• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Composites Research
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• v.36 no.6
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• pp.395-401
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• 2023

In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.2
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• pp.65-88
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• 2014

This study analyzed cases of collaborative classes between a teacher librarian and Chinese language teacher by applying problem-based learning (PBL) and intended to propose a desirable direction for class in operating collaborative classes of PBL. In order to achieve this purpose, methods to raise problems by BPL at the library of S High School, class content by each round of class, and cases of students' achievements were presented. In addition, statistical analysis of interest in subjects on 101 students in their sophomore year who had participated in PBL class was conducted. According to the study result, students' learning-related desire to accomplish, executive ability of learning, and interest were significantly improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.191-199
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• 2015

The purposes of this study are to examine the predictability of variables related to SNS intention to use in collaborative learning and provide some new implications. Based on Technology Readiness and Acceptance Model (TRAM), we hypothesized that optimism, innovativeness, discomfort, insecurity as personal disposition variables, subjective norm as a social variable, and perceived usefulness and perceived ease of use as cognitive variables would predict SNS intention to use. For this study, 274 'Share Leadership' students in E university completed surveys and it was analyzed by multiple regression analysis. The results of this study showed as follows. First, optimism, innovativeness, discomfort, and subjective norm predicted perceived ease of use. Second, optimism, insecurity, subjective norm and perceived ease of use predicted perceived usefulness. Third, subjective norm, perceived ease of use and perceived usefulness predicted SNS intention to use. From this, it is revealed that positive technology readiness predict much more than negative technology readiness do and the role of teacher and peers is very important.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.14-22
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• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.

• Journal of Science Education
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• v.35 no.1
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• pp.59-67
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• 2011

The purpose of this study was to analyze on the relations of motivation system about science learning and science achievement. TIMSS 2007 was selected and translated for science achievement test. After that, fourth-grade 496 students and eighth-grade 425 students were required to accomplish the questionnaire on behavioral inhibition/activation system about science learning(SL-BIS/BAS) and science achievement. There were negative correlation with SL-BIS and science achievement, and positive correlation with SL-BAS and science achievement. In addition, two systems account for 12% of science achievement. These results would be helpful for teachers to understand the difference about motivation by students' variables and to make a plan for the appropriate strategies for learners.

• Journal of Gifted/Talented Education
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• v.20 no.3
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• pp.967-984
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• 2010

This study purposed to examine the effects of the Feuerstein's cognitive mediated learning for gifted children on attention control and attention shift. For the study, 40 gifted students were chosen from the 3rd graders in elementary schools and divided into test and control groups using K-WISC-III and Torrance Tests of Creative Thinking. The mediated learning program that is targeted to improve the executive functions of gifted children has used the tools of Organization of Dots, Analytic Perception and Comparisons among Feuerstein's Instrumental Enrichment(FIE). According to the results of this study, a significant improvement has been observed in selective attention, self-control, sustained attention, and attention shift through cognitive mediated learning. Therefore, it has been proven that the cognitive mediated learning is effective in reducing gifted children's problematic behaviors that are caused by a lack of attention control and attention shift and improving their cognitive functions and potentials.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.4
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• pp.143-150
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• 2013

The School Curriculum amend in 2007 mandates the contents from which students can learn the principles and concepts of computer science. Computer Science is one of the most rapidly changing subjects, and the Informatics textbook should accurately explain the basic principles and concepts based on the latest technology. However, we found that the middle school textbooks in circulation lack accuracy and consistency in describing CPU. This paper attempted to discover the root-cause of the fallacy and suggest timely and appropriate explanation based on the historical and technical analysis. According to our study, it is appropriate to state that CPU is composed of datapath and control unit. The Datapath performs operations on data and holds data temporarily, and it is composed of the hardware components such as memory, register, ALU and adder. The Control unit decides the operation types of datapath elements, main memory and I/O devices. Nevertheless, considering the technological literacy of middle school students, we suggest the terms, 'arithmetic part' and 'control part' instead of datapath and control unit.