• ETRI Journal
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• 제42권3호
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• 한국재난정보학회 논문집
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• 제13권4호
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• pp.491-498
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• 2017

본 연구의 주 목적은 연성 도로포장의 소성변형 예측모델을 개발하는 것이다. 목적을 수행하기 위하여 다양한 실험실 시험이 수행되었다. 소성변형 량을 측정하기 위하여 측면 구속압을 제공하는 새로운 반복 일축압축시험이 채택되었으며 소성변형 예측모델은 층별-변형률 이론이 적용되었다. 예측모델의 소성계수는 아스팔트 콘크리트 재료의 소성변형시험을 통하여 결정되었다. 본 연구가 수행된 범위내에서 반복 일축압축시험을 통한 연성포장의 소성변형 예측모델이 제안되었다. 제안된 소성변형 예측모델은 연성포장 층 재료의 거동을 적절하게 모사하는 것으로 나타났다.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• 한국도로학회논문집
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• 제10권3호
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• pp.199-208
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• 2008

포장상태 예측은 의사결정과정에서 포장의 공용성능을 평가하고 사업대상구간의 우선순위를 선정하기 위한 적정한 정보를 제공해준다. 근래들어 현재의 포장상태가 장래에 어느 정도 저하되는지를 예측하려는 많은 접근이 있었으나 포장의 서비스수명을 적정히 예측하는 데에는 한계를 보여왔다. 본 논문에서는 포장상태 예측방법을 개선하기 위하여 포장상태 공용성모형과 포장상태 예측모형을 개발하였다. 공용성 모형은 실제 포장상태 분석결과를 회귀분석하여 포장의 종류별, 교통량별로 백분위 50%, 25%, 15%, 5%의 확률분포 모형을 도출한 것이다. 예측모형은 앞서 도출된 공용성모형 모형식을 기준으로 하여 대상구간 각각의 포장상태 측정값에 의해 포장상태 확률을 결정한다. 개발된 예측모형의 검증을 위하여 비교대상구간을 선정하였고, HPCI의 평균값 표준편차, 3.0이하 비율을 비교분석하였다. 이를 통하여 기존예측모형이 안고 있는 교통량, 재령, 현재 포장 상태를 고려하여 보다 현실에 부합되는 포장상태를 예측하는 방법을 제공하고자 한다.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.191-196
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• 2015

최근 기업의 실적 및 주가를 예측하기 위해 매출액증가율, 부채비율 등의 다양한 예측변수를 활용하여 정량적인 예측방법을 활용하는 연구가 많이 이루어지고 있다. 기업실적 및 주가를 정량적 예측하기 위해 수많은 예측변수들 중에서 모델구축을 위해 중요한 예측변수를 선정하는 것이 중요하다. 대부분의 기존연구들에서는 다양한 알고리즘을 활용하여 예측변수들을 제거하는 방법을 사용하는 경우가 많았다. 이러한 경우 각 예측변수들이 가지는 많은 정보들이 제거되는 문제점이 존재한다. 이러한 문제점을 해결하기 위해 본 연구에서는 예측모델 구축을 위해 예측변수들을 제거하는 대신 각 변수들이 가지고 있는 정보를 병합하여 새로운 변수를 생성하는 대표적인 차원축소 방법인 주성분분석(PCA)을 활용하였다. 본 연구에서는 제안된 예측모델을 미국의 전자, 전기기업의 재무정보를 활용하여 구축하고 예측성능을 실증적으로 분석해 보았다.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1009-1012
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• 1999

An analytical performance model that can predict the performance of a superscalar processor employing multiple branch prediction is introduced. The model is based on the conditional independence probability and the basic block size of instructions, with the degree of multiple branch prediction, the fetch rate, and the window size of a superscalar architecture. Trace driven simulation is performed for the subset of SPEC integer benchmarks, and the measured IPCs are compared with the results derived from the model. As the result, our analytic model could predict the performance of the superscalar processor using multiple branch prediction within 6.6 percent on the average.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 1998년도 유체기계 연구개발 발표회 논문집
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• pp.33-41
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• 1998

In this study, the performance prediction programs for centrifugal pumps are developed. To estimate the losses in the centrifugal pump impellers, two-zone model and TEIS(two elements in series) model are applied to the program. The basic concept of two zone model considers the primary zone that is an isentropic core flow and the secondary zone that is non-isentropic region at the impeller exit. The flows through two different zones mixed out at the impeller exit and the mixing process occurs with an increase in entropy, a decrease in total pressure. The level of the core flow diffusion in a impeller was calculated using TEIS(two elements in series) model. The effects of various parameters which are used in this program on the prediction of head and efficiency are discussed. The correlation curves to select the effectiveness of the primitive TEIS model were suggested according to the specific speed of the centrifugal pumps.

• 한국유체기계학회 논문집
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• 제2권1호
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• pp.56-63
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• 1999

In this study, the performance prediction programs for centrifugal pumps are developed. To estimate the losses in the centrifugal pump impellers, a two-zone model and TEIS(two elements in series) model are applied to the program. The basic concept of a two zone model considers the primary zone that is an isentropic core flow and the secondary zone that has a non-isentropic region at the impeller exit. The flow goes through two different zones and is mixed out at the impeller exit and the mixing process occurs with an increase in entropy, a decrease in total pressure. The level of the core flow diffusion in an impeller was calculated using TEIS(two elements in series) model. The effects of various parameters which are used in this program on the prediction of head and efficiency are discussed. The correlation curves used to select the effectiveness of the primitive TEIS model were suggested according to the specific speed of the centrifugal pumps.

• International journal of advanced smart convergence
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• 제7권1호
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.