• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.548-553
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• 2014

In order to evaluate the performance of arrow that manufactures through production process, it is used that personal experiences such as hunters who have been using bow and arrow for a long time, technicians who produces leisure and sports equipment, and experts related with this industries. Also, the intensity of arrow's impact point which obtains from repeated shooting experiments is an important indicator for evaluating the performance of arrow. There are some ongoing researches for evaluating performance of arrow using intensity of the arrow's impact point and the arrow's flying image that obtained from high-speed camera. However, the research that deals with mutual relation between distribution of the arrow's impact point and characteristics of the arrow (length, weight, spine, overlap, straightness) is not enough. Therefore, this paper suggests both the system that could describes the distribution of the arrow's impact point into numerical representation and the correlation model between characteristics of arrow and impact points. The inputs of the model are characteristics of arrow (spine, straightness). And the output is MAD (mean absolute distance) of triangular shaped coordinates that could be obtained from 3 times repeated shooting by changing knock degree 120. The input-output data is collected for learning the correlation model, and ANN (artificial neural network) is used for implementing the model.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.519-519
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• 2015

2007년 발간된 IPCC의 4차 평가보고서에서 자연재해, 환경, 해양, 농업, 생태계, 보건 등 다양한 부분에 미치는 기후변화의 영향에 대한 과학적 근거들이 제시되면서 기후변화는 현세기 범지구적인 화두로 대두되고 있다. 또한, 기후변화에 의한 지구 온난화는 대규모의 수문순환 과정에서의 변화들과 연관되어 담수자원은 기후변화에 대단히 취약하며 미래로 갈수록 악영향을 받을 것으로 6차 기술보고서에서 제시하고 있다. 특히 우리나라는 지구온난화가 전 지구적인 평균보다 급속하게 진행될 가능성이 높기 때문에 기후변화에 대한 담수자원 취약성이 더욱 클 것으로 예상된다. 따라서 지표수에 용수의존도가 높은 우리나라의 댐 저수지를 대상으로 기후변화에 따른 수환경 변화의 정확한 분석과 취약성 평가는 필수적이다. 본 연구에서는 SRES A1B 시나리오를 적용하여 기후변화가 주암호 저수지의 수환경 변화에 미치는 영향을 분석하였다. 지역스케일의 미래 기후시나리오 생산을 위해 인공신경망(Artificial Neural Network.,ANN)기법을 적용하여 예측인자(강우, 상대습도, 최고온도, 최저온도)에 대해 강우-유출모형에 적용이 가능한 지역스케일로 통계적 상세화를 수행하였으며, 이를 유역모델에 적용하여 저수지 유입부의 유출량 및 부하량을 예측하였다. 유역 모델의 결과를 토대로 저수지 운영모델에 저수지 유입부의 유출량을 적용하여 미래 기간의 방류량을 산정하였으며, 최종적으로 저수지 모델에 유입량, 유입부하량 및 방류량을 적용하여 저수지 내 오염 및 영양물질 순환 및 분포 예측을 통해서 기후변화가 저수지 수환경에 미치는 영향을 평가하였다. 기후변화 시나리오에 따른 상세기 후전망을 위해서 기후인자의 미래분석 기간은 (I)단계 구간(2011~2040년), (II)단계 구간(2041~2070년), (III) 단계 구간(2071~2100년)의 3개 구간으로 설정하여 수행하였으며, Baseline인 1991~2010년까지의 실측값과 모의 값을 비교하여 검증하였다. 강우량의 경우 Baseline 대비 미래로 갈수록 증가하는 것으로 전망되었으며, 2011년 대비 2100년에서 연강수량 6.4% 증가한 반면, 일최대강수량이 7.0% 증가하는 것으로 나타나 미래로 갈수록 집중호우의 발생가능성이 커질 것으로 예측되었다. 유역의 수문 수질변화 전망도 강수량 증가의 영향으로 주암댐으로 유입하는 총 유량이 Baseline 대비 증가 하였으며, 유사량 및 오염부하량도 증가하는 것으로 나타났다. 저수지 수환경 변화 예측결과 유입량이 증가함에 따라서 연평균 체류시간이 감소하였으며, 기온 및 유입수온 상승의 영향으로 (I)단계 구간대비 미래로 갈수록 상층 및 심층의 수온이 상승하는 것으로 나타났다. 연중 수온성층기간 역시 증가하는 것으로 나타났으며, 남조류는 (I)단계 구간 대비 (III)단계 구간으로 갈수록 출현시기가 빨라지며 농도 역시 증가하였다. 또한 풍수년, 평수년에 비해 갈수년에 남조류의 연평균농도 상승폭과 최고농도가 크게 나타나 미래로 갈수록 댐 유입량이 적은 해에 남조류로 인한 피해 발생 가능성이 높아질 것으로 예상된다.

• Composites Research
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• v.22 no.4
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• pp.20-26
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• 2009

In the current study, thermoforming and compression analysis were carried out for the woven composite egg-box panel with the non-orthogonal constitutive material model, which is proposed by Xue et al. The material model is implemented in commercial engineering software, LS-DYNA, with a user subroutine. Directional properties in non-orthogonal coordinates are determinedusing the deformation gradient tensor and the material modulus matrix in local coordinate is updated at eaeh corresponding time step. After the implemented non-orthogonal constitutive model is verified by the bias extension test, the egg-box panel simulations are performed. The egg-box panel simulations are divided into two categories: thermoforming (draping) and crushing. The finite element model for crushing analysiscan be obtained using the displacement result of thermoforming process.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.301-309
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• 2008

In this study, the effect of formaldehyde to phenol (F/P) molar ratios, catalyst wt%, and reaction temperature on the chemical structure was studied utilizing a two-level full factorial experimental design. The effect of three variables on the chemical structure was analyzed by using three-way ANOVA of SPSS. Concentration of methyrol-substituted phenols after 300 min addition reaction increased with higher the F/P mole ratio, lower the reaction temperature and lower the catalyst wt%. Resol catalysed by barium hydroxide showed higher addition of formaldehyde onto ortho positions of phenolic rings. A simplified elementary reaction model for resole type phenolic resin formation which do not consider the dissociation of phenolic compounds and the fraction of formaldehyde in the form of methylene glycol was proposed and compared with Zavitsas' type models. Elementary reaction model showed error of 2.79% compared to the error of 3.27% in Zavitsas' type models. It was thought that the elementary reaction model could be used to predict the behavior of addition reaction in resol formation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.25-37
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• 2022

Climate change is becoming increasingly unpredictable. This has led to changes in various systems such as ecosystems, human life and hydrological cycles. In particular, the recent unpredictable climate change frequently causes extreme droughts and torrential rains, resulting in complex water resources disasters that cause water pollution due to inundation and retirement rather than primary disasters. SWAT was used as a watershed model to analyze future runoff and pollutant loads. The climate scenario analyzed the RCP4.5 climate scenario of the Meteorological Agency standard scenario (HadGEM3-RA) using the normal quantitative mapping method. Runoff and pollutant load analysis were performed by linkage simulation of climate scenario and watershed model. Finally, the results of application and verification of linkage model and analysis of future water quality change due to climate change were presented. In this study, we simulated climate change scenarios using artificial neural networks, analyzed changes in water temperature and turbidity, and compared the results of dams with artificial neural network results through W2 model, a reservoir water quality model. The results of this study suggest the possibility of applying the nonlinearity and simplicity of neural network model to Hapcheon dam water quality prediction using climate change.

• Information Systems Review
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• v.24 no.2
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• pp.67-88
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• 2022

This study proposed a research model that can explain the usage intentions of users and non-users by considering the performance aspects of personal mobility and external environmental factors based on the SOR (Stimulus-Organism-Response) model, A survey was conducted targeting domestic users and non-users, and research models and hypotheses were verified through Partial Least Square (PLS) and Artificial Neural Network (ANN). As a result of the analysis, it was confirmed that the users' perceived satisfaction and perceived trust had a positive effect on their intention to use, and that perceived risk and environmental value had a significant relationship with perceived satisfaction and perceived trust. For non-users, it was found that there was a positive correlation between perceived satisfaction and intention to use, and it was verified that perceived risk and environmental value, like users, were significant antecedents of perceived satisfaction and perceived trust. Among the remaining variables, the perceived mobility of users and the perceived ease of use of non-users were respectively presented as important influencing factors on perceived satisfaction.