• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권3호
• /
• pp.721-739
• /
• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• 한국공간구조학회논문집
• /
• 제20권2호
• /
• pp.51-58
• /
• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• 한국건축시공학회지
• /
• 제18권3호
• /
• pp.243-249
• /
• 2018

본 연구에서는 건축구조물의 지하주차장 무근콘크리트의 컬링제어를 위한 연구를 수행하였다. 국내의 경우, 지하주차장 무근콘크리트의 수축균열을 제어하기 위해 섬유보강제가 사용되고 있지만, 수축균열과 컬링변형 제어에는 큰 도움이 되지 못하고 있는 실정이므로, 수축균열과 컬링 변형을 최소화하는 방안으로 수축저감제 (Shrinkage Reducing Agent: 글리콜 기반의 수축저감제, 이하 SRA)를 사용하였다. 또한 정량적인 컬링제어를 위하여 무근콘크리트를 켄티레버 보로 가정하여 처짐이론을 적용한 간이예측기술을 제안하였으며, 실측값과 비교하여 컬링변형 예측의 타당성을 검토하였다. 그 결과 SRA 1.0% 콘크리트가 SRA 0.0% 콘크리트보다 컬링 변형이 30% 정도 감소함을 확인하였다. 또한 전반적으로 켄틸레버 보의 처짐이론으로 무근 콘크리트의 컬링변형을 예측하는 것이 가능함을 확인할 수 있었다.

• Journal of Information Processing Systems
• /
• 제18권6호
• /
• pp.719-728
• /
• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• Asia-Pacific Journal of Atmospheric Sciences
• /
• 제54권4호
• /
• pp.623-637
• /
• 2018

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

• 한국해양공학회지
• /
• 제23권2호
• /
• pp.87-91
• /
• 2009

The welding distortion of a hull structure in the shipbuilding industry is inevitable at each assembly stage. The geometric inaccuracy caused by welding distortion tends to preclude the introduction of automation and mechanization and requires additional man-hours for adjustment work during the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method that can explicitly account for the influence of various factors on the welding distortion. The validity of this prediction method must also be clarified through experiments. For the purpose of reducing the weld-induced bending deflection, this paper proposes the plastic counter-deforming method (PCDM), which uses line heating as the optimum distortion control method. The validity of this method was substantiated by a number of numerical simulations and actual measurements.

• Journal of Welding and Joining
• /
• 제21권4호
• /
• pp.75-79
• /
• 2003

The welding distortion of a hull structure in the shipbuilding industry is inevitable at each assembly stage. This geometric inaccuracy caused by the welding distortion tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding distortion. The validity of the prediction method must be also clarified through experiments. For the purpose of reducing the weld-induced bending deflection, this paper proposes the plastic counter-deforming method (PCDM) using the line heating as the optimum distortion control method. The validity of this method has been substantiated by a number of numerical simulations and actual measurements.

• 한국통신학회논문지
• /
• 제22권2호
• /
• pp.259-267
• /
• 1997

The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2000년도 봄 학술발표회 논문집
• /
• pp.287-290
• /
• 2000

Nowadays, strength control is performed by test of compressive strength of concrete which is taken in construction filed. But because it is possible to confirm only compressive strength of concrete by that way, it is difficult to performing strength control pr process plan, So, if we can predict compressive strength of concrete, we can decide when shores and forms can be removed safety, plan process efficiently. This study intends to propose basic data for strength control as determination the time of forwoak removal through investigating propriety of strength prediction using Freiesleben function.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.485-485
• /
• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.