• 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.

• Journal of The Korean Association For Science Education
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• v.26 no.5
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• pp.637-645
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• 2006

This study reviewed and discussed the nature of scientific hypothesis described in philosophy, the philosophy of science, science, and science education. In these descriptions, a hypothesis was defined as one of five types: hypothesis as an assumption, hypothesis as a prediction, hypothesis as a tentative explanation, hypothesis as a tentative law, and hypothesis as a tentative causal explanation. Most scholars agreed that a hypothesis is a proposition or a set of propositions proposed as an explanation for an observed situation. In this view, a hypothesis is a possible answer to or an explanation of a question that accounts for all the observed facts. Also, it is a statement that explains why things happen in nature or an explanation for an observation that can be tested. In the five types of hypothesis meanings, a tentative explanation includes a tentative law and a tentative causal explanation. However, tentative laws are not explanation but description which are general statements drawn from specific experiences by way of a process known as induction. A number of studies also have distinguished hypothesis from assumption, tentative explanation, tentative law, and prediction. Therefore, a hypothesis is concluded to be a proposition or a set of propositions proposed as a tentative causal explanation for an observed situation.

• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.224-227
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• 1998

This paper proposes inter-level causal reasoning to implement synergistic approach. We decompose KOSPI prediction model into economy and industry level. Two kinds of intra-level QCOM are combined in inter-level QCOM via Inter-level relations. Downward reasoning is achieved by propagating the disturbance in the higher level to lower level while upward reasoning is to analyze the reverse cases.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• Journal of Korean Society for Quality Management
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• v.22 no.3
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• pp.179-190
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• 1994

Financial variables have been used in bankruptcy prediction. Despite of possible errors in prediction, most existing approaches do not consider the causal time sequence of prediction activity and bankruptcy phenomena. This paper proposes a prediction method using Neyman-Pearson Theorem and Bayes' rule. The proposed method uses posterior probability concept and determines a prediction policy with appropriate error rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5C
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• pp.534-539
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• 2006

In this paper, we propose a new frame prediction filtering technique and a hardware(H/W) architecture for scalable video coding. We try to evaluate MCTF(motion compensated temporal filtering) and hierarchical B-picture which are a technique for eliminate correlation between video frames. Since the techniques correspond to non-causal system in time, these have fundamental defects which are long latency time and large size of frame buffer. We propose a new architecture to be efficiently implemented by reconfiguring non-causal system to causal system. We use the property of a repetitive arithmetic and propose a new frame prediction filtering cell(FPFC). By expanding FPFC we reconfigure the whole arithmetic architecture. After the operational sequence of arithmetic is analyzed in detail and the causality is imposed to implement in hardware, the unit cell is optimized. A new FPFC kernel was organized as simple as possible by repeatedly arranging the unit cells and a FPFC processor is realized for scalable video coding.

• Journal of Bone Metabolism
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• v.25 no.4
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

• Journal of Distribution Science
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• v.12 no.6
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.228-231
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• 1998

Artificial Intelligence literatures have recognized that stock market is a highly unstructured and complex domain so that it is difficult to find knowledge that belongs to that domain. This paper demonstrates that the proposed QCOM can derive global knowledge about stock market on the basis of a set of local knowledge and express it as a digraph representation. In addition, inference mechanism using quantitative causal reasoning can describe the qualitative and quantitative effects of exogenous variables on stock market.