• Journal of Korea Trade
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• v.23 no.5
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• pp.27-44
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• 2019

Purpose - The purpose of this paper is to analyze the net effect of the green total factor productivity (GTFP) of China's manufacturing industry from the China-Korea Free Trade Area (China-Korea FTA) quantitatively. Design/methodology - Firstly, the Global Malmquist-Luenberger (GML) index based on the SBM directional distance function is used to measure the GTFP of China's manufacturing and analyze the driving force for its growth. Secondly, the regression discontinuity quantitative analysis is used to determine the impact of the China-Korea FTA on China's manufacturing GTFP. Findings - Our main findings can be summarized as follows: the China-Korea FTA has promoted the GTFP of China's manufacturing with an effect evaluation mainly resulting from green technology progress. And there is industry heterogeneity in the policy effect on the manufacturing GTFP due to the China-Korea FTA. Namely, policy promotion from the China-Korea FTA is more effective on the GTFP of equipment manufacturing than it is on those of other industries. Originality/value - First, an evaluation and analysis of the GTFP development of China's manufacturing that employs GML index based on SBM directional distance function. Second, a quantitative estimate of China-Korea FTA's net effect on China's manufacturing industrial GTFP that uses regression discontinuity analysis, which is considered to be the closest method to natural experiments and superior to other causal inference methods. Third, an in-depth discussion of the practical steps that China's manufacturing can take to improve GTFP development and integrate China-Korea FTA construction into economic development.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.285-298
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• 2022

Continuous shrinkage priors, as well as spike and slab priors, have been widely employed for Bayesian inference about sparse regression coefficient vectors or covariance matrices. Continuous shrinkage priors provide computational advantages over spike and slab priors since their model space is substantially smaller. This is especially true in high-dimensional settings. However, variable selection based on continuous shrinkage priors is not straightforward because they do not give exactly zero values. Although few variable selection approaches based on continuous shrinkage priors have been proposed, no substantial comparative investigations of their performance have been conducted. In this paper, We compare two variable selection methods: a credible interval method and the sequential 2-means algorithm (Li and Pati, 2017). Various simulation scenarios are used to demonstrate the practical performances of the methods. We conclude the paper by presenting some observations and conjectures based on the simulation findings.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Asia Marketing Journal
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• v.9 no.2
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• pp.1-21
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• 2007

This research investigates the role of causal inferences about the endorser's motivation - specifically, dispositional versus situational attributions - and their impact on persuasion of noncelebrity testimonial advertisements. Based on the correspondent inference theory and the relevant literature, it is posited that consumers will generate predictable patterns of attributional responses to testimonial messages, which in turn will influence ad and brand evaluations. An experiment with 335 consumer panelists, after a pilot experiment with the college student sample, has been conducted. Results suggest the greater impact of dispositional attributions than situational attributions on persuasion of noncelebrity testimonial messages and general evocations of situational attributions regardless of the levels of endorser credibility and dispositional attributions. On the basis of the findings from this study, theoretical and practical implications are discussed, as are directions for future research.

• The Journal of Bigdata
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• v.9 no.1
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• pp.89-100
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• 2024

In the field of computer vision, models such as You Look Only Once (YOLO) and ResNet are widely used due to their real-time performance and high accuracy. However, to apply these models in real-world environments, factors such as runtime compatibility, memory usage, computing resources, and real-time conditions must be considered. This study compares the characteristics of three deep model runtimes: ONNX Runtime, TensorRT, and OpenCV DNN, and analyzes their performance on two models. The aim of this paper is to provide criteria for runtime selection for practical applications. The experiments compare runtimes based on the evaluation metrics of time, memory usage, and accuracy for vehicle license plate recognition and classification tasks. The experimental results show that ONNX Runtime excels in complex object detection performance, OpenCV DNN is suitable for environments with limited memory, and TensorRT offers superior execution speed for complex models.

• Journal of Korean Home Economics Education Association
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• v.31 no.3
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• pp.155-177
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• 2019

The study aims to develop a teaching-learning plan that can solve the problem of the clothing and textiles area faced by the teenager as course of critical science perspective improving the empowerment. As a research method, it was conceptualized by applying the Laster(1986)'s curriculum development process. And it was applied to the conceptual framework of practical reasoning presented in: "Family, Food and Society A Teacher's guide" (Staaland & Storm, 1996). The results of this study are summarized as follows. First, based on the results of reviewing literature related to the clothing and textiles area, ongoing concerns related to the clothing and textiles is "Should we do with regard to clothing and textiles for families in the community? The valued ends is defined as a complex position with a high degree of freedom and a high responsibility, and the goal of learning is interdependence, emotional maturity, intellectual development, and communication ability. For the contents of education and activity structure, practical reasoning process was used as conceptual framework of education contents, and included sub-concerns, broad concepts, sub-concepts and intellectual and social skills. Second, based on the practical reasoning, we developed a teaching and learning plan in the clothing and textiles. As a result, a total of 12 plan of 5 modules were developed. And were developed a total of 31 tutorials, reading materials, picture materials, group activities, and video materials. The results of this study can be applied to teachers who want to try out practical inference process in class or teachers who have difficulty in practicing reasoning process in the field.

• Journal of Korean Academy of Nursing Administration
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• v.3 no.1
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• pp.51-64
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• 1997

The purpose of the study was to introduce an integrated approach of new Casuistry and specified principlism in resolving ethical problems and studying nursing ethics. In studying clinical ethics and nursing ethics, there is no systematic research method. While nurses often experience ethical dilemmas in practice, much of previous research on nursing ethics has focused merely on describing the existing problems. In addition, ethists presented theoretical analysis and critics rather than providing the specific problems solving strategies. There is a need in clinical situations for an integrated method which can provide the objective description for existing problem situations as well as specific problem solving methods. We inherit two distinct ways of discussing ethical issues. One of these frames these issues in terms of principles, rules, and other general ideas; the other focuses on the specific features of particular kinds of moral cases. In the first way general ethical rules relate to specific moral cases in a theoretical manner, with universal rules serving as "axioms" from which particular moral judgments are deduced as theorems. In the seconds, this relation is frankly practical. with general moral rules serving as "maxims", which can be fully understood only in terms of the paradigmatic cases that define their meaning and force. Theoretical arguments are structured in ways that free them from any dependence on the circumstances of their presentation and ensure them a validity of a kind that is not affected by the practical context of use. In formal arguments particular conclusions are deduced from("entailed by") the initial axioms or universal principles that are the apex of the argument. So the truth or certainty that attaches to those axioms flows downward to the specific instances to be "proved". In the language of formal logic, the axioms are major premises, the facts that specify the present instance are minor premises, and the conclusion to be "proved" is deduced (follows necessarily) from the initial presises. Practical arguments, by contrast, involve a wider range of factors than formal deductions and are read with an eye to their occasion of use. Instead of aiming at strict entailments, they draw on the outcomes of previous experience, carrying over the procedures used to resolve earlier problems and reapply them in new problmatic situations. Practical arguments depend for their power on how closely the present circumstances resemble those of the earlier precedent cases for which this particular type of argument was originally devised. So. in practical arguments, the truths and certitudes established in the precedent cases pass sideways, so as to provide "resolutions" of later problems. In the language of rational analysis, the facts of the present case define the gounds on which any resolution must be based; the general considerations that carried wight in similar situations provide warrants that help settle future cases. So the resolution of any problem holds good presumptively; its strengh depends on the similarities between the present case and the prededents; and its soundness can be challenged (or rebutted) in situations that are recognized ans exceptional. Jonsen & Toulmin (1988), and Jonsen (1991) introduce New Casuistry as a practical method. The oxford English Dictionary defines casuistry quite accurately as "that part of ethics which resolves cases of conscience, applying the general rules of religion and morality to particular instances in which circumstances alter cases or in which there appears to be a conflict of duties." They modified the casuistry of the medieval ages to use in clinical situations which is characterized by "the typology of cases and the analogy as an inference method". A case is the unit of analysis. The structure of case was made with interaction of situation and moral rules. The situation is what surrounds or stands around. The moral rule is the essence of case. The analogy can be objective because "the grounds, the warrants, the theoretical backing, the modal qualifiers" are identified in the cases. The specified principlism was the method that Degrazia (1992) integrated the principlism and the specification introduced by Richardson (1990). In this method, the principle is specified by adding information about limitations of the scope and restricting the range of the principle. This should be substantive qualifications. The integrated method is an combination of the New Casuistry and the specified principlism. For example, the study was "Ethical problems experienced by nurses in the care of terminally ill patients"(Um, 1994). A semi-structured in-depth interview was conducted for fifteen nurses who mainly took care of terminally ill patients. The first stage, twenty one cases were identified as relevant to the topic, and then were classified to four types of problems. For instance, one of these types was the patient's refusal of care. The second stage, the ethical problems in the case were defined, and then the case was analyzed. This was to analyze the reasons, the ethical values, and the related ethical principles in the cases. Then the interpretation was synthetically done by integration of the result of analysis and the situation. The third stage was the ordering phase of the cases, which was done according to the result of the interpretation and the common principles in the cases. The first two stages describe the methodology of new casuistry, and the final stage was for the methodology of the specified principlism. The common principles were the principle of autonomy and the principle of caring. The principle of autonomy was specified; when competent patients refused care, nurse should discontinue the care to respect for the patients' decision. The principle of caring was also specified; when the competent patients refused care, nurses should continue to provide the care in spite of the patients' refusal to preserve their life. These specification may lead the opposite behavior, which emphasizes the importance of nurse's will and intentions to make their decision in the clinical situations.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.383-393
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• 2020

The main objective of this study is to provide a robust model for estimating parameters of the Clark unit hydrograph (UH) using the observed rainfall-runoff data in the Soyangang dam basin. In general, HEC-1 and HEC-HMS models, developed by the Hydrologic Engineering Center, have been widely used to optimize the parameters in Korea. However, these models are heavily reliant on the objective function and sample size during the optimization process. Moreover, the optimization process is carried out on the basis of single rainfall-runoff data, and the process is repeated for other events. Their averaged values over different parameter sets are usually used for practical purposes, leading to difficulties in the accurate simulation of discharge. In this sense, this paper proposed a hierarchical Bayesian model for estimating parameters of the Clark UH model. The proposed model clearly showed better performance in terms of Bayesian inference criterion (BIC). Furthermore, the result of this study reveals that the proposed model can also be applied to different hydrologic fields such as dam design and design flood estimation, including parameter estimation for the probable maximum flood (PMF).

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.429-440
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• 2001

To achieve a computer tutor framework with high learning effects as well as practicality, the goal of this research has been set to developing an intelligent tutor for problem-solving in mathematics domain. The maine feature of the CyberTutor, a computer tutor developed in this research, is the facilitation of a learning environment interacting in accordance with the learners differing inferential capabilities and needs. The pedagogical information, the driving force of such an interactive learning, comprises of tutoring strategies used commonly in various domains such as phvsics and mathematics, in which the main contents of learning is the comprehension and the application of principles. These tutoring strategies are those of testing learners hypotheses test, providing hints, and generating explanations. We illustrate the feasibility and the behavior of our propose framework with a sample problem-solving learning in geometry. The proposed tutorial framework is an advancement from previous works in several aspects. Firstly, it is more practical since it supports handing of a wide range of problem types, including not only proof types but also finding-unkown tpes. Secondly, it is aimed at facilitating a personal tutor environment by adapting to learners of varying capabilities. Finally, learning effects are maximized by its tutorial dialogues which are derived from real-time problem-solving inference instead of from built-in procedures.