• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.237-242
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• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Journal of Integrative Natural Science
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• v.4 no.1
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• pp.31-37
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• 2011

Various algorithms and system development are being required to support the advanced decision making of navigation information support system because of a serious loss of lives and property accidents by officer's error like as carelessness and decision faults. Much of researchers have introduced the techniques about the systems, but they hardly consider environmental factors. In this paper, We collect the context information in order to assess the risk, which is considered the various factor of the sailing ship, then extract the features of knowledge context, which is to apply the weight of correlation coefficients among data in context information. We decide the risk after the extract features through the classification and prediction of context information, and compare the value accuracy of proposed method in order to compare efficiency of the weighted value with the non-weighted value. As a result of experience, we know that the method of weight properties effectively reflect the marine environment because the weight accurate better than the non-weighted.

• Journal of KSNVE
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• v.10 no.5
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• pp.792-799
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• 2000

Aircraft noise in the vicinity of Kimpo international airport has damaged to large number of people who live in communities. This paper investigates noise exposed area due to aircraft flight based on prediction modeling program INM and flight path data. Especially effect on route for aircraft has been considered. Ti also examines noise impact for various flight modes, such as a thrust cutback climb method.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.529-531
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• 2000

본 논문은 Geometry Compression 분야에서 다뤄지는 압축기법 중 delta encoding 과정을 보완하여 좀 더 높은 압축률을 얻고자 하는 vertex position prediction 과정에 대한 내용으로 구성되어 있다. 이것은 triangle strip 형태의 입력을 받아서 그 vertex data 중 position 정보들간의 delta encoding 과정을 예측 기법을 이용한 encoding 과정으로 대체하여 Huffman encoding 과정에서의 symbol 개수를 줄여 압축률을 향상시키자는 개념에서 출발한다. triangle strip 생성 기법 중 greedy algorithm을 적용한 후, 기존의 parallelogram 방식과 이 논문에서 새로이 제안하는 방식을 비교하여 보다 나은 압축 방식을 제시하는 것이 이 논문의 목적이다. 이 논문에서 제시하는 방식을 실험한 결과, 기존의 예측 기법에 비해 2.4% 정도의 향상을 보여주고 있다.

• Korean Journal of Hydrosciences
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• v.7
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• pp.125-137
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• 1996

The subject of the paper is the selection of the number and location of raingauge stations among existing ones for the computation of mean areal precipitation and for use as input of real-time flow prediction models. The weighted average method developed by National Weather Service was used to compute MAP over the Boone River basin in Iowa with a 40 year daily data set. Two different searching methods were used to find local optimal solutions. An operational rainfall-runoff model was used to determine the optimal location and number of stations for flow prediction.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1422-1432
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• 1993

Nonlinear mapping function of the HCNN( Hidden Control Neural Network ) can change over time to model the temporal variability of a speech signal by combining the nonlinear prediction of conventional neural networks with the segmentation capability of HMM. We have two things in this paper. first, we showed that the performance of the HCNN is better than that of HMM. Second, the HCNN with its prediction error measure given by weighted distance is proposed to use suitable distance measure for the HCNN, and then we showed that the superiority of the proposed system for speaker-independent speech recognition tasks. Weighted distance considers the differences between the variances of each component of the feature vector extraced from the speech data. Speaker-independent Korean digit recognition experiment showed that the recognition rate of 95%was obtained for the HCNN with Euclidean distance. This result is 1.28% higher than HMM, and shows that the HCNN which models the dynamical system is superior to HMM which is based on the statistical restrictions. And we obtained 97.35% for the HCNN with weighted distance, which is 2.35% better than the HCNN with Euclidean distance. The reason why the HCNN with weighted distance shows better performance is as follows : it reduces the variations of the recognition error rate over different speakers by increasing the recognition rate for the speakers who have many misclassified utterances. So we can conclude that the HCNN with weighted distance is more suit-able for speaker-independent speech recognition tasks.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.149-154
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• 2001

Ship owners are demanding quieter vessels since crews have become more sensitive to their acoustic environment. Accordingly, designers of shipyards need to respond intelligently to the challenging requirements of delivering a quiet vessel. In early design stage, to predict shipboard noise the statistical approach is preferred to other methods because of simplicity. However, since the noise characteristics of the ships vary continuously with the environments, it is necessary to update the prediction formula with data base management system. This paper describes the feature of database program with the prediction method. Database management programs with GUI, are applied to Intranet system that is accessible by any users. Statistical approach to the prediction of A-weighted noise level in ship cabins, based on multiple regression analysis, is conducted. The noise levels in ship cabins are mainly affected by the parameters of the deadweight, the type of ship, the relative location of engines and cabins, the type of deckhouse, etc. As a result of verification, the formulas ensure the accuracy of 3 ㏈ in 83 % of cabins.

• Journal of the Korean Institute of Gas
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• v.18 no.1
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• pp.8-16
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• 2014

In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.