• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Steel and Composite Structures
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• 제39권3호
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.37-37
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• 2023

There is currently a high-accuracy modern forecasting method that uses machine learning algorithms or artificial neural network models to forecast river water levels or flowrate. As a result, this study aims to develop a mathematical model based on artificial neural networks to effectively forecast river water levels upstream of Tranh Culvert in North Vietnam's Bac Hung Hai irrigation system. The mathematical model was thoroughly studied and evaluated by using hydrological data from six gauge stations over a period of twenty-two years between 2000 and 2022. Furthermore, the results of the developed model were also compared to those of the long-short-term memory neural networks model. This study performs four predictions, with a forecast time ranging from 6 to 24 hours and a time step of 6 hours. To validate and test the model's performance, the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error, and root mean squared error were calculated. During the testing phase, the NSE of the model varies from 0.981 to 0.879, corresponding to forecast cases from one to four time steps ahead. The forecast results from the model are very reasonable, indicating that the model performed excellently. Therefore, the proposed model can be used to forecast water levels in North Vietnam's irrigation system or rivers impacted by tides.

• Journal of Korean Neurosurgical Society
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• 제47권6호
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• pp.446-453
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• 2010

Objective : The purpose of this study was to analyze the biomechanical effects of three different constrained types of an artificial disc on the implanted and adjacent segments in the lumbar spine using a finite element model (FEM). Methods : The created intact model was validated by comparing the flexion-extension response without pre-load with the corresponding results obtained from the published experimental studies. The validated intact lumbar model was tested after implantation of three artificial discs at L4-5. Each implanted model was subjected to a combination of 400 N follower load and 5 Nm of flexion/extension moments. ABAQUS$^{TM}$ version 6.5 (ABAQUS Inc., Providence, RI, USA) and FEMAP version 8.20 (Electronic Data Systems Corp., Plano, TX, USA) were used for meshing and analysis of geometry of the intact and implanted models. Results : Under the flexion load, the intersegmental rotation angles of all the implanted models were similar to that of the intact model, but under the extension load, the values were greater than that of the intact model. The facet contact loads of three implanted models were greater than the loads observed with the intact model. Conclusion : Under the flexion load, three types of the implanted model at the L4-5 level showed the intersegmental rotation angle similar to the one measured with the intact model. Under the extension load, all of the artificial disc implanted models demonstrated an increased extension rotational angle at the operated level (L4-5), resulting in an increase under the facet contact load when compared with the adjacent segments. The increased facet load may lead to facet degeneration.

• Design & Manufacturing
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• 제15권4호
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• 한국콘텐츠학회논문지
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• 제19권1호
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• pp.108-116
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• 2019

본 논문에서는 인공지능기술을 통합보안관제 기술에 효율적으로 적용하는 방안을 제안하였다. 즉, 통합보안관제시스템에 수집된 빅 데이터를 기반으로 머신러닝 학습을 인공지능에 적용하여 사이버공격을 탐지하도록 하고 적절한 대응을 한다. 기술의 발달에 따라서 늘어나는 보안장비와 보안 프로그램들로부터 쌓이는 수많은 대용량의 로그들을 사람이 일일이 분석하기에는 한계에 부딪히고 있다. 분석방법 또한 한 가지 로그가 아닌 여러 가지 이기종간의 보안장비의 로그까지 서로 상관분석을 해야 하기 때문에 더욱 더 통합보안관제에 적용되어서 신속한 분석이 이루어져야 하겠다. 이런 행위를 분석하고 대응하는 과정들이 효과적인 학습방법을 통해서 점진적으로 진화를 거쳐 성숙해가는 인공지능기반 통합보안관제 서비스모델을 새롭게 제안하였다. 제안된 모델에서 예상되는 핵심적인 문제점들에 대한 해결방안을 모색하였다. 그리고 정상 행위 기반의 학습모델을 개발하여 식별되지 않는 비 정상행위 위협에 대응력을 강화하는 학습방법을 도출하였다. 또한, 제안된 보안 서비스모델을 통하여 보안담당자들의 분석과 대응을 효율적으로 지원할 수 있는 보안관제에 대한 향후 연구방향을 제시하였다.

• 대한치과교정학회지
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• 제53권3호
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• pp.194-204
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• 2023

Objective: To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject. Methods: Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling. Results: Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added. Conclusions: The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.

• Wind and Structures
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• 제36권6호
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• 한국정보과학회논문지:시스템및이론
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• 제27권8호
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• pp.701-709
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• 2000

실세계에서의 바람은 자연 발생적인 것과 인위적으로 생성한 것으로 분류할 수 있다. 이제까지의 연구 결과들은 자연 현상으로서의 바람을 모델링하였다. 본 논문에서는 사람의 입이나 선풍기, 에어컨 등에서 발생하는 인공적인 바람을 모델링하기 위한 바람 모델을 제시한다. 본 논문의 바람 모델에서는 생성된 바람이 도달하는 물체를 찾아내고, 그 물체에 가해지는 힘을 계산하는 방법을 제공한다. 특히, 이 모델은 가상 현실과 같은 실시간 처리가 필요한 분야들에서 사용 가능하도록 최적화된 계산을 수행하도록 설계되었다. 본 논문에서 제시한 방법은 기존의 자연 발생적인 바람 모델들과는 보완적인 관계에 있다. 이들 모델들을 통합하여 종합적인 바람 생성 시스템을 구성할 수 있을 것으로 기대된다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.