• Journal of the Korean School Mathematics Society
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• v.2 no.1
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• pp.15-30
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• 1999

For the purpose of turning learners' locus of control into internal-controllable variables, counseling materials were developed, and attribution counseling was given. The counseling effects were practically confirmed by way of teaching and evaluation in the actual classes, and furthermore the efforts to provide learners with successful experiences in learning were repeatedly made. As a result, the conclusions are as follows: 1. The procedure of Individual counseling for learning attribution based on individual standard grades and data of the variable order of merit apparently shows learners that if learners are to try their best in learning, they will surely go far in terms of learning in the near future. 2. The procedure of Individual counseling for teaming attribution based on achievement distribution in individual behavior-oriented fields suggests to learners that how to learn is as important as how much effort they make. Surely enough, learners are required to make more effective and efficient efforts, considering their own learning abilities. 3. With the above 1, 2 procedures involved, learners have attributed locus of causality in achievement to their internal-controllable causes. 4. With preparatory assignments according to learner's abilities provided, even slower learners came to be assured that their constant efforts could give rise to success in learning achievement. 5. Above all, it was confirmed that the learners' struggling attitude might well have a significant correlation with achievement success. The learners who are willing to attribute locus of causality in achievement to their internal-controllable causes or strenuous efforts and intrinsic motivation tend to be convinced that they can address themselves to whatever faces them, so they can set up specific learning goals fit for their abilities. Accordingly, they will bit by bit acquire successful experiences (often called 'Aha' experiences) and in turn, feeling the senses of self-efficacy and self-esteem enough to push their efforts even further, they can grow to form a positive self-concept. With one successful experience after another fed back into learners, they are gradually motivated to bring the oncoming achievement expectation to a higher level. To conclude, it is necessary that instruction leading to internal-controllable attribution should be provided, inducing learners to recognize success and failure in learning achievement as a result of their strenuous efforts.

• Journal of Dental Anesthesia and Pain Medicine
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• v.17 no.4
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• pp.297-305
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• 2017

Background: Fiberoptic nasotracheal intubation (FNI) is performed if it is difficult to open the mouth or if intubation using laryngoscope is expected to be difficult. However, training is necessary because intubation performed by inexperienced operators leads to complications. Methods: Every resident performed intubation in 40 patients. Success of FNI was evaluated as the time of FNI. First intubation time was restricted to 2 min 30 s. If the second attempt was unsuccessful, it was considered a failed case, and a specialist performed nasotracheal intubation. If the general method of intubation was expected to be difficult, awake intubation was performed. The degree of nasal bleeding during intubation was also evaluated. Results: The mean age of the operators (11 men, 7 women) was 27.8 years. FNI was performed in a total of 716 patients. The success rate was 88.3% for the first attempt and 94.6% for the second attempt. The failure rate of intubation in anesthetized patients was 4.9%, and 13.6% in awake patients. When intubation was performed in anesthetized patients, the failure rate from the first to fifth trial was 9.6%, which decreased to 0.7% when the number of trials increased to > 30 times. In terms of awake intubation, there was no failed attempt when the resident had performed the FNI > 30 times. The number of FNIs performed and nasal bleeding were important factors influencing the failure rate. Conclusion: The success rate of FNI increased as the number of FNI performed by residents increased despite the nasal bleeding.

• Communications of Mathematical Education
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• v.20 no.2 s.26
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• pp.215-230
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• 2006

In this paper, we performed research about the successful and unsuccessful factors for college math education. In addition, on the basis of the results from the comparative study on the Korean/foreign students' attitude toward the role of computers in math study, we tried to find out the ways to reflect the results on college math education. As the ways to improve college math education we propose that the professors should emphasize the significance of math and explain the successful and unsuccessful factors for math learning during the initial period of each semester. Furthermore, the professors should recognize the importance of computers in math study and ask for the university authority's support to provide necessary softwares and establish computer labs.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.254-260
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• 2022

The purpose of this study was to identify the obstacles facing gifted students with learning disabilities (GSLDs) from the point of view of their teachers in the Makkah region and to find suggested solutions to overcome these obstacles. The study covered Makkah, Jeddah and Taif and used semi-structured interviews which included open-ended questions. The study findings indicated that there were several educational obstacles including the absence of adapted courses or specialized teachers for GSLDs category and the insufficient time for the students to express their talents. According to the findings, there were also societal obstacles including the society's failure to expect the presence of talents along with disabilities, or its denial or rejection of their talents in addition to ridiculing them. The findings also confirmed the existence of administrative obstacles including the lack of community partnership. There were also family obstacles such as the family's lack of encouragement for the students, and ignorance of the nature of GSLDs. The study came up with a number of solutions and proposals related to awareness, educational institutions, education and competitions for talented people with learning disabilities.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.79-91
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• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.57-68
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• 2007

Benchmarking is more than just a comparison of measures about different company's performance in a wider sense. It is a methodology of learning-comparing-learning, at least within small and medium sized enterprises. This learning is not just limited to learn by copying successful concepts from other enterprises or competitors. It starts in learning more about the own company, about its structure and processes causing its own success or its failure. This kind of learning is necessary before the enterprise starts watching for a suitable Benchmarking partner. Learning from each other's strengths and weaknesses is the main goal of the European research project Quality beyond Borders! By using the Benchmarking methodology, small and medium sized enterprises get the opportunity to take part in a Benchmarking study and can learn more about the different strengths and weaknesses of other enterprises on both sides of the border. The results of such a Benchmarking can help to identify potentials for future cooperations among German and Polish enterprises in the same market or business. These potentials can lie in different ways of realising the same success or top-position. The Benchmarking study is not focused on an special business or region. That helps to find out trends for different kinds of top-positions, which can be claimed in all markets within a country. Every trend is characterised by different success factors which are responsible for the success in this top-position. In a first overview, the results of the Benchmarking study show 5 different groups of top-positions within a market which all have different profiles regarding to the importance of their success factors. By the end of the Benchmarking study it will be possible, to give answer about the special reasons for different kind of successes of these groups. These answers can be related to a special region within a country, a special business or of course related to possible differences in the expression of the group success factors in comparison of both countries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.30-39
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• 2024

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.