwangju and the teaming unit was based on chapter 4「The structure and function of plants」 in the 5-1 elementary Science textbook. This research has been done for 4 weeks after the students learned STAD for 8 weeks previously. The learning attitude was examined in pre and post tests, and the academic achievement was inspected twice at 2-week intervals after the pre test. The results were analysized by the SAS program In the case of academic achievement, both groups showed a significant improvement(p<.05). The experimental group showed no significant improvement in the first test, compared with the control group(p>.05), but after 4 weeks, it showed a significant improvement in the second test, compared with the control group(p<.05). From this result, it is identified that the reward should be done for a long time and the individual reward of the complex reward is successful in improving academic achievement. However, in the case of learning attitude, there was no meaningful difference in both groups(p>.05). But the control group showed a significant improvement, compared with the experimental group(p<.05). According to this result, it is indicated that the group reward only is more effective in improving learning attitude and complex reward can decrease the individual competition in experimental group.

• 지식경영연구
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• 제5권1호
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• pp.83-99
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• 2004

This research aims to verify individual and organizational factors that will affect knowledge sharing, especially in research & development organizations with a premise that knowledge sharing is one of the most important factors of successful knowledge management. Many factors which are supposed to affect knowledge sharing arc empirically investigated in this research : individual factors ((1)self esteem (2)expected contribution), organizational factors ((3)top management's will, (4)accessibility of knowledge management system (5)validity of evaluation & reward knowledge sharing, (6)trust, (7)open communication). As a result, it is found that 'expected contribution' among individual factors and 'open communication' and 'validity of evaluation & reward' among organizational factors are highly correlated with knowledge sharing. These findings suggest that enhancement of perceived expected contribution among employees and building cultures of open communication and effective evaluation & reward system for knowledge sharing will contribute to activation of knowledge sharing in the research and development organizations.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.169-178
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• 2019

Due to the characteristics of an ad hoc network without a control center, self-organization, and flexible topology, the trust evaluation of the nodes in the network is extremely difficult. Based on the analysis of ad hoc networks and the blockchain technology, a blockchain-based node-level trust evaluation model is proposed. The concepts of the node trust degree of the HASH list on the blockchain and the perfect reward and punishment mechanism are adopted to construct the node trust evaluation model of the ad hoc network. According to the needs of different applications the network security level can be dynamically adjusted through changes in the trust threshold. The simulation experiments demonstrate that ad-hoc on-demand distance vector(AODV) Routing protocol based on this model of multicast-AODV(MAODV) routing protocol shows a significant improvement in security compared with the traditional AODV and on-demand multipath distance vector(AOMDV) routing protocols.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.166-169
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• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• 한국컴퓨터정보학회논문지
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• 제29권1호
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• pp.11-19
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• 2024

멀티에이전트는 전장 교전 상황, 무인 운송 차량 등 다양한 실제 협동 환경에 사용될 수 있다. 전장 교전 상황에서는 도메인 정보의 제한으로 즉각적인 보상(Dense Reward) 설계의 어려움이 있어 명백한 희소 보상(Sparse Reward)으로 학습되는 상황을 고려해야 한다. 본 논문에서는 전장 교전 상황에서의 아군 에이전트 간 협업 가능성을 확인하며, 희소 보상 환경인 Multi-Robot Warehouse Environment(RWARE)를 활용하여 유사한 문제와 평가 기준을 정의하고, 강화학습 라이브러리인 Ray RLlib의 QMIX 알고리즘을 사용하여 학습 환경을 구성한다. 정의한 문제에 대해 QMIX의 Agent Network를 개선하고 Random Network Distillation(RND)을 적용한다. 이를 통해 에이전트의 부분 관측값에 대한 패턴과 시간 특징을 추출하고, 에이전트의 내적 보상(Intrinsic Reward)을 통해 희소 보상 경험 획득 개선이 가능함을 실험을 통해 확인한다.

• The Journal of Asian Finance, Economics and Business
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• 제7권4호
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• pp.275-286
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• 2020

The study explores factors that affect the performance of responsibility accounting at public universities in Vietnam. The study conducted a survey of 130, out of 154 public universities in Vietnam and uses regression analysis methods. The results identify eight groups of factors affecting the performance of responsibility accounting at these establishments of higher education: Division of the organization into responsibility centers, Estimation and Reality, Cost and income allocation, University autonomy, Management decentralization, Estimation, Reward, and Report. Costs and income allocation for responsibility centers, university autonomy, and the division of universities into responsibility centers are shown to significantly affect the performance of public universities in Vietnam. The results show that the performance of responsibility accounting at public universities in Vietnam is influenced by decentralization of management, division of the organization into responsibility centers, reward, cost and income allocation, estimation, evaluation of the results achieved between the estimation compared with reality, release of reports and university autonomy. Among the factors, cost and income allocation, university autonomy, division of the organization into responsibility centers, reward and evaluation of results achieved between estimation and reality have strong impacts. The study findings also indicate that the school council does not affect responsibility accounting at these universities.

• International Journal of Contents
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• 제6권4호
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• pp.30-34
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• 2010

Effective job scheduling scheme is a crucial part of complex heterogeneous distributed systems. Gang scheduling is a scheduling algorithm for grid systems that schedules related grid jobs to run simultaneously on servers in different local sites. In this paper, we address grid jobs (gangs) schedule modeling using Stochastic reward nets (SRNs), which is concerned for static and dynamic scheduling policies. SRN is an extension of Stochastic Petri Net (SPN) and provides compact modeling facilities for system analysis. Threshold queue is adopted to smooth the variations of performance measures. System throughput and response time are compared and analyzed by giving reward measures in SRNs.

• 전자공학회논문지A
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• 제32A권4호
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• pp.1-14
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• 1995

A new stochastic Petri nets model, called Stochastic Petri Nets Reward Model(SPNRM) is used for modeling and evaluating the performance of telecommunication protocols. We have developed a SPNRM of the TDX-10 Internal protocol, which has a packet data exchange facility between DCEs, Especially a timer and retransmission handling model is presented for error control of the data transmission phase. The stochastic Petri nets package(SPNP), a software package for SPNRM used in this paper, has been used to generate numerical results by analytical-numerical method rather than simulation. From the steady state solution of the net, it is possible to calculate automatically the performance measure of the protocol medeled with both end-to-end and link-by-link method, which are the mean response times and the throughputs.