• 제목/요약/키워드: predictive accuracy

검색결과 782건 처리시간 0.041초

투자와 수출 및 환율의 고용에 대한 의사결정 나무, 랜덤 포레스트와 그래디언트 부스팅 머신러닝 모형 예측 (Investment, Export, and Exchange Rate on Prediction of Employment with Decision Tree, Random Forest, and Gradient Boosting Machine Learning Models)

  • 이재득
    • 무역학회지
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    • 제46권2호
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    • pp.281-299
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    • 2021
  • This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

Integration of Multi-spectral Remote Sensing Images and GIS Thematic Data for Supervised Land Cover Classification

  • Jang Dong-Ho;Chung Chang-Jo F
    • 대한원격탐사학회지
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    • 제20권5호
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    • pp.315-327
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    • 2004
  • Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

Neuro-fuzzy optimisation to model the phenomenon of failure by punching of a slab-column connection without shear reinforcement

  • Hafidi, Mariam;Kharchi, Fattoum;Lefkir, Abdelouhab
    • Structural Engineering and Mechanics
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    • 제47권5호
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    • pp.679-700
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    • 2013
  • Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

An Approach for Identifying the Temperature of Inductance Motors by Estimating the Rotor Slot Harmonic Based on Model Predictive Control

  • Wang, Liguo;Jiang, Qingyue;Zhang, Chaoyu;Jin, Dongxin;Deng, Hui
    • Journal of Power Electronics
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    • 제17권3호
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    • pp.695-703
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    • 2017
  • In order to satisfy the urgent requirements for the overheating protection of induction motors, an approach that can be used to identify motor temperature has been proposed based on the rotor slots harmonic (RSH) in this paper. One method to accomplish this is to improve the calculation efficiency of the RSH by predicting the stator winding distribution harmonic order by analyzing the harmonics spectrum. Another approach is to increase the identification accuracy of the RSH by suppressing the influence of voltage flashes or current surges during temperature estimation based on model predictive control (MPC). First, an analytical expression of the stator inductance is extracted from a steady-state positive sequence motor equivalent circuit model developed from the rotor flux field orientation. Then a procedure that applies MPC for reducing the identification error of the rotor temperature caused by voltage sag or swell of the power system is given. Due to this work, the efficiency and accuracy of the RSH have been significantly improved and validated our experiments. This work can serves as a reference for the on-line temperature monitoring and overheating protection of an induction motor.

경흉세침흡인 생검에 의한 폐질환의 세포병리학적 진단 (Cytopathologic Diagnosis of Pulmonary Diseases by Transthoracic Fine Needle Aspiration Biopsy)

  • 박인애;함의근
    • 대한세포병리학회지
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    • 제1권1호
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    • pp.27-35
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    • 1990
  • The authors report series of 360 cases of transthoracic fine-needle aspiration cytology (TFNA) from Oct. 1982, through Aug. 1986 at the Seoul National University Hospital. A diagnosis of neoplastic lesion was established in 50.3% of the cases. A non-neoplastic diagnosis was made in 38.5%, nondiagnostic one in 6.5% and inadequate one in 4.7% of the total. Statistical findings on cytological diagnoses were as follows. Specificity was 100% ; sensitivity, 92% ; predictive value for positive, 1.0 ; predictive value for negative, 0.9 ; concordance rate, 84.2% ; diagnostic accuracy in non-neoplastic lesion, 65.4%, and typing accuracy in malignant tumor, 0.77.

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개선된 적응 예측 필터를 이용한 새로운 능동전력필터용 기준신호발생기 (Novel Reference Signal Generator for Active Power Filter Using Improved Adaptive Predictive Filter)

  • 배병열;김희중;한병문
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2003년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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    • pp.212-216
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    • 2003
  • The performance of active power filter depends on the inverter characteristic, the control method, and the accuracy of reference signal generator. The accuracy of reference generator is the most critical item to determine the performance of active power filter. This paper introduces a novel reference signal generator composed of improved adaptive predictive filter. The performance of proposed reference signal generator was verified by means of simulation with MATLAB. The simulation result confirm that the proposed reference signal generator can be utilized for the active power filter.

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비정규화 데이터를 이용한 신경망 소프트웨어 신뢰성 예측 (Neural Network for Softwar Reliability Prediction ith Unnormalized Data)

  • 이상운
    • 한국정보처리학회논문지
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    • 제7권5호
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    • pp.1419-1425
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    • 2000
  • When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

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A NEW SYSTEM OF VISUAL PRESENTATION OF ANALYSIS OF TEST PERFORMANCE: THE 'DOUBLE-RING' DIAGRAM

  • Stefadouros Miltiadis A.
    • 대한예방의학회:학술대회논문집
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    • 대한예방의학회 1994년도 교수 연수회(역학)
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    • pp.142-149
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    • 1994
  • Substitution of graphic representation for extensive lists of numerical statistical data is highly desirable by both editors and readers of medical journals, faced with an exploding abundance of contemporary medical literature. A novel graphic tool. the 'double-ring diagram', is described herein which permits visual representation of information regarding certain statistical variables used to describe the performance of a test or physical sign in the diagnosis of a disease. The diagram is relatively easy to construct on the basis of a number of primary data such as the prevalence and the true positive, true negative. false positive and false negative test results. These values are reflected in the diagram along with the values of other statistical variables derived from them. such as the sensitivity. specificity, predictive values for positive and negative test result. and accuracy. This diagram may be useful in visualizing a test's performance and facilitating visual comparison of performance of two or more tests.

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흡인세포검사의 세포-병리학적 검색 (Cytohistopathologic Comparative Study of Aspiration Biopsy Cytology from Various Sites)

  • 박효숙
    • 대한세포병리학회지
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    • 제2권1호
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    • pp.8-19
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    • 1991
  • A statistical analysis of the diagnostic value for 244 aspiration biopsy cytology(ABC) among a total 1,043 cases from various sites was performed. ABC, using diagnostic terminology similar to that of a surgical pathology reports, was compared to the final tissue diagnosis. For the entire series, a sensitivity of 91.8%, a specificity of 99.3%, a positive predictive value of 98.9%, a negative predictive value of 94,8%, and an efficacy of the test of 96.3% were shown. There were 8 false negative and 1 false positive diagnosis. The diagnostic accuracy was 89.8%. Those results indicate that the ABC is a considerably highly accurate procedure that should be routinely employed.

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A Novel Discrete-Time Predictive Current Control for PMSM

  • Sun, Jung-Won;Suh, Jin-Ho;Lee, Young-Jin;Lee, Kwon-Soon
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.1915-1919
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    • 2004
  • In this paper, we propose a new discrete-time predictive current controller for a PMSM(Permanent Magnet Synchronous Motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that it takes to apply the voltage to motor. A new control strategy in this paper is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking and is proposed simplified calculation. Moreover, the validity of the proposed method is demonstrated by numerical simulations and the simulation results will be verified the improvements of predictive controller and accuracy of the current controller.

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