• Journal of Technology Innovation
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• v.5 no.1
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• pp.206-222
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• 1997

The conventional analysis with which justifies government intervention of the private sector's innovation activities is the market failure approach. According to such analysis, fund allocation through autonomous market mechanisms is not optimal in technology financing because of the disparity between the desirable level of investment for society as a whole and that for private firms. To optimize the fund allocation, public policies such as subsidy, preferencial loan and venture capital investment programs are designed for technology development projects performed by private firms. They, however, have not been effective in increasing private investment for such projects. In most cases, it was found that little considerations given to the relationship between uncertainty embodied in technology development projects and each types of financing. With respect to optimizing fund allocation, technology development projects should be financed by different means according to their probability of success and the expected value of technology. Employing various theoretical models on financing decision-making we verify here that technology development projects to be supported by commercial banks or venture capital institutions is limited contingent upon levels of uncertainty adn expected value. Under the assumption that financial institutions are risk averse, loan or investment can be available only if the probability of success of the project is higher than the probability premium and the current market rate of interest. Therefore, the projects that have lower probability of success and/or small expected return are excluded from commercial loan or investment programs. However, the remaining projects, whose probability of success is low but with high expected return, may be applied under government subsidy programs. To achieve optimality of fund allocation and to activate technology financing, we conclude that there should be a systematic division of role among financial institutions including government commercial banks, and venture capital institutions.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.783-796
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• 2017

In the theory of probability, a Bernoulli trial is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted. In the successive games of scissors paper stone there exists the case of draw in each game. In this paper we are interested in the ultimate success probability of each participant and the expected number of the game till any one of the two has the ultimate victory. Using our results, we can calculate the ultimate winning probability of each player of the two players and the expected number of the game till any one of the two has the ultimate victory in any case whether there is draw or not in each game.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.342-357
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• 2014

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.143-152
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• 1999

In Shewhart control chart, the average run length(ARL) is calculated using the mean of a conventional geometric distribution(CGD) assuming a sequence of identical and independent Bernoulli trials. In this, the success probability of CGB is the probability that any point exceeds the control limits. When the process is in-control state, there is no problem in the above assumption since the probability that any point exceeds the control limits does not change if the in-control state continues. However, if the out-of-control state begins and continues during the process, the probability of exceeding the control limits may take two forms. First, once the out-of-control state begins with exceeding probability p, it continues with the same exceeding probability p. Second, after the out-of-control state begins, the exceeding probabilities may very according to some pattern. In the first case, ARL is the mean of CGD with success probability p as usual. But in the second case, the assumption of a sequence of identical and independent Bernoulli trials is invalid and we can not use the mean of CGD as ARL. This paper concentrate on that point. By adopting one generalized binomial distribution(GBD) model that allows correlated Bernoulli trials, generalized geometric distribution(GGD) is defined and its mean is derived to find an alternative ARL when the process is in out-of-control state and the exceeding probabilities take the second form mentioned in the above. Small-scale simulation is performed to show how an alternative ARL works.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.263-269
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• 1996

The somplest approximate confidence interval for the probability of success is the one based on the normal approximation to the binomial distribution, It is widely used in the introductory teaching, and various guidelines for its use with "large" sample have appeared in the literature. This paper suggests a guideline when to use it as an approximation to the exact confidence interval, and comparisons with existing guidelines are provided. provided.

• Journal of the Korean Mathematical Society
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• v.61 no.4
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• pp.693-711
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• 2024

A Markov-modulated Bernoulli process is a generalization of a Bernoulli process in which the success probability evolves over time according to a Markov chain. It has been widely applied in various disciplines for modeling and analysis of systems in random environments. This paper focuses on providing analytical characterizations of the Markovmodulated Bernoulli process by introducing key metrics, including success period, failure period, and cycle. We derive expressions for the distributions and the moments of these metrics in terms of the model parameters.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.183-191
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• 2005

Axiomatic design offers a scientific base for design in an efficient way. It is well known that it has two axioms: the Independence Axiom and the Information Axiom. Many applications of the Independence Axiom have been published, however, the Information Axiom has been mainly applied to IFR (functional requirement) - 1DP (design parameter) problems except fer a few case studies. This research presents various methods for calculation of information content. Generally, the information content is evaluated by the probability of success. The probability of success is calculated in two ranges: the FR range and the DP range. In the FR range, the graphical method is utilized with uniform distribution of the DP. In the FP range, the integration method is employed. It is noted that any distribution function of the DP can be accommodated in the integration method. The developed method can be applied to a decoupled design with multiple FRs and DPs. The developed method is extended to a coupled design and a design with a hierarchical structure of axiomatic design.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.107-120
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• 2022

The purpose of this study is to analyze the net effect and the combined effect of the determinants of the likelihood of start-up success of researchers at public research institutes. Based on the existing literature, the determinants of the researcher's likelihood of start-up success were reviewed, and a conceptual relationship between the determinants of the likelihood of start-up success was established. Data collection was conducted through a survey targeting researchers at public research institutes, and a total of 114 data were collected. The partial least squares (PLS) analysis method was used to analyze the net effect of the likelihood of start-up success determinant, and the fuzzy-set qualitative comparative analysis (fsQCA) was used to analyze the combined effect of the likelihood of start-up success determinant. In the PLS analysis results, it was found that technology commercialization probability and creative self-efficacy had a significant positive effect independently on the likelihood of start-up success. In the fsQCA results, we found a combined effect of increasing the likelihood of start-up success when the technology commercialization probability, technology commercialization capability, and creative self-efficacy were high. These research results provide academic implications for understanding the determinants of the likelihood of start-up success of researchers in public research institutes.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.752-758
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• 2011

In this paper, we perform a research on a message delivery link based on visible light communication using illumination light-emitting diodes (LEDs) for car-to-car communications and the link transmission success probability is analyzed for the link. The message delivery system is modeled and the signal-to-noise ratio is calculated from the received optical power. Then, the link transmission probability is estimated from the calculated bit error rates (BERs). The message delivery system has optical links from an LED transmitter near the rear lamp of a car ahead to a receiver near the headlamp of a car behind, whose positions are assumed to follow the normal Gaussian distribution. The link transmission success probability is calculated considering the physical characteristics of the optical link. The car positions are generated according to the normal distribution and the bit error rates are calculated for all links. The link transmission success probability is defined. For the unoptimized optical car-to-car message delivery links, it is shown that the link transmission success probability is larger than 0.9 with the transmitted optical power of 400 mW and the semi-angle at half power of 30 degree.