• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.84-89
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• 2023

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

• 한국컴퓨터산업학회논문지
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• 제7권3호
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• pp.171-178
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• 2006

슈퍼스칼라 프로세서에서 명령어 이슈 길이 값 예측방식은 명령의 결과 값을 미리 예측하고, 그 이후에 데이터 종속관계가 이는 명령들에게 값을 조기에 공급하므로써 이들 명령들을 모험적으로 실행하여 성능을 향상시키는 방식이다. ILP 프로세서는 명령어 수준 병렬성의 성능향상을 위하여 값을 미리 예측하여 병렬로 이슈하고 수행한다[4]. 본 논문에서는 이를 수행하기 위한 값 예측기의 명령어 이슈 길이(4,8,16)의 성능분석을 위한 예측률, 예측정확도, 성능향상 등을 측정하여 평가한다. 실험결과 8이슈의 성능향상이 높음을 보였다.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 시스템엔지니어링학술지
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• 제16권1호
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• 산업경영시스템학회지
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• 제38권4호
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• International Journal of Fluid Machinery and Systems
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• 제8권4호
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• pp.221-229
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• 2015

For efficient design process of regenerative blower, the present study provides new generalized pressure and leakage flow loss models, which can be used in the performance analysis method of regenerative blower. The present performance analysis on designed blower is made by incorporating momentum exchange theory between impellers and side channel with mean line analysis method, and its pressure loss and leakage flow models are generalized from the related fluid mechanics correlations which can be expressed in terms of blower design variables. The present performance analysis method is applied to four existing models for verifying its prediction accuracy, and the prediction and the test results agreed well within a few percentage of relative error. Furthermore, the present performance analysis method is also applied in developing a new blower used for fuel cell application, and the newly designed blower is manufactured and tested through chamber-type test facility. The performance prediction by the present method agreed well with the test result and also with the CFD simulation results. From the comparison results, the present performance analysis method is shown to be suitable for the actual design practice of regenerative blower.

• 한국정보통신학회논문지
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• 제1권1호
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• pp.77-83
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• 1997

본 논문에서는 현재 디지털 이동통신 시스템에서 운용되고 있는 QCELP(Qualcomm Code Excited Linear Predictor) 음성부호화 방식과 향후 IMT-2000 (International Mobile Telecommunications 2000) 등의 시스템에서 사용 예정인 CS-ACELP(Conjugate Structure Algebraic Code Excited Linear Prediction) 음성부호화 방식과의 성능을 비교한다. 특히 무선 채널을 사용하는 이동통신환경의 특징인 채널에러로 인한 음성부호화기의 성능을 비교함으로써 채널에러에 강인한 음성부호화 알고리즘 설계에 대한 고찰을 유도한다.

• Structural Engineering and Mechanics
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• 제74권4호
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• pp.503-514
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• 2020

This study proposes prediction models for the shear strength of steel fiber reinforced concrete (SFRC) and ultra-high-performance fiber reinforced concrete (UHPC) beams using a Bayesian parameter estimation approach and a collected experimental database. Previous researchers had already proposed shear strength prediction models for SFRC and UHPC beams, but their performances were limited in terms of their prediction accuracies and the applicability to UHPC beams. Therefore, this study adopted a statistical approach based on a collected database to develop prediction models. In the database, 89 and 37 experimental data for SFRC and UHPC beams without stirrups were collected, respectively, and the proposed equations were developed using the Bayesian parameter estimation approach. The proposed models have a simplified form with important parameters, and in comparison to the existing prediction models, provide unbiased high prediction accuracy.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 한국정보통신학회논문지
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• 제18권10호
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• pp.2562-2570
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• 2014

단백질의 기능을 유추할 수 있는 중요한 정보중의 하나는 단백질이 존재하는 세포내 위치이다. 최근에는 하나의 단백질이 동시에 존재하는 여러 세포내 위치를 예측하는 연구가 활발하다. 본 논문에서는 단백질이 존재하는 세포내의 다중위치를 예측하기 위해서 레이블 멱집합 방법을 개선한다. 레이블 멱집합 방법으로 분류한 다중위치들을 예측 확률에 따라 결합하여 최종적인 다중레이블로 분류한다. 각 다중위치에 대한 정확한 확률적 기여를 구하기 위하여 쌍별 비교와 오류정정 출력코드를 사용한 다중클래스 확률추정 방법을 적용하였다. 단백질 세포내 위치 예측 실험에 제안한 방법을 적용하여 성능이 향상됨을 보였다.