• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권1호
• /
• pp.136-151
• /
• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• 소성∙가공
• /
• 제29권4호
• /
• pp.218-228
• /
• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• 대한인간공학회:학술대회논문집
• /
• 대한인간공학회 1993년도 추계학술대회논문집
• /
• pp.124-132
• /
• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• 품질경영학회지
• /
• 제48권1호
• /
• pp.171-186
• /
• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• 한국정보시스템학회지:정보시스템연구
• /
• 제28권3호
• /
• pp.249-276
• /
• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• 한국건축시공학회지
• /
• 제22권3호
• /
• pp.317-326
• /
• 2022

건설 분야에서 머신러닝(Machine learning)에 필요한 방대한 공사비 자료를 확보하는 데 어려움이 있어, 아직은 실용적으로 활용되지는 못하고 있다. 본 연구에서는 이러한 공사비 예측을 위하여 최신의 인공신경망(ANN) 방법을 사용하여, 공사비 예측성능을 향상 시키기 위한 방법을 제시하고자 한다. 특히 타겟변수를 로그 변환하는 방식, 피처스케일링 방식을 적용하고자 하였으며, 이들의 공사비 예측성능을 비교 분석하고자 한다. 이는 향후 다양한 조건을 갖는 공사비 예측과 적정 공사비 검증에 도움을 줄 수 있을 것으로 예측된다.

• 전자공학회논문지B
• /
• 제30B권5호
• /
• pp.62-72
• /
• 1993

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three dimensional adaptive target tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track updata illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver level detector. A detailed simulation is conducted to test the validity of our tracking algorithm for target trajectories under various conditions of maneuver.

• 제어로봇시스템학회논문지
• /
• 제9권7호
• /
• pp.563-568
• /
• 2003

In this paper, We propose a target tracking filter which utilizes the PDA for data association in a clutter environment and also propose an analytic solution for ideal filter covariance which accounts for all the possible events in the PDA. Monte Carlo simulation for the proposed filter in a clutter environment indicates that the proposed analytic solution forms the true error covariance of the PDA Filter.

• 융합정보논문지
• /
• 제10권6호
• /
• pp.209-216
• /
• 2020

단백질 항원에 존재하는 에피토프는 에피토프를 기반으로 한 백신 개발의 표적이 되고 있다. 인간의 주조 직적합 복합체 (MHC-1)에 결합하는 펩타이드를 확인할 수 있는 여러 서버들이 보고되고 있으나 인간의 MHC-I 분자의 수가 매우 많고 각 서버 검색 방법의 표준화 부재 등의 문제로 인해 펩타이드 예측에 적절한 서버를 선정하는 것이 쉽지 않다. 본 논문에서는 MHC-I 결합 펩타이드를 예측하는 서버 30 종을 비교하였으며, 다발성 골수종에 적용하기 위해 survivin 단백질로부터 사람의 HLA-A2 제한 펩타이드를 예측하였다. 본 연구의 결과는 MHC-I 결합 예측의 표준화된 방법을 제시하고 펩타이드 에피토프를 예측하는데 도움을 줄 것이다.

• 해양환경안전학회지
• /
• 제6권1호
• /
• pp.111-120
• /
• 2000

In this study the drift prediction models currently used in search and rescue(SAR) operations are introduced and the basic differences among them are analyzed by comparing the target classification, the treatment of environmental conditions, leeway equations and the determination of search area. In addition the new methods under development including AP98 of U.S. Coast Guard are compared and studied to improve the efficiency of SAR operation.