• Title/Summary/Keyword: Performance Models

Search Result 7,848, Processing Time 0.031 seconds

Selection framework of representative general circulation models using the selected best bias correction method (최적 편이보정 기법의 선택을 통한 대표 전지구모형의 선정)

  • Song, Young Hoon;Chung, Eun-Sung;Sung, Jang Hyun
    • Journal of Korea Water Resources Association
    • /
    • v.52 no.5
    • /
    • pp.337-347
    • /
    • 2019
  • This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM's and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.

Exploration of Support Plans for 2015 Integrated Science Curriculum through the Performance Evaluation of Implemented Teacher Training Programs (교사연수 성과평가를 통한 2015 통합과학 교육과정 현장 정착 방안 탐색)

  • Kwak, Youngsun
    • Journal of The Korean Association For Science Education
    • /
    • v.39 no.2
    • /
    • pp.197-205
    • /
    • 2019
  • The purpose of this study is to derive ways to support Integrated Science curriculum implementation by evaluating the results of Integrated Science teacher training programs conducted by the Ministry of Education to support the settlement of 2015 revised Integrated Science curriculum. Teachers' output from the teacher training programs and interviews with training instructors in the 2017 Integrated Science Leading Teacher Training program were analyzed to derive the features of the Integrated Science curriculum and support plans for the implementation of Integrated Science in schools. Teachers who participated in the 2017 Integrated Science Leading Teacher Training program developed teaching, & learning and evaluation plans through participatory training sessions, where the achievement standards most selected by teachers were [10IS08-03] and [10IS09-04]. Through the text mining analysis of these achievement standards, we explored the implementation realities such as reconstruction of achievement standards, teaching and learning methods, learning materials, evaluation methods, and subject competencies. In addition, we analyzed exemplary reconstruction models of achievement standards in light of best integrated instruction, student-participatory instruction, and developing science competencies. Based on the results, we propose teacher training support plans and further studies for the implementation and settlement of the Integrated Science curriculum.

White striping degree assessment using computer vision system and consumer acceptance test

  • Kato, Talita;Mastelini, Saulo Martiello;Campos, Gabriel Fillipe Centini;Barbon, Ana Paula Ayub da Costa;Prudencio, Sandra Helena;Shimokomaki, Massami;Soares, Adriana Lourenco;Barbon, Sylvio Jr.
    • Asian-Australasian Journal of Animal Sciences
    • /
    • v.32 no.7
    • /
    • pp.1015-1026
    • /
    • 2019
  • Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

Development of Simplified Immersed Boundary Method for Analysis of Movable Structures (가동물체형 구조물 해석을 위한 Simplified Immersed Boundary법의 개발)

  • Lee, Kwang-Ho;Kim, Do-Sam
    • Journal of Korean Society of Coastal and Ocean Engineers
    • /
    • v.33 no.3
    • /
    • pp.93-100
    • /
    • 2021
  • Since the IB (Immersed Boundary) method, which can perform coupling analysis with objects and fluids having an impermeable boundary of arbitrary shape on a fixed grid system, has been developed, the IB method in various CFD models is increasing. The representative IB methods are the directing-forcing method and the ghost cell method. The directing-forcing type method numerically satisfies the boundary condition from the fluid force calculated at the boundary surface of the structure, and the ghost-cell type method is a computational method that satisfies the boundary condition through interpolation by placing a virtual cell inside the obstacle. These IB methods have a disadvantage in that the computational algorithm is complex. In this study, the simplified immersed boundary (SIB) method enables the analysis of temporary structures on a fixed grid system and is easy to expand to three proposed dimensions. The SIB method proposed in this study is based on a one-field model for immiscible two-phase fluid that assumes that the density function of each phase moves with the center of local mass. In addition, the volume-weighted average method using the density function of the solid was applied to handle moving solid structures, and the CIP method was applied to the advection calculation to prevent numerical diffusion. To examine the analysis performance of the proposed SIB method, a numerical simulation was performed on an object falling to the free water surface. The numerical analysis result reproduced the object falling to the free water surface well.

Target Length Estimation of Target by Scattering Center Number Estimation Methods (산란점 수 추정방법에 따른 표적의 길이 추정)

  • Lee, Jae-In;Yoo, Jong-Won;Kim, Nammoon;Jung, Kwangyong;Seo, Dong-Wook
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.38 no.6
    • /
    • pp.543-551
    • /
    • 2020
  • In this paper, we introduce a method to improve the accuracy of the length estimation of targets using a radar. The HRRP (High Resolution Range Profile) obtained from a received radar signal represents the one-dimensional scattering characteristics of a target, and peaks of the HRRP means the scattering centers that strongly scatter electromagnetic waves. By using the extracted scattering centers, the downrange length of the target, which is the length in the RLOS (Radar Line of Sight), can be estimated, and the real length of the target should be estimated considering the angle between the target and the RLOS. In order to improve the accuracy of the length estimation, parametric estimation methods, which extract scattering centers more exactly than the method using the HRRP, can be used. The parametric estimation method is applied after the number of scattering centers is determined, and is thus greatly affected by the accuracy of the number of scattering centers. In this paper, in order to improve the accuracy of target length estimation, the number of scattering centers is estimated by using AIC (Akaike Information Criteria), MDL (Minimum Descriptive Length), and GLE (Gerschgorin Likelihood Estimators), which are the source number estimation methods based on information theoretic criteria. Using the ESPRIT algorithm as a parameter estimation method, a length estimation simulation was performed for simple target CAD models, and the GLE method represented excellent performance in estimating the number of scattering centers and estimating the target length.

Development and Validation of the GPU-based 3D Dynamic Analysis Code for Simulating Rock Fracturing Subjected to Impact Loading (충격 하중 시 암석의 파괴거동해석을 위한 GPGPU 기반 3차원 동적해석기법의 개발과 검증 연구)

  • Min, Gyeong-Jo;Fukuda, Daisuke;Oh, Se-Wook;Cho, Sang-Ho
    • Explosives and Blasting
    • /
    • v.39 no.2
    • /
    • pp.1-14
    • /
    • 2021
  • Recently, with the development of high-performance processing devices such as GPGPU, a three-dimensional dynamic analysis technique that can replace expensive rock material impact tests has been actively developed in the defense and aerospace fields. Experimentally observing or measuring fracture processes occurring in rocks subjected to high impact loads, such as blasting and earth penetration of small-diameter missiles, are difficult due to the inhomogeneity and opacity of rock materials. In this study, a three-dimensional dynamic fracture process analysis technique (3D-DFPA) was developed to simulate the fracture behavior of rocks due to impact. In order to improve the operation speed, an algorithm capable of GPGPU operation was developed for explicit analysis and contact element search. To verify the proposed dynamic fracture process analysis technique, the dynamic fracture toughness tests of the Straight Notched Disk Bending (SNDB) limestone samples were simulated and the propagation of the reflection and transmission of the stress waves at the rock-impact bar interfaces and the fracture process of the rock samples were compared. The dynamic load tests for the SNDB sample applied a Pulse Shape controlled Split Hopkinson presure bar (PS-SHPB) that can control the waveform of the incident stress wave, the stress state, and the fracture process of the rock models were analyzed with experimental results.

Doubly-robust Q-estimation in observational studies with high-dimensional covariates (고차원 관측자료에서의 Q-학습 모형에 대한 이중강건성 연구)

  • Lee, Hyobeen;Kim, Yeji;Cho, Hyungjun;Choi, Sangbum
    • The Korean Journal of Applied Statistics
    • /
    • v.34 no.3
    • /
    • pp.309-327
    • /
    • 2021
  • Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

Effects of photobiomodulation on different application points and different phases of complex regional pain syndrome type I in the experimental model

  • Canever, Jaquelini Betta;Barbosa, Rafael Inacio;Hendler, Ketlyn Germann;Neves, Lais Mara Siqueira das;Kuriki, Heloyse Uliam;Aguiar, Aderbal Silva Junior;Fonseca, Marisa de Cassia Registro;Marcolino, Alexandre Marcio
    • The Korean Journal of Pain
    • /
    • v.34 no.3
    • /
    • pp.250-261
    • /
    • 2021
  • Background: Complex regional pain syndrome type I (CRPS-I) consists of disorders caused by spontaneous pain or induced by some stimulus. The objective was to verify the effects of photobiomodulation (PBM) using 830 nm wavelength light at the affected paw and involved spinal cord segments during the warm or acute phase. Methods: Fifty-six mice were randomized into seven groups. Group (G) 1 was the placebo group; G2 and G3 were treated with PBM on the paw in the warm and acute phase, respectively; G4 and G5 treated with PBM on involved spinal cord segments in the warm and acute phase, respectively; G6 and G7 treated with PBM on paw and involved spinal cord segments in the warm and acute phase, respectively. Edema degree, thermal and mechanical hyperalgesia, skin temperature, and functional quality of gait (Sciatic Static Index [SSI] and Sciatic Functional Index [SFI]) were evaluated. Results: Edema was lower in G3 and G7, and these were the only groups to return to baseline values at the end of treatment. For thermal hyperalgesia only G3 and G5 returned to baseline values. Regarding mechanical hyperalgesia, the groups did not show significant differences. Thermography showed increased temperature in all groups on the seventh day. In SSI and SFI assessment, G3 and G7 showed lower values when compared to G1, respectively. Conclusions: PBM irradiation in the acute phase and in the affected paw showed better results in reducing edema, thermal and mechanical hyperalgesia, and in improving gait quality, demonstrating efficacy in treatment of CRPS-I symptoms.

The Prediction of Cryptocurrency on Using Text Mining and Deep Learning Techniques : Comparison of Korean and USA Market (텍스트 마이닝과 딥러닝을 활용한 암호화폐 가격 예측 : 한국과 미국시장 비교)

  • Won, Jonggwan;Hong, Taeho
    • Knowledge Management Research
    • /
    • v.22 no.2
    • /
    • pp.1-17
    • /
    • 2021
  • In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

Analysis of Operation Efficiency in Private University Using the DEA (DEA를 활용한 국내 사립대학 운영 효율성 분석)

  • Bae, Young-Min;Han, Seung-Jo
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.22 no.2
    • /
    • pp.67-75
    • /
    • 2021
  • The structure of universities needs to be adjusted and reformed to cope with the decrease in admission resources and the quality of education due to the low birth rate and aging population. Such a policy is receiving much attention. To analyze the relative efficiency of private universities in Korea from the perspective of resource and performance, this study evaluated the efficiency of private university operation by applying a DEA(Data Envelopment Analysis) technique. The DEA measurements were compared with the diagnosis results of the department of education (Government) in 2018. The input and output variables used in the research analysis were utilized by the university's notification materials (public disclosure information). An analysis of the operational efficiency showed that 48% (12 universities) of the 25 DMUs (Decision Making Unit) were efficient for DEA-BCC models and that some of the capacity-building universities were operating efficiently. In addition, the DEA analysis found ways to improve inefficient groups through DEA-Additive results. This paper can be meaningful because it confirmed the relative efficiency of private universities and suggested improvement directions through the DEA method, which is characterized by the simultaneous consideration of various input and output factors. This will help apply the limited resources related to the input and output elements of each university.