• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Geomechanics and Engineering
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• 제39권1호
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• pp.73-85
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• 2024

There is growing interest in introducing artificial ground freezing (AGF) as a method to temporarily secure unstable ground during tunnel construction. In order to efficiently operate an artificial ground freezing system, basic modeling research is needed on the changes in freezing behavior according to various soil environmental conditions as well as design conditions. In this study, a thermal-hydraulic coupled analysis was performed to simulate the artificial ground freezing process of ground containing salt water. The effect of major variables, including pore water salinity, on artificial ground freezing test performance was investigated. Additionally, an artificial neural network-based prediction model was proposed to estimate the time required to achieve the desired arch thickness. The artificial neural network model demonstrated reliable accuracy (R² = 0.9942) in predicting the time it would take to reach the desired arch thickness. Among the major input variables considered, pore water salinity appeared to be the most influential input variable, and initial soil temperature showed the least importance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.907-910
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• 2005

Although several artificial disc designs have been developed for the treatment of discogenic low back pain, biomechanical change with its implantation was rarely studied. To evaluate the effect of artificial disc implantation on the biomechanics of functional spinal unit, nonlinear three-dimensional finite element model of L4-L5 was developed with 1-mm CT scan data. Two models implanted with artificial discs, SB $Charit\acute{e}$ or Prodisc, via anterior approach were also developed. The implanted model predictions were compared with that of intact model. Angular motion of vertebral body, force on spinal ligaments and facet joint, and the stress distribution of vertebral endplate for flexion-extension, lateral bending, and axial rotation with a compressive preload of 400 N were compared. The implanted model showed increased flexion-extension range of motion and increased force in the vertically oriented ligaments, such as ligamentum flavum, supraspinous ligament and interspinous ligament. The increase of facet contact force on extension were greater in implanted models. The incresed stress distribution on vertebral endplate for implanted cases indicated that additinal bone growth around vertebral body and this is matched well with clinical observation. With axial rotation moment, relatively less axial rotation were observed in SB $Charit\acute{e}$ model than in ProDisc model.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1999년도 KOBA 방송기술 워크샵 KOBA Broadcasting Technology Workshop
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• pp.119-124
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• 1999

This paper proposes an evolutionary model for automatically generating artificial creatures of various shapes and colors according to insect ecology. This model offers a novel way to naturally evolve the shapes and colors of artificial creatures. The evolutionary model used in our research is based on Genetic Algorithms (GA). In this paper, artificial Computer Graphics(CG) creatures develop into various shapes and colors according to the evolutionary model. Later, they can be used as CG animated characters. This model also solves the problem of reducing the time and labor cost for mass production of various characters. It could be used in such areas as the cavalry battle scene in Disney's animation, “Mulan”. Our approach has two steps. At first, artificial creatures move according to information gathered form the five senses. This information is also used for generating the shapes of the five sense organs[1]. Then, based on the GA, evolutionary mode[2], we prepare prototype creatures, which evolve into various shapes and different colors in alternating generations. Finally, our evolutionary model successfully generates various character shapes and colors automatically.

• 융합보안논문지
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• 제24권2호
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• pp.47-53
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• 2024

현행 개인정보 라이프 사이클 모델은 전통적인 시스템에 맞추어져 있어서 인공지능의 개인정보 흐름 파악과 효율적인 보호 대책 수립에 적합하지 않은 문제점이 있다. 따라서 본 논문은 인공지능에 적합한 개인정보 라이프사이클 모델을 제시하는 것을 목적으로 한다. 본 논문은 수집-보유-학습-이용-파기·정지 단계와 파기·정지를 위한 재학습 프로세스가 포함된 인공지능의 학습 특성을 고려한 개인정보 라이프 사이클 모델을 제시했다. 이후 기존 모델(개인정보 영향평가와 ISMS-P 모델)과 본 논문에서 새로 제시한 모델의 성능을 평가했다. 이를 통해 새로 제안한 모델이 기존 모델보다 인공지능의 개인정보 라이프 사이클의 설명에 우수한 특성을 가지고 있음을 증명했다.

• 한국건축시공학회지
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• 제13권1호
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• pp.66-74
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• 2013

In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• 한국유기농업학회지
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• 제25권3호
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• 한국포장학회지
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• 제29권3호
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.