• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.105-121
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• 2009

The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (${\ge}$14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season.

• Korean Journal of Environmental Agriculture
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• v.8 no.1
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• pp.37-46
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• 1989

A 2-dimensional pollutant transport phenomenon in shallow reservoirs was analyzed by using a finite element method. The Galerkin's weighted residual method, based on linear interpolation, was used and a triangle was adopted as an element. The two dimensional Stock's equation and the advection-diffusion equation integrated over depth were used as governing equations. Also the Newton-Raphson method was introduced to solve the non-linear terms of the equation. The results calculated by the model are in good agreement with the analytical solution for a simplified channel where a known solution is avaiable. An actual application of the model is attempted for Sapkyo Lake with a consideration of the influx of the Sapkyo Chun, the Muhan Chun and kogkyo Chun. Further refined research is needed to evaluate the water quality in the other reservoirs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5015-5022
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• 2010

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.97-109
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• 2003

This study is mainly conducted to derive the design drought rainfall by the consecutive duration using probability weighted moments with rainfall in the regional drought frequency analysis. It is anticipated to suggest optimal design drought rainfall of hydraulic structures for the water requirement and drought frequency of occurrence for the safety of water utilization through this study. Preferentially, this study was conducted to derive the optimal regionalization of the precipitation data that can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. Five homogeneous regions in view of topographical and climatological aspects were accomplished by K-means clustering method. Using the L-moment ratio diagram and Kolmogorov-Smirnov test, generalized extreme value distribution was confirmed as the best fitting one among applied distributions. At-site and regional parameters of the generalized extreme value distribution were estimated by the method of L-moments. Design drought rainfalls using L-moments following the consecutive duration were derived by the at-site and regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design drought rainfall derived by at-site and regional analysis in the observed an simulated data were computed and compared. In has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE. RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design drought rainfall. Consequently, optimal design drought rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2725-2736
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• 1998

In this paper. we present a new pitch determination method for speech analysis. namely VFF(Variable Forgetting Factor) based. by using the WRLS-VFF-VT(Weighted Recursive Least Square-Variable Forgetting Factor-Variable Threshold) algorithm. A proposed method uses VFF to identify the glottal closure points which correspond to the instants of the main excitation pulses for voiced speech. The modified EGG

• The KIPS Transactions:PartD
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• v.14D no.3 s.113
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• pp.347-354
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• 2007

It is not easy for the customers to search the valuable information on the goods among countless items available in the Internet. In order to save time and efforts in searching the goods the customers want, it is very important for a recommender system to have a capability to predict accurately customers' preferences. In this paper we present a refined neighbor selection algorithm for clustering based collaborative filtering in recommender systems. The algorithm exploits a graph approach and searches more efficiently for set of influential customers with respect to a given customer; it searches with concepts of weighted similarity and ranked clustering. The experimental results show that the recommender systems using the proposed method find the proper neighbors and give a good prediction quality.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9439-9444
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• 2014

Purpose: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. Materials and Methods: We used two publicly available RNA-Seq datasets from normal prostate tissue and prostate cancer. Putative lncRNAs were predicted using the biological technology, then specific lncRNAs of prostate cancer were found by differential expression analysis and co-expression network was constructed by the weighted gene co-expression network analysis. Results: A total of 1,080 lncRNA transcripts were obtained in the RNA-Seq datasets. Three genes (PCA3, C20orf166-AS1 and RP11-267A15.1) showed a significant differential expression in the prostate cancer tissues, and were thus identified as prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. Conclusions: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.11
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• pp.1594-1605
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• 2001

In this study, a numerical simulation was developed which was capable of predicting the characteristics of NO formation in pilot scale combustor adopting the air-staged burner flame. The numerical calculation was constructed by means of establishing the mathematical models fur turbulence, turbulent combustion, radiation and turbulent nitric oxide chemistry. Turbulence was solved with standard k-$\xi$ model and the turbulent combustion model was incorporated using a two step reaction scheme together with an eddy dissipation model. The radiative transfer equation was calculated by means of the discrete ordinates method with the weighted sum of gray gases model for CO$_2$and H$_2$O. In the NO chemistry model, the chemical reaction rates for thermal and prompt NO were statistically averaged using the $\beta$ probability density function. The results were validated by comparison with measurements. For the experiment, a 0.2 MW pilot multi-air staged burner has been designed and fabricated. Only when the radiation was taken into account, the predicted gas temperature was in good agreement with the experimental one, which meant that the inclusion of radiation was indispensable for modeling multi-air staged gas flame. This was also true of the prediction of the NO formation, since it heavily depended on temperature. Subsequently, it was found that the multi-air staged combustion technique might be used as a practical tool in reducing the NO formation by controlling the peak flame temperature.