A Comparative Study for the R&D supporting systems between Seoul National University and Arizona State University

학위논문 (석사)-- 서울대학교 대학원 : 행정대학원 행정학과, 2018. 2. 금현섭.Since its establishment in 1946, Seoul National University has played an important role in education and research in Korea for more than 70 years as a leading national university. Especially, it is required to play a more important role in research and development (R&D) with the advent of the knowledge-based economy. This study tries to explore R&D supporting systems of Seoul National University (SNU) which incorporated on December 28, 2011. In Seoul National University, there are concerns about future changes such as decrease of government grant which are derived from the incorporation might lead decline in R&D performance. Since current R&D supporting systems of Seoul National University is the extension of past systems which had existed before the incorporation, it is important to suggest future directions of R&D supporting systems preparing for the future changes of the universitys R&D environment. With regard to this issue, SNU might adopt several lessons from prominent state universities in the U.S. about their structure of organization, policies & programs, research fund, technology licensing & commercialization, and regulations of R&D management. Arizona State University (ASU), which is located in Arizona State, is one of the best state universities in the U.S. Arizona State University has achieved remarkable growth in the research after it became R1 University in 1994. The purpose of this study is to provide helpful suggestions to improve R&D supporting systems of Seoul National University by investigating various aspects of those in Seoul National University and Arizona State University. This thesis found several lessons from the comparison of the R&D supporting systems of Seoul National University and those of Arizona State University. First, the role redistribution of ORA (Office of Research Affairs) and R&DB Foundation may be considered to develop more efficient R&D management process. For example, ORA can be specialized to manage overall funded research process and R&DB Foundation can be specialized IP (Intellectual Property) management. In addition, it might be better for ORA to expand its roles for improving the quality of researches by referring OKED at ASU cases. Second, the current R&D supporting programs and policies at SNU looks like too much focusing on direct financial supports to the researchers. These programs have a positive role to attract prominent scholars into the university. However, it might be just consumption rather than a long term investment. Instead, it might be better to develop various R&D management programs and strategies such as fostering entrepreneurship or relationship management as ASU has done. Third, SNU is required to be largely engaged in the research fund management process including proposal process not just being limited in connecting research fund organizations and researchers. It that sense, various activities to enhance quality of a researchers proposals is required at SNU just as ASU is doing, which can bring more chances for funding competitions. Forth, technology licensing and commercialization management requires professional supports and strategic approaches, which includes specialization of R&DB Foundation on IP management and deciding selective fields of R&D investment which can bring substantial profit. In addition, technology licensing and commercialization management requires university-industry collaboration, therefore, for better performance, university should provide supporting services and useful information for industry as well as faculty. This means universities should try to attract industry to make further cooperation and investment. Fifth, SNU is required to change its systems for perspective of users not for managers. For example, R&D regulations at SNU are required to be briefly and concisely refined for the perspective of the user. It includes endeavors for integration of regulations and guidelines and elimination of duplicated or unnecessary regulations. Last but not least, current indirect expenses collecting rates and royalty distribution rates at SNU can be good incentives to the researchers. However, those rates might be reconsidered for more investments of the facilities and infrastructure to improve R&D supporting environment of the university. I insist that investments to R&D infrastructure are more important than monetary incentives for fostering fundamental R&D capacity of the university.Chapter I Introduction 1 Chapter II Research and Development (R&D) 4 1. Concept of R&D 4 2. R&D promotion strategies 4 3. University-industry R&D collaboration 5 4. R&D supports & performance of universities 6 5. Research questions 7 6. Selection of comparison criteria 7 7. Method for the research 8 Chapter III R&D capacity of Seoul National University and Arizona State University 10 1. Seoul National University 10 1.1. Research funding 10 1.2. Resources for R&D funding 10 1.3. Intellectual property & technology transfer 10 2. Arizona State University 10 1.1. Research expenditure 11 1.2. Resources for R&D funding 11 1.3. Intellectual property & technology transfer 11 3. Comparisons of R&D capacity between the universities 11 Chapter IV R&D supporting systems of Seoul National University and Arizona State University 13 1. Seoul National University 13 1.1. Organizations for R&D administration 13 1.2. Policies & programs for R&D support 15 1.3. Research fund management 18 1.4. Technology licensing and commercialization management 18 1.5. R&D regulations 19 2. Arizona State University 19 2.1. Organizations for R&D administration 20 2.2. Policies & programs for R&D support 21 2.3. Research fund management 23 2.4. Technology licensing and commercialization management 24 2.5. R&D regulations 25 3. Summary of R&D supporting systems 26 3.1. Organizations for R&D administration 26 3.2. Policies & programs for R&D support 27 3.3. Research fund management 27 3.4. Technology licensing and commercialization management 27 3.5. R&D regulations 27 Chapter V Lessons learned from the comparative Analysis of the R&D Supporting Systems 30 1. Organization for R&D administration 30 2. Policies & programs for R&D supports 30 3. Research fund management 31 4. Technology licensing and commercialization management 32 5. R&D regulations 32 Chapter VI Conclusions 33 Bibliography 35 Apendix 38Maste

혼잡환경에서의 단순성의 대가 : 정보통신산업에서의 가격정책과 수익에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 산업공학과, 2013. 2. 박진우.In this dissertation, we consider pricing schemes and revenues in the telecommunication industry. Recently, broadband Internet subscribers are increasing exponentially. In 1998, domestic Internet users were 3.1 million and have increased to 30 million in 2003 among which the broadband users were 10 million. Despite the rapid increase in Internet demand, the prevailing pricing scheme, flat-rate pricing, has not been changed, which is inevitably pulling down the providers revenue to its limit. Moreover, under the situation called disparity in Internet usage where a small fraction of users generate most of the traffic, the flat-rate pricing made the users with small usage subsidize the ones with massive consumption. Due to the aforementioned problems, the network providers are recently considering to adopt usage-based pricing. However, this has brought a strong customer resistance and thus, most of the providers are keep using the flat pricing. In light of this, an interesting study related to pricing schemes and revenue in the network was conducted. In 2008 IEEE JSAC, one article argued that the revenue loss from using a simple entry fee in lieu of revenue-maximizing scheme is small. The authors defined this loss as the price of simplicity and showed mathematically that this price is low in a various environment. However, what they have overlooked is congestion, which is one of the crucial element in modeling the network environment. In effect, current Internet is a huge network connected with numerous users. In this context, a users net-utility from using the Internet consists of not only the pure utility of service and the price paid but also the congestion cost where the cost grows as the number of users increases. Thus, in this thesis, we reexamine the price of simplicity under the environment that exists "congestion externality." In particular, we compare revenues obtained using a flat price and two-part tariff and analyze the effect of congestion on the revenue loss when using a simple entry fee in lieu of the two-part tariff. Previous study has shown that when there is no delay disutility the revenue loss is small, which leads to a low "price of simplicity." However, in this study, we show that in an extreme case where all users are identical, the price of simplicity is substantial. Then we consider a more practical scenario where users have different preferences, and show that even in this case, under congestion externality, the price can be extremely high.1. Introduction 2. Internet Pricing 3. Paris Metro Pricing 4. Token Pricing 5. Price of Simplicity 6. Discussion 7. ConclusionsDocto

Resonance phenomena in the cortical network model of macaque monkey

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The Effect of management`s strategic choice on work group design and its effectiveness

학위논문(박사)--서울大學校 大學院 :經營學科 經營學專攻,1996.Docto

Nonlinear Dynamical Modeling in the Spontaneous Brain Activity

DoctorNonlinear dynamics have also received much attention in recent years for the purpose of investigating the underlying mechanism in the brain. They have been successful to explain and predict the dynamics of brain. The development of the analysis of nonlinear dynamical systems has provided a strong tool to neuroscience to understand the brain. In this thesis, we propose several of models constructed at different levels to investigate the emergent of function in the cortical networks in the context of nonlinear dynamics. We also demonstrate that building a large-scale brain network from the small blocks can be useful for understanding the brain activity. Recently, the dynamics of spontaneous brain activity have generated considerable recent research interest in the various neuroscience fields including the computational neuroscience. It is important to study the endogenous brain activity because experiment and modeling studies show that the activity reflects the underlying structure, which can help understanding the relationship between structural and functional network. So we apply the developed computational modeling with a large scale brain network to study the spontaneous brain activity. We develop the building blocks for the whole brain modeling. Using a compartment model of a non-uniform axon, the information processing in a signal neuron is studied. We find that the transmission efficiency in these axons has nonlinear mode-locking structure that depends on the relative ion channel density. The refractory period of the soma is found to be longer than that of its axon and this structure appears by it. Our study suggests that in addition to just conduct electrical signals, non-uniform axons can actively participate in information processing. Connecting the building elements, we study noise-induced dynamics of the cortical brain network of a macaque monkey in context with a large-scale network. We model each node of the network with a sub-network of interacting FitzHugh-Nagumo (FHN) neurons. Recently, the study of comparing between anatomical and functional networks using a large-scale brain model has received considerable attention. The main goal of these studies is that how functional networks are self-organized and emerged from the underlying structures. In this study, we mainly focus on the role of noise intensity in forming a functional network, which is interrelated with the structural network. We find that the anatomical cortical brain network functions optimally at a certain noise intensity. We also study that the noise can induce the reconfiguration of the functional networks, which may affect the information processing in terms of the segregation and integration of information.In the last, we study the dynamics patterns in spontaneous neural activity, using the whole brain modeling includes a large-scale brain network. Functional MRI studies show that the spontaneous BOLD signals during the resting have the characteristics of ultraslow rhythms ($<0.1$Hz) and topologically anticorrelated relationship between some cortical areas. Some modeling studies demonstrate that the brain activity during the resting state can be represented as a set of multiple attractors and their activity is organized by transitions between attractors. In this thesis, we propose a model to explain the particular pattern in the brain during the resting state. We adapt a simplified Wilson-Cowan model, which can describe the transition of each area from up (active) and down (quiescent) states using a combination of excitation and inhibition populations. Also we extend this model to a network model. Thus we extend our model to random networks and modular networks and find that the activity of network can be split into two anticorrelated clusters because of the motifs includes most inhibition-dominant links. Our model may provide the framework for the spontaneous brain activity and suggest that the spontaneous brain activity is represented as a transition between stable fixed points, constrained by the brain network. We find that our model can provide a framework to understand the spontaneous brain activity and hope that our study benefits for modeling the brain activity during the resting state

Computational modeling of the brain resting state activity based on the large-scale structure

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Organization Development Intervention Strategies in Worldwide Corporation

Today. it is very important issue that firms should take measures of responding to the changes of global marketplace. Organizations are growing and complex in order to manage worldwide operations. Management should choose appropriate strategic intents for across nations and cultures. Under this circumstance organization development intervention can play an important role in facilitating helping worldwide company manage operations. In this point of view. I examined management' strategic intents and organization development intervention strategies to implement them. First. I divided management' strategic intents on worldwide operations in to four types, international. global. transnational, and multinational ones, according to two dimensions such as global integrity and local responsibility. They are situational factors effecting on organization development intervention strategies. Second. I represent organization development intervention strategies fitting strategic intents. such as management team building, large group intervention. culture training, career development program, employee involvement, conflict management, ethnocentric/regioncentric/ polycentric/geocentric selection. reward system. and building corporate vision. mrd. because this study on organization dcvclopmnt intervention strategies of worldwide companies is beginning stage, more detailed field studies and more sophisticated theories should be continued to modify and compensate for the present propositions

The Characteristics of Illegal Labor Disputes in 2003 and Their Implications in the Behavioral Changes of Unions, Management, and Government

In Korea. 320 labor disputes occurred during 2003. And the approximately 20% of labor disputes were illegal. In this study, we examined the characteristics of labor disputes occurred in 2003. especially those of illegal labor disputes. The results of our analysis are as follows; First, the illegal disputes occurred in the firms that have large employees and old aged unions. Second, the illegal disputes were related to non-wage issues such as labor hours, business reengineering, and union recognition. Third, the rate of union's illegal behaviors like holding production utilities were higher than that in case of legal disputes. Fourth, unions, management. and government should change their traditional roles in order to overcome the illegal labor disputes

Effect of Clustering on Spiking Variability in heterogeneous Network

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