• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• 대한조선학회논문집
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• 제57권3호
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• Computers and Concrete
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• 제26권6호
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• pp.533-546
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• 2020

The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.

• Korean Journal of Radiology
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• 제24권6호
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• 한국시뮬레이션학회논문지
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• 제17권4호
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• pp.61-69
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• 2008

이산사건 시뮬레이션에서의 미래사건 리스트 관리에 요구되는 우선순위 큐의 성능을 평가하기 위하여 사건의 삽입과 삭제패턴을 묘사한 성능 모델이 필요하다. 성능 모델을 이용하여 다양한 우선순위 큐 구조를 시간 복잡성 측면에서 비교 평가할 수 있다. 본 연구는 대상이 되는 시뮬레이션 모델이 반복적으로 운용되고, 실행 시간이 유한적인 경우에 보다 정확한 성능모델을 작성하는 방안을 제시한다. 제안된 성능모델은 다단계 마코브 프로세스 모델에 기반을 두어 확정적인 순서에 의한 삽입과 삭제를 하기 보다는 확률적인 패턴에 의해 연산 순서를 결정한다. 대한민국 육군의 전쟁 연습 모델인 창조 모델을 운영한 결과를 바탕으로 다단계 마코브 프로세스 모델을 작성한 사례연구를 포함하였다.

• 대한산업공학회지
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• 제40권3호
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• pp.342-357
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• 2014

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.63-79
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• 2022

Past studies on the influence of corporate social responsibility (CSR) activities on firms have been inconsistent, highlighting the significance of examining how CSR affects the performance of Malaysian government-linked companies (GLCs). The study aims to investigate the impact of CSR dimensions (economic, legal, ethical, and philanthropic) on firm performance from 2016 to 2020 using a sample of 31 GLCs from the top 100 companies under the Main Board of Bursa Malaysia. A total of 35 GLCs were selected as the study sample size based on the top 100 businesses listed under the board of Bursa Malaysia as of 31 December 2020. The study employed correlation and multiple linear regression models to examine the relationship between CSR dimensions and firm performance. Financial performance is evaluated using accounting-based models of return on assets (ROA) and return on equity (ROE) and market-based models of earnings per share (EPS) and market value (MV). The CSRHub database was employed to collect information on the performance of company CSR dimensions. The findings suggested a significant positive relationship between ethical and philanthropic CSR and firm performance regarding ROE. Thus, GLCs prioritized ethical and philanthropic CSR over other dimensions.

• 경영과학
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• 제15권1호
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• pp.63-76
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• 1998

Reliability evaluation methodologies of the multipath MIN system are reviewed and critically compared. Some guidelines are proposed to select efficient evaluation method for the system designers to use. Considering the switch failure and repair characteristics of the MIN system, three types of Markov models are proposed for the MIN system availability models. These models can be used for the MIN performance analysis. The performance of the MIN system are supposed to vary according to the failure state of the system.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.183-186
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• 2003

In this paper, performance evaluation of HNCP home network is using stochastic activity network models is proposed. HNCP is a home network protocol for controling and monitoring home appliances using power line communication. a CSMA/CA with packet drop method is used in HNCP MAC layer. Using the proposed stochastic activity network models. performances of HNCP home networks with error-free environment and error environment are evaluated.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권8호
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• pp.1043-1053
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• 2004

컴포넌트 기반 소프트웨어에서는 사용될 컴포넌트의 성능이 개발되는 소프트웨어의 품질 확보에 있어서 매우 중요한 요소이다. 컴포넌트 성능에는 흔히 컴포넌트 모델이나 미들웨어에 종속적인 성능측정 도구가 이용된다. 이러한 성능측정 방법에서는 소프트웨어의 개발환경이 변경될 경우 측정 도구도 함께 수정되어야 한다는 문제점을 가지고 있다. 또한, 여러 가지 다른 모델의 컴포넌트들을 한 시스템으로 통합하는 경우에도 유사한 어려움이 존재한다. 본 논문은 이러한 문제에 대한 해결 방법으로 컴포넌트 모델이나 미들웨어에 독립적인 성능측정 방법을 제안한다. 제안된 방법은 미들웨어에 공통적으로 적용 가능한 성능측정기 모델에서 특정 미들웨어를 위한 성능측정기 모델로의 모델변환 과정을 통해 성능데이타간의 상호운용성을 보장한다. 이 방법을 이용하면 동일한 컴포넌트 모델에 따른 컴포넌트들뿐만 아니라 서로 다른 컴포넌트 모델을 기반으로 하는 컴포넌트들에 대한 성능측정이 가능하다.