• Structural Engineering and Mechanics
• /
• v.86 no.4
• /
• pp.547-571
• /
• 2023

To deeply probe the actual earthquake level and fragility of typical reinforced concrete (RC) structures under multiple intensity grades, considering diachronic measurement building stock samples and actual observations of representative catastrophic earth shocks in China from 1990 to 2010, RC structures were divided into traditional RC structures (TRCs) and bottom reinforced concrete frame seismic wall masonry (BFM) structures, and the empirical damage characteristics and mechanisms were analysed. A great deal of statistics and induction were developed on the historical experience investigation data of 59 typical catastrophic earthquakes in 9 provinces of China. The database and fragility matrix prediction model were established with TRCs of 4,122.5284×104 m² and 5,844 buildings and BFMs of 5,872 buildings as empirical seismic damage samples. By employing the methods of structural damage probability and statistics, nonlinear prediction of seismic vulnerability, and numerical and applied functional analysis, the comparison matrix of actual fragility probability prediction of TRC and BFM in multiple intensity regions under the latest version of China's macrointensity standard was established. A novel nonlinear regression prediction model of seismic vulnerability was proposed, and prediction models considering the seismic damage ratio and transcendental probability parameters were constructed. The time-varying vulnerability comparative model of the sample database was developed according to the different periods of multiple earthquakes. The new calculation method of the average fragility prediction index (AFPI) matrix parameter model has been proposed to predict the seismic fragility of an areal RC structure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.4
• /
• pp.337-342
• /
• 2016

In this paper, we studied a modified linear prediction method to estimate target signal correctly. Linear prediction method estimate direction-of-arrival to linear combination for any antenna element and other antenna elements. Modified linear prediction used optimal weight and posterior probability method. Through simulation, we are comparative analysis about the performance of proposed, bartlett and MUSIC method. From simulation, Bartlett and MUSIC method was estimation 3 targets signal, and proposed method estimated 4 targets. We showed the superior performance of the proposed algorithm relative to the classical method in order to estimate of target signals.

• Journal of Environmental Impact Assessment
• /
• v.10 no.4
• /
• pp.297-304
• /
• 2001

Recently the road traffic noise has appeared as a significant environmental issue because of dramatic increase of vehicles and expansion of newly constructed road. Therefore, this study proposes the method that improves prediction factors and models through analysis of the existing road traffic noise prediction model. Prediction factors can be improved by establishing guideline for diffraction attenuation and applying daily traffic discharge, peak traffic discharge, and average traveling speed through an analysis of level service. Prediction must be made by periods of one or five years during 20 years. Prediction models also can be improved to include better prediction model through setting the database, establishing functional relation between physical properties and noise levels by acoustic analysis, and developing models for road traffic noise prediction in residential areas.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.463-468
• /
• 2011

Context prediction methods have been developed in two ways - one is a prediction for discrete context and the other is for continuous context. As most of the prediction methods have been used with prediction algorithms in specific domains suitable to the environment and characteristics of contexts, it is difficult to conduct a prediction for a user's context which is based on various environments and characteristics. This study suggests a context prediction method available for both discrete and continuous contexts without being limited to the characteristics of a specific domain or context. For this, we conducted a context prediction based on sequence matching by generating sequences from contexts in consideration of association rules between context attributes and by applying variable weights according to each context attribute. Simulations for discrete and continuous contexts were conducted to evaluate proposed methods and the results showed that the methods produced a similar performance to existing prediction methods with a prediction accuracy of 80.12% in discrete context and 81.43% in continuous context.

• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.2
• /
• pp.47-56
• /
• 2015

In an energy saving server cluster environment, the power modes of servers are controlled according to load situation, that is, by making ON only minimum number of servers needed to handle current load while making the other servers OFF. This algorithm works well under normal circumstances, but does not guarantee QoS under abnormal circumstances such as sharply rising or falling loads. This is because the number of ON servers cannot be increased immediately due to the time delay for servers to turn ON from OFF. In this paper, we propose a new prediction algorithm of the power consumption for improving QoS under not only normal but also abnormal circumstances. The proposed prediction algorithm consists of two parts: prediction based on the conventional time series analysis and prediction adjustment based on trend analysis. We performed experiments using 15 PCs and compared performance for 4 types of conventional time series based prediction methods and their modified methods with our prediction algorithm. Experimental results show that Exponential Smoothing with Trend Adjusted (ESTA) and its modified ESTA (MESTA) proposed in this paper are outperforming among 4 types of prediction methods in terms of normalized QoS and number of good reponses per power consumed, and QoS of MESTA proposed in this paper is 7.5% and 3.3% better than that of conventional ESTA for artificial load pattern and real load pattern, respectively.

• Journal of Information Technology Services
• /
• v.18 no.1
• /
• pp.187-200
• /
• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Genomics & Informatics
• /
• v.14 no.4
• /
• pp.138-148
• /
• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• The KIPS Transactions:PartD
• /
• v.10D no.4
• /
• pp.689-696
• /
• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone are used for identifying trouble spots of software system in analysis or design phases. Many criticality prediction models for identifying fault-prone modules using complexity metrics have been suggested. But most of them need training data set. Unfortunately very few organizations have their own training data. To solve this problem, this paper builds a new prediction model, KSM, based on Kohonen SOM neural networks. KSM is implemented and compared with a well-known prediction model, BackPropagation neural network Model (BPM), considering internal characteristics, utilization cost and accuracy of prediction. As a result, this paper shows that KSM has comparative performance with BPM.

• Genomics & Informatics
• /
• v.12 no.4
• /
• pp.254-260
• /
• 2014

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

• Journal of the Korea Institute of Building Construction
• /
• v.2 no.4
• /
• pp.177-182
• /
• 2002

The purpose of this study is to develope the strength prediction model by Maturity Method. A maturity function is a mathematical expression to account for the combined effects of time and temperature on the strength development of a cementious mixture. The method of equivalent ages is to use Arrhenius equation which indicates the influence of curing temperature on the initial hydration ratio of cement. For the experimental factors of this study, we selected the concrete mixing of W/C ratio 45, 50, 55 and 60% and curing temperature 5, 10, 20 and $30^{\circ}C$. And we compare and evaluate with logistic model that is existing strength prediction model, because we have to verify adaption possibility of new strength prediction model which is proposed by maturity method. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor.