• Korean journal of communication and information
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• v.22
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• pp.7-38
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• 2003

This study is primarily concerned with the discussion about the crisis of Korean Public Service Broadcasting within the context of changing media environment. The study firstly attempts to explain the causes of current problems of Public Service Broadcasting in general, and categorizes the patterns of Western countries' reactions to cope with the problems. Secondly, the present study deals with the case of Korean Public Service Broadcasting. This paper argues that Korean Public Service Broadcasters have adopted four salient strategies: 1) They have tried to achieve high program ratings based on non-distinctive programming; 2) They have preferred the commercial advertising revenues as the primary financial resource; 3) They have attempted to expand their social power while exploiting their program contents; 4) Finally, they have tried to be a sole player in the decision making process, independent from all the other social forces such as the state, capital, and the civil organizations. This study suggests that four strategies should be reconsidered because those are far from the original ideas of Public Service Broadcasting.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.1031-1042
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• 2006

In recent years, a growing concern exists in watershed and stream improvement projects. Under these circumstances, this paper estimates monetary value of the attributes of alternatives for rehabilitation of hydrologic cycle using choice experiments. Choice experiments shows vivid image and estimates a willingness to pay based on their preference for environmental goods. A preliminary survey shows that the attributes of the Anyangcheon watershed are flood-damage possibilities, Instreamflow, water quality, river characteristic and estimates the tax for the Anyangcheon watershed improvements. We surveyed 200 citizens were selected as samples of watershed beneficing in Seoul and Gyeonggi Province and used conditional logit model to analyze the implicit values of the attributive per household. The benefit of the attributes by province based on the implicit price obtained from estimated parameters were calculated. This study is expected to contribute to the decision-making process for policy-makers by providing useful methodological framework and quantitative information related to watershed improvement projects.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.315-324
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• 2008

This paper double-bound dichotomous choice method as a contingent valuation methods is used to derive willingness to pay for the water quality at the Anyangcheon watershed. The linear random utility model show that value of improvement of the Anyangcheon watershed water quality is 4,930 won per house and month of the Seoul and Gyeong-gi area residents. There is no difference between Seoul and Gyeong-gi area residents. Respondents overall accepted the contingent market and were willing to contribute a significant amount (3,860 to 5,101 won), on average, per household per month. The aggregate value of the water quality improvement in the Anyangcheon watershed amounts to approximately 83.0 to 109.7 billion won per year. This study is expected to contribute to the decision-making process for policy-makers by providing useful methodological framework and quantitative information related to watershed improvement projects.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.1-9
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• 2015

This paper proposes CAC method that is more efficient for RRM using game theory combined with Multiple Attribute Decision Making(MADM). Because users request services with different Quality of Service(QoS), the network preference values to alternative networks for each service are calculated by MADM methods such as Grey Relational Analysis(GRA), Simple Additive Weighting(SAW) and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS). According to a utility function representing preference value, non-cooperative game is played, and then network provider select the requested service that provide maximum payoff. The appropriate service is selected through Nash Equilibrium that is the solution of game and the game is played repeated. We analyze two overlaid networks among four Wireless LAN(WLAN) systems with different properties. Simulation results show that proposed MADM techniques have same outcomes for every game round.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.563-577
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• 2017

Due to the Internet and computing capability, new and variant malware are discovered around 1 Million per day. Companies use dynamic analysis such as behavior analysis on virtual machines for unknown malware detection because attackers use unknown malware which is not detected by signature based AV effectively. But growing number of malware types are not only PE(Portable Executable) but also non-PE such as MS word or PDF therefore dynamic analysis must need more resources and computing powers to improve detection effectiveness. This study elicits the pre-filtering system evaluation factor to improve effective dynamic malware analysis system and presents and verifies the decision making model and the formula for solution selection using AHP(Analytics Hierarchy Process)

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.220-231
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• 2002

Trojan-horse programs are the programs that disguise normal and useful programs but do malicious thing to the hosts. This paper proposes an anti-Trojan horse mechanism using the information attached to the code by the developers. In this mechanism, each code is accompanied with the information on their possible accesses to resources, and based on this information users determine whether the code is malicious or not. Even in the case a code is accepted by users due to its non-malicious appearance, its runtime behaviors are monitored and halted whenever any attempts to malicious operations are detected. By hiring such runtime monitoring system, this mechanism enables detecting unknown Trojan horses and reduces the decision-making overhead being compared to the previous monitoring-based approaches. We describe the mechanism in a formal way to show the advantages and the limitations of the security this mechanism provides.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.