• 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.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.261-264
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• 2010

Fast algorithm is proposed by using mel scale and the distribution characteristic of LSP parameters, and is to reduce the computational amount. Computational amount means the calculating times of transformation from LPC coefficients to LSP parameters. Among conventional methods, the real root method is considerably simpler than other, but neverthless, it still suffer from its indeterministic computational time. Because the root searching is processed sequentially in frequency region. In this paper, the searching interval is arranged by using mel scale but not it is uniform and searching order is arranged by the distribution characteristic of LSP parameters that is most LSP papameters are occured in specific frequency region. In experimental results, computational amount of the proposed algorithm is reduced about 48.95% in average, but the transformed LSP parameters of the proposed method were the same as those of real root method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.169-178
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• 2021

Recently, a bike-sharing system (BSS) has become popular as a convenient "last mile" transportation. Rebalancing of bikes is a critical issue to manage BSS because the rents and returns of bikes are not balanced by stations and periods. For efficient and effective rebalancing, accurate traffic prediction is important. Recently, cluster-based traffic prediction has been utilized to enhance the accuracy of prediction at the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clustering algorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employing the centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposed algorithm can be deterministic and fast.