• International Journal of Contents
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• v.12 no.4
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• pp.69-75
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• 2016

Adolescent media addiction has emerged as an important social agenda in Korea. However, there has not been enough discussion on the causal structure of media addiction and policy interventions. The objective of this study is to identify and assess the mechanism of the existing and the revised Shutdown policy based on the systems thinking approach. To achieve this purpose, we establish the relationship between media usage, flow, and addiction, and develop a causal loop diagram. Based on the causal loop diagram, we explore the causal structure of two policy scenarios: shutdown policy and deregulation. Our study suggests that policy interventions inducing direct parental control on children's media usage time are ineffective since the time control reduces children's autonomy, which helps alleviate media addiction. Therefore, this study suggests that policy intervention should focus on alleviating addiction itself rather than on controlling media usage time.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.723-726
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• 2009

This paper describes a non-causal interpolative prediction method for B-picture encoding. Interpolative prediction uses correlations between neighboring pixels, including non-causal pixels, for high prediction performance, in contrast to the conventional prediction, using only the causal pixels. For the interpolative prediction, the optimal quantizing scheme has been investigated for preventing conding error power from expanding in the decoding process. In this paper, we extend the optimal quantization sceme to inter-frame prediction in video coding. Unlike H.264 scheme, our method uses non-causal frames adjacent to the prediction frame.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.335-346
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• 2008

Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.23-30
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• 2005

Causal order of message delivery algorithm ensures that every transmitted message is delivered in causal order. It should be noted that control information should be transmitted with each message in order to enforce causal order. Hence, it is important to reduce this communication overhead because the impact of the overhead increases proportionally with the number of related processes. In this paper we propose and evaluate effective a ${\Delta}-causal$ order algorithm for multimedia data which have real-time property. To reduce transmission overhead, proposed algorithm eliminates redundant information as early as possible which is not explicitly required for preserving causal order. Average communication overhead of our algorithm is much smaller than other existing algorithms.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.47-75
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• 2010

Two experiments were conducted to explore whether the Or structure works as a default causal model in inferring the causal structure from the contingency data. The contingencies of three unfamiliar variables were used in Experiment 1. Participants inferred the Or structure quite well from the OR data, but incorrectly inferred the Or structure from the And data for about a little less than half of the time, and almost always inferred the Or structure from the chain data. The results suggested that the Or interpretation can be the default causal model. The prevalence of the Or interpretation from the contingency data was reported even when the three variables were familiar ones in Experiment 2. Multinomial modeling performed on the results of the two experiments strongly suggested that the Or interpretation work as a default causal model.

• Korean Journal of Logic
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• v.12 no.2
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• pp.89-114
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• 2009

We have many causal beliefs, and they play an important role in our decision making. Unlike evidential decision theory, causal decision theory claims that an account of rational choice must use causal beliefs to identify the considerations that make a choice rational. I claim that evidential decision theory is refuted by the original Newcomb's problem but not by the medical Newcomb problem. The latter is taken to be the best example to point out the weakness of evidential decision theory. However, by the explicit statement about causal relations, I argue that the medical Newcomb problem loses its strength in refuting evidential decision theory. With this argument, this paper clarifies the difference between evidential decision theory and causal decision theory.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1025-1031
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• 2006

In this paper, a deconvolution filtering method for all-pass systems based on FIR approximation is proposed. The proposed method enables us to obtain a causal stable deconvolution filter by FIR approximating a non-causal stable deconvolution filter to a causal stable one. As we can see in this paper, the impulse response of the deconvolution filter for all-pass system is simply the mirror image of the impulse response for all-pass system itself. Due to this symmetric property between all-pass system itself and its deconvolution Inter, this method can be applied to all-pass systems without special limitation of the system's order. In order to verify the performance of the proposed method. computer simulation results for 1st-, 2nd- and 400th-order all-pass systems are included.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.253-258
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• 2010

The causal network represents the knowledge related to the dependency relationship between all attributes. If the causal network is available, the dependency relationship can be employed to estimate the missing values for improving the estimation performance. However, the previous method had a limitation in that it did not consider the bidirectional characteristic of the causal network. The proposed method considers the bidirectional characteristic by applying prior and posterior conditions, so that it outperforms the previous method.