• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.31-57
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• 1991

Korean firms of all sizes, from virtually every industry, have used and are using the term "excessive competition" to describe the state of their industry and to call for government interventions. Moreover, the Korean government has frequently responded to such calls in various ways favorable to the firms, such as controlling entry, curbing capacity investments, or allowing collusion. Despite such interventions' impact on the overall efficiency on the Korean economy as well as on the wealth distribution among diverse groups of economic agents, the term "excessive competition", the basis for the interventions, has so far escaped rigorous scrutiny. The objective of this paper is to clarify the notion of "excessive competition" and "over-investment" which usually accompanies "excessive competition", and to examine the circumstances under which they might occur. We first survey the cases where the terms are most widely used and proceed to examine those cases to determine if competition is indeed excessive, and if so, what causes "excessive competition". Our main concern deals with the case in which the firms must make investment decisions that involve large sunk costs while facing uncertain demand. In order to analyze this case, we developed a two period model of capacity precommitment and the ensuing competition. In the first period, oligopolistic firms make capacity investments that are irreversible. Demand is uncertain in period 1 and only the distribution is known. Thus, firms must make investment decisions under uncertainty. In the second period, demand is realized, and the firms compete with quantity under realized demand and capacity constraints. In the above setting, we find that there is "no over-investment," en ante, and there is "no excessive competition," ex post. As measured by the information available in period 1, expected return from investment of a firm is non-negative, overall industry capacity does not exceed the socially optimal level, and competition in the second period yields an outcome that gives each operating firm a non-negative second period profit. Thus, neither "excessive competition" nor "over-investment" is possible. This result will generally hold true if there is no externality and if the industry is not a natural monopoly. We also extend this result by examining a model in which the government is an active participant in the game with a well defined preference. Analysis of this model shows that over-investment arises if the government cannot credibly precommit itself to non-intervention when ex post idle capacity occurs, due to socio-political reasons. Firms invest in capacities that exceed socially optimal levels in this case because they correctly expect that the government will find it optimal for itself to intervene once over-investment and ensuing financial problems for the firms occur. Such planned over-investment and ensuing government intervention are the generic problems under the current system. These problems are expected to be repeated in many industries in years to come, causing a significant loss of welfare in the long run. As a remedy to this problem, we recommend a non-intervention policy by the government which creates and utilizes uncertainty. Based upon an argument which is essentially the same as that of Kreps and Wilson in the context of a chain-store game, we show that maintaining a consistent non-intervention policy will deter a planned over-investment by firms in the long run. We believe that the results obtained in this paper has a direct bearing on the public policies relating to many industries including the petrochemical industry that is currently in the center of heated debates.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.107-120
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• 2015

International logistics systems for cross-border trading aim not only to reduce costs and time, but also to maximize added value. For accuracy of shipping time, cost reduction, rapidity, and safety improvement, demand for Ro-Ro shipping as a global supply chain operating system has increased. To anticipate these demands, this study estimates the optimal demand and scale of Ro-Ro berth construction by analyzing shipping volumes between Korea and Japan. Based on Ro-Ro shipping speeds and distances between Korea and Japan, the shipping volume (container and general cargo) of Busan port is sorted into two separate zones. After analyzing the ratio of items that have great potential benefits from using Ro-Ro shipping, we estimate possible shipping volumes using Ro-Ro berth provision. The results reveal that two berths for 20,000t vessels are additionally required to accommodate the estimated volume. Results suggest the construction of an exclusive Ro-Ro berth in Busan port. This would not only accommodate the demand between Korea and Japan, but also provide a way to avoid the lean-to-one-side port policy.

• Journal of Information Technology Services
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• v.21 no.4
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• pp.91-103
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• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.201-210
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• 2012

Because sensing odor varies depending on each person, even if the odor is released in line with the legal emission permission concentration levels, it can still become a social issue if a civil complaint is made. The purpose of this research is to study the possibility of putting Mn-Cu metallic oxide catalysts into practical use to economically eliminate acetaldehyde which produces a odor in the industrial process. An optimal operating parameter to eliminate acetaldehyde was deduced through a performance evaluation in the research laboratory and the performance was verified by applying the parameter into an actual facility as an on-the-site experiment through a Scale-up of pilot size. The operating temperature of the metallic oxide catalysts researched so far was at the minimum close to $220^{\circ}C$, and the $220^{\circ}C$ elimination efficiency was 50% or below. However, having experimented by using a Mn-Cu metallic oxide catalyst in this research, optimum elimination efficiency showed when space velocity (GHSV) was equal to or below 6,000 $hr^{-1}$. The average elimination efficiency was 61.2% when the catalyst controlling temperature was $120^{\circ}C$, 93.3% when the catalyst controlling temperature was $160^{\circ}C$, and 94.9% when catalyst controlling temperature was $180^{\circ}C$, thereby reflecting high elimination efficiency. The specific surface area of the catalyst was $200m^2/g$ before use, however, was reduced to $47.162m^2/g$ after 24 months and therefore showed that despite the decrease in specific surface area as time passed, there was no significant influence on the performance. Having operated Mn-Cu metallic oxide catalyst systems for at least two years on a site where there was no inflow of toxins like sulfur compounds and acidic gases, we were able to confirm that elimination efficiency of at least 90% was maintained.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of Korea Technology Innovation Society
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• v.15 no.4
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• pp.881-915
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• 2012

These days, according as the role and function of science and technology become a important solving means of various national and social problems caused in knowledge information society as well as the development of national economy, the national target and mission of science and technology have been newly illuminated. The task of science and technology has embodied into national policies, and these policies acquired institutional foundation through the legal system related to science and technology. Recently, the discussions about the operating system of present science and technology legislation are often happened, if or not the legal system is optimal and appropriate itself under the changed environment. In special, some issues are raised continuously, for example, about the improvement of coordination system on S&T policies, and the governance system on national R&D programs, etc. This paper aims to research and suggest the reorganization method of science and technology law. For this purpose, the development and existing state of S&T legislation was investigated, the government role and policy driving direction were reviewed under the recent changed environment, and the problems of S&T law in structure and content were analyzed in variety of perspectives. On this basis, the reorganization methods of science and technology law are suggested.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.243-254
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• 2019

With the recent accelerated policy-making and interests in new renewable energy, plans to develop and supply the new renewable energy have been devised across multiple regions in Korea. Solar energy, in particular, is being applied to small-scale power supply in provincial areas, as solar cells are used to convert solar energy into electric energy to produce electric power. Nonetheless, in the case of solar power plants, the need for a large stretch of land and considerable sum of financial support implies that the planning step should take into consideration the most suitable meteorological and geographical factors. In this study, the proxy variables of meteorological and geographical factors associated with solar energy were considered in analyzing the vulnerable areas regarding the photovoltaic power generation facility across the nation. GIS was used in the spatial analysis to develop a map for assessing the optimal location for photovoltaic power generation facility. The final vulnerability map developed in this study did not reveal any areas that exhibit vulnerability level 5 (very high) or 1 (very low). Jeollanam-do showed the largest value of vulnerability level 4 (high), while a large value of vulnerability level 3 (moderate) was shown by several administrative districts including Gwangju metropolitan city, Jeollabuk-do, Chungcheongbuk-do, and Gangwon-do. A value of vulnerability level 2 (low) was shown by the metropolitan cities including Daegu, Ulsan, and Incheon. When the 30 currently operating solar power plants were compared and reviewed, most were found to be in an area of vulnerability level 2 or 3, indicating that the locations were relatively suitable for solar energy. However, the limited data quantity for solar power plants, which is the limitation of this study, prevents the accuracy of the findings to be clearly established. Nevertheless, the significance of this study lies in that an attempt has been made to assess the vulnerability map for photovoltaic power generation facility targeting various regions across the nation, through the use of the GIS-based spatial analysis technique that takes into account the diverse meteorological and geographical factors. Furthermore, by presenting the data obtained for all regions across the nation, the findings of this study are likely to prove useful as the basic data in fields related to the photovoltaic power generation.

• Journal of Advanced Navigation Technology
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• v.4 no.2
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• pp.114-131
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• 2000

An airline should consider the number of seats or size of aircraft, when it composes fleet or selects a type of aircraft for some routes. There are two major factors considered for this choice problem under the assumption that the objectives of an airline is a profit maximization: the operating cost and revenue from the aircraft operated. This research tries to solve the problem of aircraft size selection by airline. The study applies four steps to get optimal choice of aircraft size: (1) cost analysis for the relationship between airline operation cost and aircraft size: (2) market share and revenue analysis: (3) flight segment-level analysis, based on the derived cost, demand and revenue functions: and (4) network-level analysis to see how airlines make choice of aircraft size systematically at a network level. An airline can accommodate the increasing air travel demand by either increasing operation frequency, or increasing aircraft size that is represented by seat capacity, or both. Airport runway capacity and productivity depend on the size of aircraft used at airport. This paper presents the understanding of how airlines make decisions on the size of aircraft to operate, how they will adjust their choices when airport capacity is constrained, and how public regulation such as policy for landing fees could influence airlines' aircraft choice.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.