• Journal of Animal Science and Technology
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• 제62권4호
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• pp.438-448
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• 2020

This study was performed to increase the accuracy of genomic estimated breeding value (GEBV) predictions for domestic pigs using single-breed and admixed reference populations (single-breed of Berkshire pigs [BS] with cross breed of Korean native pigs and Landrace pigs [CB]). The principal component analysis (PCA), linkage disequilibrium (LD), and genome-wide association study (GWAS) were performed to analyze the population structure prior to genomic prediction. Reference and test population data sets were randomly sampled 10 times each and precision accuracy was analyzed according to the size of the reference population (100, 200, 300, or 400 animals). For the BS population, prediction accuracy was higher for all economically important traits with larger reference population size. Prediction accuracy was ranged from -0.05 to 0.003, for all traits except carcass weight (CWT), when CB was used as the reference population and BS as the test. The accuracy of CB for backfat thickness (BF) and shear force (SF) using admixed population as reference increased with reference population size, while the results for CWT and muscle pH at 24 hours after slaughter (pH) were equivocal with respect to the relationship between accuracy and reference population size, although overall accuracy was similar to that using the BS as the reference.

• 한국해양공학회지
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• 제31권6호
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• pp.425-430
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• 2017

In order to reduce the cost of corrections and time needed for the block assembly process, the reverse setting method is applied for a back-heated block to neutralize deck deformation. The proper reverse setting shape for a back-heated block to correct deformation improved the deck flatness, but an excessive amount of reverse setting could inversely affect the flatness of the block. A prediction method was developed for the proper reverse setting shape using a back-heated block, considering the complex geometry of blocks, thickness of the deck plate, and thermal loading conditions such as welding and back-heating. The prediction method was developed by combining the re-meshing technique and inherent strain-based deformation analysis using the finite element method. Because the flatness deviation was decreased until the lower critical point and thereafter it tended to increase again, the optimum value for which the flatness was the best case was selected by repeatedly calculating the predefined reverse setting values. Based on this analysis and the study of the back-heating deformation of large assembly blocks, including the reverse setting shape, the mechanism for selecting the optimum reverse setting value was identified. The developed method was applied to the actual blocks of a ship, and it was confirmed that the flatness of the block was improved. It is concluded that the developed prediction method can be used to predict the optimum reverse setting shape value of a ship's block, which will reduce the cost of corrections in the construction stage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3150-3170
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• 2016

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

• Earthquakes and Structures
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• 제18권1호
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• 대한토목학회논문집
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• 제35권2호
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• pp.465-470
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• 2015

에폭시 아스팔트 혼합물은 에폭시 수지와 경화제의 화학반응이 진행되어 양생시간을 거쳐 성능 발현이 이루어진다. 에폭시 아스팔트의 양생수준은 후속공정의 진행과 교통개방 및 공정계획의 수립에 절대적인 영향을 미치므로 정확한 예측모델의 개발이 중요하다. 본 연구에서는 기존 예측식에 사용되는 인자들의 화학적 의미 분석을 통하여 에폭시 수지의 농도와 경화특성을 반영하여 기존식보다 확대된 적용 범위를 갖는 양생수준 예측식을 제시하였다. 실외양생 실험과 비교 결과 상관계수가 0.971 이상으로 나타나 조성이 다른 에폭시 아스팔트 혼합물의 온도와 시간에 따른 양생수준을 예측할 수 있는 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제24권5호
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• pp.1101-1101
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• 2013

• 한국수자원학회논문집
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• 제53권11호
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• pp.1025-1037
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• 2020

본 연구는 호우경보에 사용되는 Limited area ENsemble prediction System (LENS) 강우예측자료에 대한 공간적 특성 및 적용성을 평가하였다. LENS는 13개의 강우앙상블 멤버를 가지고 있어 호우경보를 발령하는데 있어 확률적인 방법을 활용할 수 있다. 그러나 LENS의 자료의 접근성은 매우 낮아 강우예측자료의 적용성에 대한 연구가 미흡한 실정이다. 본 연구에서는 행정구역별로 활용되는 호우경보 시스템에 따라 하나의 지점값과 면적평균값을 관측값과 비교하여 평가지수를 산정하였다. 또한, LENS의 발령시간에 따르는 각 앙상블 멤버들의 정확성을 평가하였다. LENS는 멤버별로 과대 혹은 과소 예측의 불확실성을 보여줬다. 면적단위의 예측이 지점단위의 예측보다 더 높은 예측성을 보여주었다. 또한, 다가오는 72시간의 강우를 예측하는 LENS 자료는 수재해의 영향성이 있을 수 있는 강우 사상에 대하여 예측성능이 좋은 것으로 평가되었다. 추후 국지강우앙상블시스템(LENS) 자료는 행정구역 또는 유역면적 단위의 홍수 대비에 기초자료로 활용될 수 있을 것으로 기대된다.

• Journal of the Korean Wood Science and Technology
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• 제44권6호
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• pp.864-871
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• 2016

This study was conducted to compare and analyze gross calorific values from measurement methods of lignocellulosic biomass and calculation data from calorific value prediction models based on the elemental content. The deviation of Liriodendron tulipifera (LT) and Populus euramericana (PE) was shown 7.7 cal/g and 7.4 cal/g respectively in palletization method, which are within repeatability limit 28.8 cal/g of ISO FDIS 18125. In the case of Thailand charcoal (TC), nontreatment method and palletization method was satisfied with repeatability limit as 22.8 cal/g and 8.8 cal/g respectively. Seowon charcoal (SC) was shown deviation of 11.4 cal/g in nontreatment method, because the density and chemical affinity of sample increases as the carbon content increases from heat treatment at high temperature in the case of TC and SC. In addition, after applying the elemental content of each of these samples to the calorific value prediction models, the study found that Model Equation (3) was relatively consistent with measured calorific values of all these lignocellulosic biomass. Thus, study about the correlation between the density and size of particle should be conducted in order to select the measurement method for a wide range of solid biofuels in the future.