• 제목/요약/키워드: simple prediction

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재무부실화 예측을 위한 랜덤 서브스페이스 앙상블 모형의 최적화 (Optimization of Random Subspace Ensemble for Bankruptcy Prediction)

  • 민성환
    • 한국IT서비스학회지
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    • 제14권4호
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    • pp.121-135
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    • 2015
  • Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

Implementation of Fund Recommendation System Using Machine Learning

  • Park, Chae-eun;Lee, Dong-seok;Nam, Sung-hyun;Kwon, Soon-kak
    • Journal of Multimedia Information System
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    • 제8권3호
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    • pp.183-190
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    • 2021
  • In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

위성 통신 링크에서 강우 감쇠 보상을 위한 신호 레벨 예측기법 (A Signal-Level Prediction Scheme for Rain-Attenuation Compensation in Satellite Communication Linkes)

  • 임광재;황정환;김수영;이수인
    • 한국통신학회논문지
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    • 제25권6A호
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    • pp.782-793
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    • 2000
  • 본 논문은 10GHz이상의 주파수 대역을 사용하는 위성 통신 링크에서 강우에 의해 감쇠된 신호 레벨을 동적으로 예측하기 위한 비교적 간단한 예측 기법을 제시한다. 예측 기법은 이산시간 저역 통과 필터링, 기울기에 근거한 예측, 평균 오차 보정, 고정 및 가변 혼합 예측 여유 할당의 4가지 기능 블록을 갖는다. Ku 대역의 측정 데이터로부터 주파수 스케일링에 의해 얻어진 Ka 대역 강우 감쇠 데이터를 이용하여 시뮬레이션을 수행하였다. 평균 오차 보정을 갖는 기울기 예측 기법은 1dB 이하의 표준 편차를 가지며, 평균 오차 보정에 의해 약 1.5~2.5 배의 예측 오차 감소를 보인다. 요구되는 평균 여유 면에서, 혼합 예측 여유 할당은 고정 여유 방법과 가변 여유 방법에 비해 더 적은 평균 여유를 요구한다.

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동적 데이터베이스 기반 태풍 진로 예측 (Dynamic data-base Typhoon Track Prediction (DYTRAP))

  • 이윤제;권혁조;주동찬
    • 대기
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    • 제21권2호
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    • pp.209-220
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    • 2011
  • A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

Sequential prediction of TBM penetration rate using a gradient boosted regression tree during tunneling

  • Lee, Hang-Lo;Song, Ki-Il;Qi, Chongchong;Kim, Kyoung-Yul
    • Geomechanics and Engineering
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    • 제29권5호
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    • pp.523-533
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    • 2022
  • Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R2≥0.79; whereas, a one-step prediction is R2≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R2 and UPD is proposed. When UPD increases R2 decreases exponentially; In particular, UPD at R2=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R2 value targeted by an operator. Therefore, a calculation process for the equation between the R2 and UPD is addressed.

관측치를 이용한 적응적 조위 예측 방법 (Adaptive Sea Level Prediction Method Using Measured Data)

  • 박상현
    • 한국전자통신학회논문지
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    • 제12권5호
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    • pp.891-898
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    • 2017
  • 기후변화 등으로 해안 침수 등의 피해가 증가하고 있으며, 이러한 피해를 줄이기 위해 해양을 지속적으로 모니터링하기 위한 연구들이 진행되고 있다. 본 논문에서는 해수면의 변화를 모니터링하기 위한 조위 센서에 적용할 수 있는 조위 예측 모델을 제안한다. 기존의 조위 예측 모델은 장기적인 예보를 위한 것으로 많은 데이터와 복잡한 알고리즘이 필요하다. 반면, 제안하는 알고리즘은 조위 센서에 탑재되어 동작할 수 있는 간단하지만 정확한 알고리즘으로, 센서에 의해 측정된 데이터를 기반으로 한 시간 또는 두 시간의 비교적 짧은 시간 후의 조위를 예측한다. 실험 결과는 제안하는 알고리즘이 간단하지만 정확하게 조위를 예측하는 것을 보여준다.

A simple prediction procedure of strain-softening surrounding rock for a circular opening

  • Wang, Feng;Zou, Jin-Feng
    • Geomechanics and Engineering
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    • 제16권6호
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    • pp.619-626
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    • 2018
  • A simple prediction procedure was investigated for calculating the stresses and displacements of a circular opening. Unlike existed approaches, the proposed approach starts each step with a radius increment. The stress for each annulus could be obtained analytically, while strain increments for each step can be determinate numerically from the compatility equation by finite difference approximation, flow rule and Hooke's law. In the successive manner, the distributions of stresses and displacements could be found. It should be noted that the finial radial stress and displacement were equal to the internal supporting pressure and deformation at the tunnel wall, respectively. By assuming different plastic radii, GRC and the evolution curve of plastic radii and internal supporting pressures could be obtained conveniently. Then the real plastic radius can be calculated by using linear interpolation in the evolution curve. Some numerical and engineering examples were performed to demonstrate the accuracy and validity for the proposed procedure. The comparisons results show that the proposed procedure was faster than that in Lee and Pietrucszczak (2008). The influence of annulus number and dilation on the accuracy of solutions was also investigated. Results show that the larger the annulus number was, the more accurate the solutions were. Solutions in Park et al. (2008) were significantly influenced by dilation.

Solar radiation forecasting using boosting decision tree and recurrent neural networks

  • Hyojeoung, Kim;Sujin, Park;Sahm, Kim
    • Communications for Statistical Applications and Methods
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    • 제29권6호
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    • pp.709-719
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    • 2022
  • Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.

Time-dependent stresses and curvatures in cracked R.C. sections under working loads

  • Al-Zaid, Rajeh Z.
    • Structural Engineering and Mechanics
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    • 제18권3호
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    • pp.363-376
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    • 2004
  • The present study provides a relatively simple and accurate analytical model for the prediction of time-dependent stresses and curvatures of cracked R.C. sections under working loads. A more simplified solution is also provided. The proposed models are demonstrated by considering a numerical example and conducting a parametric study on the effects of relevant R.C. design parameters. In contrary to tension reinforcement, the compression reinforcement is found to contribute significantly in reducing tensile stresses in tension steel and in reducing the total section curvatures. The good accuracy of the proposed approximate solution opens a new vision towards a simple yet accurate model for the prediction of time-dependent effects in R.C. structures.

간단한 손실모델을 이용한 단단축류압축기 탈설계점 성능예측 (Off-Design Performance Prediction of an Axial Flow Compressor Stage Using Simple Loss Correlations)

  • 김병남;정명균
    • 대한기계학회논문집
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    • 제18권12호
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    • pp.3357-3368
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    • 1994
  • Total pressure losses required to calculate the total-to-total efficiency are estimated by integrating empirical loss coefficients of four loss mechanisms along the mean-line of blades as follows; blade profile loss, secondary flow loss, end wall loss and tip clearance loss. The off-design points are obtained on the basis of Howell's off-design performance of a compressor cascade. Also, inlet-outlet air angles and camber angle are obtained from semi-empirical relations of transonic airfoils' minimum loss incidence and deviation angles. And nominal point is replaced by the design point. It is concluded that relatively simple loss models and Howell's off-design data permit us to calculate the off-design performance with satisfactory accuracy. And this method can be easily extended for off-design performance prediction of multi-stage compressors.