• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.624-629
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• 2007

While single view video coding uses the temporal prediction scheme, multi-view video coding (MVC) applies both temporal and inter-view prediction schemes. Thus, the key problem of MVC is how to reduce the inter-view redundancy efficiently, because various existing video coding schemes have already provided solutions to reduce the temporal correlation. In this paper, we propose a global disparity compensation scheme which increases the inter-view correlation and a new inter-view prediction structure based on the global disparity compensation. By experiment, we demonstrate that the proposed global disparity compensation scheme is less sensitive to change of the search range. In addition, the new Inter-view prediction structure achieved about $0.1{\sim}0.3dB$ quality improvement compared to the reference software.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.85-94
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• 2010

In this paper, we propose a location-prediction structure that can provide user service in advance. It consists of seven steps and supplies intelligent services which can forecast user's location. Context information collected from physical sensors and a history database is so difficult that it can't present importance of data and abstraction of data because of heterogeneous data type. Hence, we offer the location-prediction that change data type from heterogeneous data to homogeneous data. Extracted data is clustered by SOFM, then it gets user's location information by ARIMA and realizes the services by a reasoning engine. In order to validate the proposed location-prediction, we built a test-bed and test it by the scenario.

• Environmental Mutagens and Carcinogens
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• v.20 no.1
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• pp.21-25
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• 2000

Recently there is increasing interest in the use of structure activity relationships for predicting the biological activity of chemicals. The reasons for the interest include the decrease cost and time per chemical as compared with animal or cell system for identifying toxicological effects of chemicals and the reduction in the use of animals for toxicological testing. This study is to test the validity of the mutagenicity data generated from QSAR (Quantitative Structure Activity Relationship) program. Thirty chemicals, which had been evaluated by Ames test during 1997-1999, were assessed with TOPKAT QSAR mutagenicity prediction module. Among 30chemicals experimented, 28 were negative and 2 were positive for Ames test. On the contrary, 23 chemicals showed the high confidence level indicating high prediction rate in mutagenicity evaluation, and 7 chemicals showed the lsow to moderate confidence level indicating low prediction in mutagenicity evaluation. Overall mutagenicity prediction rate was 77% (23/30). The prediction rates for non-mutagenic chemicals were 79% (22/28) and mutagenic chemicals were 50% (1/2). QSAR could be a useful tool in providing toxicological data for newly introduced chemicals or in furnishing data for MSDS or in determining the dose in toxicity testing for chemicals with no known toxicological data.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.914-927
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• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• Journal of Distribution Science
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• v.13 no.10
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• pp.73-82
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• 2015

Purpose - This paper aims to investigate whether information asymmetry could explain capital structures in Korean corporations. According to Myers (1984), firms prefer internal funding to external financing due to the costs associated with information asymmetry. When external financing is necessary, firms prefer to issue debt rather than equity by the same reasoning. Since Shyam-Sunder and Myers (1999), numerous studies continue to debate the validity of the theory. In this paper, we show how the theory depends on assumptions and incorporated variables. We hope our investigation can provide helpful implications regarding capital structure, information asymmetry, and other firm characteristics. Specifically, our empirical results are complementary to the analysis of Son and Lee's (2015), a recent study that examines the pecking order theory prediction for Korean retail firms. Research design, data, and methodology - We test empirical models that are some variants of model used in Shyam-Sunder and Myers (1999). The financial and accounting data are provided by WISEfn for the firms listed on the KOSPI during 1990 to 2013. Bond ratings are supplied by the Korea Investor Service (KIS). We take into account the heterogeneity in debt capacity; a firm's debt capacity is measured by using the method of Lemmon and Zender (2010) based on its bond ratings. Finally, we estimate empirical models suggested by Shyam-Sunder and Myers (1999), Frank and Goyal (2003), and Lemmon and Zender (2010). Results - First, we find that Shyam-Sunder and Myers' (1999) prediction fails to explain total debt changes of Korean firms. Second, we find a non-monotonic relationship between total debt changes and financial deficits with respect to debt capacity. This contradicts the prediction of Lemmon and Zender (2010) that argues the pecking order theory survives with a monotonically increasing relationship. Third, we estimate a negative correlation coefficient between financial deficit and current debt changes. The result is the complete opposite of the prediction of Lemmon and Zender (2010). Finally, we also confirm the non-monotonic relationship between non-current debt changes and financial deficits with respect to debt capacity. Yet, the slope of coefficient is smaller than that of total debt change case. Indeed, the results are, to some extent, consistent with the prediction of pecking order theory, if we exclude the mid-debt capacity firms. Conclusions - Our empirical results complementary to the analysis of Son and Lee (2015), a recent study focusing on capital structure in Korean retail firms; their paper suggests interesting topics regarding capital structure, information asymmetry, and other firm characteristics in Korean corporations. Contrary to Son and Lee (2015), our results show that total debt changes and current debt changes are inconsistent with the prediction of Shyam-Sunder and Myers (1999). However, similar to Son and Lee (2015), non-current debt changes are consistent with the pecking order prediction, in the case of excluding the mid-level debt capacity firms. This contrast allows us to infer that industry characteristics significantly affect the validity of the pecking order prediction. Further studies are needed to analyze the economics behind this phenomenon, which is beyond the scope of our paper. In addition, the estimation bias potentially matters regarding the firm-level debt capacity calculation. We also reserve this topic for future research.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.457-464
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• 2019

Objectives: Quantitative structure-activity relationship (QSAR) is one of the effective alternatives to animal testing, but its credibility in terms of toxicity prediction has been questionable. Thus, this work aims to evaluate its predictive capacity and find ways of improving its credibility. Methods: Using $TOPKAT^{(R)}$, OECD toolbox, and $Derek^{(R)}$, all of which have been applied world-wide in the research, industrial, and regulatory fields, an analysis of prediction credibility markers including accuracy (A), sensitivity (S), specificity (SP), false negative (FN), and false positive (FP) was conducted. Results: The multi-application of QSARs elevated the precision credibility relative to individual applications of QSARs. Moreover, we found that the type of chemical structure affects the credibility of markers significantly. Conclusions: The credibility of individual QSAR is insufficient for both the prediction of chemical toxicity and regulation of hazardous chemicals. Thus, to increase the credibility, multi-QSAR application, and compensation of the prediction deviation by chemical structure are required.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.7 s.250
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• pp.841-848
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• 2006

Many researchers are developing computational prediction methods for protein tertiary structures to get much more information of protein. These methods are very attractive on the aspects of breaking technologies of computer hardware and simulation software. One of the computational methods for the prediction is a fragment assembly method which shows good ab initio predictions at several cases. There are many barriers, however, in conventional fragment assembly methods. Argues on protein energy functions and global optimization to predict the structures are in progress fer example. In this study, a new prediction method for protein structures is proposed. The proposed method mainly consists of two parts. The first one is a fragment assembly which uses very shot fragments of representative proteins and produces a prototype of a given sequence query of amino acids. The second one is a global optimization which folds the prototype and makes the only protein structure. The goodness of the proposed method is shown through numerical experiments.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1589-1595
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• 2018

Similarity index measures the topological proximity of node pairs in a complex network. Numerous similarity indices have been defined and investigated, but the dependency of structure on the performance of similarity indices has not been sufficiently investigated. In this study, we investigated the relationship between the performance of similarity indices and structural properties of a network by employing a two-state random network. A node in a two-state network has binary types that are initially given, and a connection probability is determined from the state of the node pair. The performances of similarity indices are affected by the number of links and the ratio of intra-connections to inter-connections. Similarity indices have different characteristics depending on their type. Local indices perform well in small-size networks and do not depend on whether the structure is intra-dominant or inter-dominant. In contrast, global indices perform better in large-size networks, and some such indices do not perform well in an inter-dominant structure. We also found that link prediction performance and the performance of similarity are correlated in both model networks and empirical networks. This relationship implies that link prediction performance can be used as an approximation for the performance of the similarity index when information about node type is unavailable. This relationship may help to find the appropriate index for given networks.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.