• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.91-98
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• 2000

Despite their wide application of some traditional project management techniques like the Program Evaluation and Review Technique, they lack of learning, one of important factors in many disciplines today, due to a static view for project progression. This study proposes a framework for estimation by loaming based on a Linear Bayesian approach. As a project Progresses, we sequentially observe the durations of completed activities. By reflecting this newly available information to update the distribution of remaining activity durations and thus project duration, we can implement a decision support system that updates e.g., the expected project completion time as well as the probabilities of completing the project within the due bate and by a certain date. By implementing such customized system, project manager can be aware of changing project status more effectively and better revise resource allocation plans.

• Journal of the Korean School Mathematics Society
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• v.10 no.3
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• pp.369-384
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• 2007

This study examined and compared the level of mathematical anxiety according to the types of students' characteristics based on the former research study showing that there exists a close relationship between characteristics and mathematical anxiety. The subjects of this study are 159 students enrolled in Chungnam Gongju and Kyunggi-do Ahnyang. They were categorized into groups following various standards such as preference index(E-1, S-N, T-F, J-P), ability & disposition, 16 types of characteristics. Then these were tested for types and the level of mathematical anxiety by the factors of mathematical anxiety. The results show that Type E students show the greatest anxiety in learning motivation, and Type N students in the pedagogy of teaching and loaming for the subfactor of mathematical anxiety. Further, Type NT students respond strongly to the pedagogy of teaching and loaming in psychological ability and disposition, which shows that mathematical anxiety and sub-factors of mathematical anxiety are closely somehow related.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Communications of Mathematical Education
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• v.28 no.2
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• pp.235-253
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• 2014

This study aims to develop strategies for improving the affective characteristics of Korean students based on results from international achievement tests. In pursuing the goal, different research methods are employed including a) analysis of the theories and literature regarding the affective domains included in PISA and TIMSS studies; b) analysis of the current situation and needs of Korean students with respect to the affective factors based on PISA and TIMSS results; c) case studies of best practices in relation to students' affective domains in Korea and abroad; and d) development of strategies for improving and supporting Korean students' affective characteristics. Especially, this paper deals with the analysis of the results from in-depth interviews and class observations, so as to identify the current situation and best practice cases of students' affective characteristics education in Korea. The results are classified into a) curriculum, which is in turn divided into national curriculum and reconstruction of curriculum school and classroom; and b) teaching, learning and evaluation, which is in turn divided into learner characteristics, motivation, teaching strategies, class grouping, activities and interaction, question and feedback, evaluation methods, and evaluation tools. Support plans in terms of school and social environments are also suggested based on the results.