• 산업경영시스템학회지
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• 제44권4호
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• pp.23-31
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• 2021

Treatment and management of chronic low back pain (CLBP) should be tailored to the patient's individual context. However, there are limited resources available in which to find and manage the causes and mechanisms for each patient. In this study, we designed and developed a personalized context awareness system that uses machine learning techniques to understand the relationship between a patient's lower back pain and the surrounding environment. A pilot study was conducted to verify the context awareness model. The performance of the lower back pain prediction model was successful enough to be practically usable. It was possible to use the information from the model to understand how the variables influence the occurrence of lower back pain.

• 한국산업융합학회 논문집
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• 제5권2호
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• pp.103-110
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• 2002

An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.146.4-146
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• 2001

Various methods have been proposed for the time series prediction. Most of the conventional methods only optimize parameters of mathematical models, but to construct an appropriate functional form of the model is more difficult in the first place. We employ the Genetic Programming (GP) to construct the functional form of prediction models. Our method is distinguished because the model parameters are optimized by using Back-Propagation (BP)-like method and the prediction model includes discontinuous functions, such as if and max, as node functions for describing complicated phenomena. The above-mentioned functions are non-differentiable, but the BP method requires derivative. To solve this problem, we develop ...

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2005년도 춘계학술발표대회 논문집
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• pp.158-161
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• 2005

The fiber-reinforced composite materials have been advanced for various applications because of its excellent mechanical and electromagnetic properties. On their manufacturing processes, however, thermo-curing inherently produces the undesired thermal deformation mainly from temperature drop from the process temperature to the room temperature, so called spring-back. The spring-back must be removed to keep the precision of designed shape. In this research, the spring-back of {glass fiber / epoxy}+{carbon fiber / epoxy} unsymmetric hybrid composites were predicted using Classical Lamination Theory (CLT), and compared with the experimental data. Additionally, using finite element analysis (ANSYS), the predicted data and experimental data were compared. The predicted values by CLT and ANSYS were well matched with experimental data.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.646-649
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• 1995

An experimental and analyical investigations were undertaken to improve understanding of spring-back phenomena of chopped fiber reinforced thermoplastic composite sheet. The materials tested contained 20, 35, 40 percent by weight of readomly oriented glass fiber in a prolypropylene matrix. The simple bending tests were performed at temperatures ranging form 75 .deg. c to 150 .deg. c with 25 .deg. c increment and at punch speed of 1mm/sec and 0.01mm/sec. The spring-back angel measured in pure bending is compared with the prediction base on the analytical model. Good agreement between experimental and predicted results was observed.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제33권4호
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• pp.323-330
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• 2011

Purpose: The present study examined the reproducibility of an operation plan by comparing the jaw position of STO with the postoperative mandibular set back measurement in sagittal split ramus osteotomy. Methods: Thirty patients with class III dental and skeletal malocclusion and who were treated with BSSRO were reviewed. Three plain radiographs such as the panoramic view, the lateral cephalogram and the submentovertex view were taken before and after operation. Also, paper surgery for STO and model surgery were used to evaluate the amount of mandibular set back. Results: On the panoramic view, the amount of mandibular set back in STO was similar to the postoperative results of model surgery, but the amount of mandibular set back on the lateral cephalogram was smaller than the postoperative result of model surgery and then the amount of set back on submentovertex view was similar to the postoperative result of model surgery. Conclusion: Precise tracing and paper surgery should be performed for a combined expected STO in order to predict the exact amount of preoperative mandibular set back.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• 응용통계연구
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• 제23권2호
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• pp.219-234
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• 2010

시가총액에 따른 인덱스(INDEX) 투자를 했을 경우에, VaR(Value at Risk)을 종합주가지수(KOSPI)로부터 얻은 수익율의 극단 손실값들로부터 추정한다. 이를 위해, 극단값 이론 중 BM(Block Maxima) 모형을 적용하며, 극단 손실값들의 비독립적 발생을 고려하기 위하여, extremal index 역시 추정한다. 모형의 타당성을 알아보기 위해, 실패율방법을 이용한 사후검정 (back-testing) 을 실시한다. 사후검정을 통해, BM 모형을 적용한 VaR의 추정이 적절함을 알 수 있었다. 또한, 일반적으로 많이 사용되는 GARCH 모형을 이용한 VaR의 추정과 비교한다. 이를 통해, 오차가 t-분포를 따른다고 가정하는 경우, GARCH 모형을 이용한 VaR의 추정이 BM 모형을 이용한 경우와 사후 검정결과에 차이가 없음을 확인하였다. 그러나, GARCH 모형을 통한 VaR 추정은 추정시점근방의 극단 손실값들에 민감하게 반응하지만, BM 모형은 그렇지 않았다. 따라서, 현 시점으로부터 단기간동안의 손실위험은 GARCH 모형을 이용한 VaR의 추정값을 사용하는 것이 적절하며, 장기간동안의 손실위험은 BM 모형으로부터 얻은 VaR의 추정값을 사용하는 것이 적절하다.

• Journal of Magnetics
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• 제19권1호
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• pp.84-89
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• 2014

This paper presents an analytical prediction and experimental verification of the electromagnetic performance of a parallel magnetized surface-mounted permanent magnet (SPM) motor having a fractional number of slots per pole combination. On the basis of a two-dimensional (2-D) polar coordinate system and a magnetic vector potential, analytical solutions for flux density produced by the permanent magnets (PMs) and stator windings are derived. Then, analytical solutions for back-electromotive force (emf) and electromagnetic torque are derived from these field solutions. The analytical results are thoroughly validated with 2-D nonlinear finite element (FE) analysis results. Finally, the experimental back-emf and electromagnetic torque measurements are presented to test the validity of the analysis.

• 한국정밀공학회지
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• 제13권10호
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• pp.105-111
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• 1996

This paper suggests the scheme to simultaneously accomplish prediction of fracture initiation and geomeytical configuration of deformation in metal forming processes using the artificial neural network. A three-layer neural network is used and a back propagation algorithm is adapted to train the network. The Cookcroft-Lathjam criterion is used to estimate whether fracture occurs during the deformation process. The geometrical configuration and the value of ductile fracture are measured by finite element method. The predictions of neural network and numerical results of simple upsetting are compared. The proposed scheme has successfully predicted the geometrical configuration and fracture initiation.