• 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.

• Korean Journal of Logic
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• v.8 no.1
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• pp.47-67
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• 2005

Bayesian networks have been used in studying and simulating causal inferences by using the probability function distributed over the variables consisting of inquiry space. The focus of the debates concerning Bayesian networks is the causal Markov condition that constrains the probabilistic independence between all the variables which are not in the causal relations. Cartwright, a strong critic about the Bayesian network theory, argues that the causal Markov condition cannot hold in indeterministic systems, so it cannot be a valid principle for causal inferences. The purpose of the paper is to explore whether her argument on the causal Markov condition is valid. Mainly, I shall argue that it is possible for upholders of the causal Markov condition to respond properly the criticism of Cartwright through the continuous causal model that permits the infinite sequence of causal events.

• 한국체육학회지인문사회과학편
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• v.54 no.5
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• pp.233-250
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• 2015

In this study, changes and emotions that result from doing yoga and the influence of yoga on daily lives were investigated by using causal network. This information was gathered from interviews and outlined in a diagram form. By checking the daily participation records of 77 participants who took a yoga class as part of the cultural studies curriculum at H University, general factors related to change were extracted and then 7 participants were chosen for in-depth interviews. In the interviews, the changes experienced from doing yoga and the emotions caused by the change and the influence this change had on daily lives were documented and the collected results were displayed in a diagram using causal network according to the flow of questionnaire. As a result, the changes experienced through doing yoga were divided in 4 categories: physical function, emotional, cognitive and physiological changes. Each change and emotion caused by the change were shown to have an influence on daily lives. Through schematized causal network for each change, the changes and emotions which the participants experienced and the influence of yoga on daily lives could be checked. Based on the study results, the effect of yoga, the need for various approaches to examine the effect exercise has on emotions and the applicability of causal network that can be employed as a creative and effective quantitative data analysis method were discussed.

• Korean System Dynamics Review
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• v.13 no.4
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• pp.33-55
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• 2012

The research is to identify important diffusion factors and their effects on green car diffusion process using system dynamics perspectives and a causal-loop analysis. Through a deep review on previous research, we have found the important factors of green car diffusion process. Price, driving range, network effect, recharge system, fuel cost had important facilitation on consumer attraction and green car diffusion. Based on the review, we have constructed a causal loop diagram explaining hybrid car diffusion process. We have found 3 important reinforcing loops in the causal loop diagram. Loop for learning & economies of scale(supply side), loop for network effect(consumer side), and loop for battery development(technology side) had most significant roles in the whole diffusion process. Through a deliberate analysis on the 3 causal loops, we have found meaningful results. First, there seems to exist a critical mass in the diffusion. Second, of the 3 loops, the battery technology had most significant role. Third, not consumer installed base but sales must be a standard to decide whether the critical mass is achieved or not. Based on these findings, several meaningful implications are suggested for the government and corporations related to the green car industries.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• 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.

• The Journal of Korean Academic Society of Nursing Education
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• v.10 no.1
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• pp.54-63
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• 2004

The purpose of this study is to identify the factors that influence stress experienced by nursing students and to provide a perceived causal structure model among these variables. The ultimate goal of this study is to develop efficient guidance to clinical nursing education in this population. This study intends to apply perceived causal structure: network analysis method which was developed by Kelly(1983), and has been applied in nursing research. This method is selected to show dynamic relationship of stressor using network method. Data was collected from convenient sample of 186 junior college nursing students who had the clinical practice experience during 10 weeks. Data collection and analysis was conducted in 2 steps from December, 9, 2002 to February, 8, 2003. Step 1.: Data was collected using literature review(10 articles) to identify the causes of stress. Nine causes of stress were extracted. Step 2.: As perceived casual structure network study, data was collected using questionnaires which included 9 extracted cause and stress. The questionnaire contained a 10 X 10 grid table with 10 causes and effects printed. In network analysis, 'Yes' was scored as 1, 'No' was scored as 0, and the mean(maximum 1, minimum 0) was calculated. Construction of the network under inductive eliminative analysis which stopped the construction of the network when the consensual agreement level dropped near 50% was proceeded by adding causes in order of the mean rating level. In this study, construction of the final network was stopped by consensual agreement level of 52% of the total subjects. The results are summarized as follows : Step 1: Investigation of the causes of stress ; The extracted causes of stress from quality data was identified 9 categories ; negative nurse, lack of clinical practice opportunity, ambiguous role, negative patient, lack of nursing knowledge and skill, difficult of personal relations, inefficient clinical practice guidance, gap of theory and practice, lack of support. Step 2 : Construction of the perceived causal structure model ; 1) The most central cause of stress is ambiguous role in the systems of causation. 2) The distal cause of stress is inefficient clinical practice guidance 3) The causes that have a number of outgoing link are negative nurse, ambiguous role. 4) The causes that have a number of incoming link are ambiguous role, gap of theory- practice, lack of clinical practice opportunity, lack of nursing knowledge- skill. 5) There is a mutual relationship between stress and difficult of personal relations, stress and ambiguous role, ambiguous role and negative nurse, ambiguous role and lack of clinical practice opportunity, ambiguous role and lack of nursing knowledge-skill, lack of nursing knowledge-skill and gap of theory- practice. In conclusion, the network suggests that the first centre cause is related on ambiguous role and the second on negative nurse, inefficient clinical practice guidance in the systems of causation

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• The Journal of Korean Academic Society of Nursing Education
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• v.11 no.2
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• pp.161-168
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• 2005

Purpose: The purposes are to identify the factors that influence work-based stressor experienced by clinical nurses and to provide a perceived causal structural model among these factors. Method: Data was collected and analyzed in 2 steps to apply a perceived causal structure : network analysis which was developed by Kelley(1983). Results: 1. The extracted causes from qualitative data were identified 10 categories ; over loaded work, relative feelings of deprived, inefficient duty schedule, negative attitudes of patient, burden of extra affair, inadequate administrative support, negative attitudes of physician, conflict with other personnels in hospital, lack of professional knowledge and skill, nursing service marketing burden. 2. Construction of the perceived causal structural model ; 1) The most central cause is over loaded work and the distal causes were inadequate administrative support, lack of professional knowledge and skill in the systems of causation. 2) The causes that have a number of outgoing link were over loaded work, inadequate administrative support, negative attitudes of physician. 3) The cause that have a number of incoming link was relative feelings of deprived. Conclusion: The network suggests that the first centre cause was related on over loaded work.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.