• Health Policy and Management
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• v.12 no.4
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• pp.91-113
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• 2002

We report and analyze the Korean physicians' recent general strike over the implementation of the Separation of Prescribing and Dispensing Practice (SPDP) in which more than 18,000 private clinics and 280 hospitals participated. Utilizing game-theoretic models of bargaining we explain why the Korean physicians were so successful in organizing intense collective action against the government and securing very favorable policy outcomes. In particular, we highlight the role of distributional conflict among social actors in shaping the details of institutional reform. The introduction of the SPDP was a necessary first step in the overall reform of health care system in Korea. However, the SPDP was perceived to be a serious threat to the economic viability of their profession by the vast majority of Korean physicians who had long been relied on the profits from selling medicines to compensate for the loss of income due to the low service fee under the previous health care system. The strong political coalition among heterogeneous physicians enabled them to organize an intense form of collective action, the general strike. Thus, physicians were successful not only in dragging the government to a bargaining table, but also winning in the bargaining and securing an outcome vastly favorable to them. On the other hand, the lack of an overall reform plan in the health care policy area, especially the finance of the National Health Insurance and the need for maintaining an image as a successful reform initiator, motivated the government to reach a quick resolution with the striking physicians.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.347-352
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• 2021

In this paper, we propose a method for finding feature subsets that are effective for classification in an input dataset by using a multi-agent reinforcement learning method. In the field of machine learning, it is crucial to find features suitable for classification. A dataset may have numerous features; while some features may be effective for classification or prediction, others may have little or rather negative effects on results. In machine learning problems, feature selection for increasing classification or prediction accuracy is a critical problem. To solve this problem, we proposed a feature selection method based on reinforced learning. Each feature has one agent, which determines whether the feature is selected. After obtaining corresponding rewards for each feature that is selected, but not by the agents, the Q-value of each agent is updated by comparing the rewards. The reward comparison of the two subsets helps agents determine whether their actions were right. These processes are performed as many times as the number of episodes, and finally, features are selected. As a result of applying this method to the Wisconsin Breast Cancer, Spambase, Musk, and Colon Cancer datasets, accuracy improvements of 0.0385, 0.0904, 0.1252 and 0.2055 were shown, respectively, and finally, classification accuracies of 0.9789, 0.9311, 0.9691 and 0.9474 were achieved, respectively. It was proved that our proposed method could properly select features that were effective for classification and increase classification accuracy.

• The KIPS Transactions:PartD
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• v.17D no.5
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• pp.337-346
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• 2010

Timing diagram is popularly utilized for the reason of its advantages; it is convenient for timing diagram to describe behavior of system and it is simple for described behaviors to recognize it. Various techniques are needed to test systems described in timing diagram. One of them is a technique to derive the system into a certain condition under which a test case is effective. This paper proposes a technique to automatically generate the test input sequence to reach the condition for systems described in timing diagram. It requires a proper input set which satisfy transition condition restricted by input waveform and timing constraints to generate a test input sequence automatically. To solve the problem, this paper chooses an approach utilizing the linear programming, and solving procedure is as follows: 1) Get a Timing diagram model as an input, and transforms the timing diagram model into a linear programming problem. 2) Solve the linear programming problem using a linear programming tool. 3) Generate test input sequences of a timing diagram model from the solution of linear programming problem. This paper addresses the formal method to drive the linear programming model from a given timing diagram, shows the feasibility of our approach by prove it, and demonstrates the usability of our paper by showing that our implemented tool solves an example of a timing diagram model.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.117-123
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• 2012

Smart devices are great tool to get a lot of information of user by variety sensors, application, web. The information is good clue to seize pattern of user. So, we can expect that customized content-service will be possible based on utilizing information of user. This expectation alters the type of content-service from just providing lots of contents to smart devices to recommendation contents which wanted, needed, favorite looking by user. For this customized content-service, a system model like a curator in galleries or museums is required. So, in this paper, we suggest Intelligent Contents Curation(ICCuration) model which has 3 sub modules with sensing, analysis and filtering information of user. The collected information of user are processed up to scenarios and the scenario is a clue for selecting contents which will be recommended to users. In the scenario has user's preferences and behaviors as well as devices informations as elements. So, contents can be optimized not only domain category but type of media for devices.

• Journal of Radiation Protection and Research
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• v.19 no.2
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• pp.109-120
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• 1994

A method for estimating the effectiveness of each protective action against a nuclear accident has been proposed using the fuzzy set theory. In most of the existing countermeasure models in actions under radiological emergencies, the large variety of possible features is simplified by a number of rough assumptions. During this simplification procedure, a lot of information is lost which results in much uncertainty concerning the output of the countermeasure model. Furthermore, different assumptions should be used for different sites to consider the site specific conditions. Tn this study, the diversity of each variable related to protective action has been modelled by the linguistic variable. The effectiveness of sheltering and evacuation has been estimated using the proposed method. The potential advantage of the proposed method is in reducing the loss of information by incorporating the opinions of experts and by introducing the linguistic variables which represent the site specific conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of Music and Human Behavior
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• v.14 no.2
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• pp.91-105
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• 2017

Community music therapy (CoMT) has been recently developed and expands the opportunities for music therapy. The concept of CoMT is introduced in this article, and its three attributes of community, music, and health are reviewed. This study specified each attribute of CoMT: a community (a group of people, a field where members of a group interact with each other), music (a substance of interaction, collective music-making), and health (motivation and goal of interaction, relational and social well-being). The application and interactions of the three attributes of CoMT are introduced as in the concept of community music, music and health, and community health. How CoMT can be applied to the field of music therapy is also detailed and based on the concept of CoMT and its relationship with the attributes, the CoMT was reconstructed as CoMuHeal in this study. Future studies are needed to propose how music therapy approaches can be developed to provide music for well-being and better health in the community and how CoMT can be applied in collaboration with other professional fields.

• Journal of KIISE:Information Networking
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• v.31 no.2
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• pp.171-178
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• 2004

Port scanning detection systems should rather satisfy a certain level of the requirement for system performance like a low rate of “False Positive” and “False Negative”, and requirement for convenience for users to be easy to manage the system security with detection systems. However, public domain Real Time Scan Detection Systems have high rate of false detection and have difficulty in detecting various scanning techniques. In addition, as current real time scan detection systems are based on command interface, the systems are poor at user interface and thus it is difficult to apply them to the system security management. Hence, we propose TkRTSD(Tcl/Tk Real Time Scan Detection System) that is able to detect various scan attacks based on port scanning techniques by applying a set of new filter rules, and minimize the rate of False Positive by applying proposed ABP-Rules derived from attacker's behavioral patterns. Also a GUI environment for TkRTSD is implemented by using Tcl/Tk for user's convenience of managing network security.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.635-642
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• 2003

Reinforcement learning got successful results in a lot of applications such as control and scheduling. Various function approximation methods have been studied in order to improve the learning speed and to solve the shortage of storage in the standard reinforcement learning algorithm of Q-Learning. Most function approximation methods remove some special quality of reinforcement learning and need prior knowledge and preprocessing. Fuzzy Q-Learning needs preprocessing to define fuzzy variables and Local Weighted Regression uses training examples. In this paper, we propose a function approximation method, Fuzzy Q-Map that is based on on-line fuzzy clustering. Fuzzy Q-Map classifies a query state and predicts a suitable action according to the membership degree. We applied the Fuzzy Q-Map, CMAC and LWR to the mountain car problem. Fuzzy Q-Map reached the optimal prediction rate faster than CMAC and the lower prediction rate was seen than LWR that uses training example.