• Journal of The Korean Association For Science Education
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• v.28 no.4
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• pp.340-349
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• 2008

This study investigated the mapping errors induced in learning the concept of reaction rate with analogies, and compared these mapping errors by the analogy presentation types. Tenth graders (N=418) at a high school were assigned to the four groups by the target concepts and the analogy presentation types. The target concepts were 'concentration and reaction rate' and 'temperature and reaction rate'. In presenting analogy, the verbal and the verbal/pictorial analogs were used. After the students learned one of the analogs, a mapping test was administered. From the analysis, eight types of mapping errors were identified: overmapping, artificial mapping, failure to map, rash mapping, mismapping, mapping of a superficial feature, retention of a base feature, and impossible mapping. According to the analogy presentation types and the features of the target concepts, there were some differences in the frequencies of mapping errors. Educational implications of these findings are discussed.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of architectural history
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• v.7 no.3 s.16
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• pp.137-149
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• 1998

The purpose of this study is to learn from a lesson of the historical fact, the Team 10's break away from the CIAM, which is selected as the most important event in the whole 20th century architecture by author as a historian. The CIAM, organized in 1928 by leading European architects in order to propose new architecture in the industrial era, expanded to the world, met almost annually with an idea of economic efficiency, new functional order, and industrial production for thirty years. Young architects had conflicted with old established group from 6th congress, and after 10th congress they met independently in 1959; the CIAM was disappeared and the Team 10 was born. Main issue of the break-away was human aspect. The Team 10 started from real man, concept of 'human contact', 'sense of community', and 'belonging' instead of abstract functional order. Although CIAM did not suggest inhumane architecture, their biological criteria with sunlight, air, sufficient site became physical determinism. Critique against the Team 10, unsuccess for making humane architecture leads to underestimation like a generational hegemony struggle. However, architect is not specialist of life but form. Historical reevaluation for Team 10 should be that they are the first group to raise an human issue in architecture. Success or not to solve the problem belongs to another domain. After 1960, modern architecture was attacked from the common people, not clients but 'users'. Academic circle tried to solve the problem with behavioral approach through a clear process, 'design method' and with phenomenological approach on real human experience. However practice became reactionary tendency, to make form a little complex, they became post-modern and deconstruction form. Failure of the Team 10's form proved that a complex form does not necessarily make a good life of people. In the Korean historic situation of colony ruling, confusion of liberation, and the War, we did not know the existence of both CIAM and Team 10. After 1970s' economic development, we have just copied Western form from Modern via Post-Modern to Deconstruction. If we make architecture people mattered, we should start from the basic, learning from the Team's break-away, instead of copying.

• 대한공업교육학회지
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• v.42 no.1
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• pp.87-105
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• 2017

The purpose of this study was to verify that students' interest in team activities for problem solving, the effect of interest on it, and students' changes in perceptions and their behavioral characteristics in the process of problem solving. The study reviewed documents prepared by students, such as work sheets, descriptive questionnaires, works and their photos, student activity photos and observation journals of teachers. The results of this research are as below. First, a problem solving team activity for making a model car was considered an interesting assignment by more than 90% of male/female students. The fact that female students could be more focused on this assignment than male students was discovered. Interest in the assignment not only had an influence on the points from the start (the blueprint) to the end (model cars completed based upon the designs) of problem solving, but also provided the traction power behind the assignment. Second, the problem solving team activity allowed the students to change their existing recognition (thoughts) while positively taking a lead or indirectly utilizing various learning experiences (including experiences of failure). Third, $2^{nd}$ graders in middle school had a tendency to solve problems in dependently rather than to receive help from others when they encountered problematic situations.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.591-600
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• 1993

Considerable controversy exists about the trend of animal traction effects on crop production in dryland farming systems in sub-Saharan Africa (SSA). This problem arises on account of the failure of the few available empirical studies to recognise the important of technological experience of the individual adopting farmers. This study hence addresses this issue by examining the effects of experience in animal traction technology (ATT) on farm size, cropping emphasis, total crop output and farm productivity. It is based on farm management survey data on 42 small holder farm households fro Ghana. Thirty of these households used animal traction technology (ATT) fro crop cultivation and the rest, mainly hand-hoe. The animal traction sub-sample is classified into three groups according to farmers' years of experience with the technology , thus , those with 1-2, 3-10, and more than 10. Evidence from the study shows that the progression of years of experience with ATT leads to inten ification of labour and land use systems, enhancement of degree of motivation to enter into the market economy, increases in total crop output and farm productivity resulting for decreases in cultivated acreages. The implication of the findings is that institutioal and technical support that do accompany the introduction of such technologies should be structured to last for a relatively longer period to accomodate the learning process.

• Journal of Information Technology Services
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• v.19 no.5
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• pp.83-91
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• 2020

Language Models such as BERT has been an important factor of deep learning-based natural language processing. Pre-training the transformer-based language models would be computationally expensive since they are consist of deep and broad architecture and layers using an attention mechanism and also require huge amount of data to train. Hence, it became mandatory to do fine-tuning large pre-trained language models which are trained by Google or some companies can afford the resources and cost. There are various techniques for fine tuning the language models and this paper examines three techniques, which are data augmentation, tuning the hyper paramters and partly re-constructing the neural networks. For data augmentation, we use no-answer augmentation and back-translation method. Also, some useful combinations of hyper parameters are observed by conducting a number of experiments. Finally, we have GRU, LSTM networks to boost our model performance with adding those networks to BERT pre-trained model. We do fine-tuning the pre-trained korean-based language model through the methods mentioned above and push the F1 score from baseline up to 89.66. Moreover, some failure attempts give us important lessons and tell us the further direction in a good way.

• Journal of the Korean School Mathematics Society
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• v.3 no.2
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• pp.99-109
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• 2000

There has been a considerable number of studies on aggressive behaviors and violences, but it is hard to find one that gives us clear and satisfactory answers with transparent conclusions. Juvenile aggressiveness seems to be caused by unsatisfied desire in the problematic situations and emotional instability. The aim of this study is to find out how emotional instability - especially mathematics anxiety - is correlated with aggressiveness and how aggressiveness affects mathematics proficiency, and thus to help students improve their academic proficiency by finding out appropriate measures to take in case of aggressive behaviors of students. Main tasks of this study are as follows : 1. To find out any correlation between aggressiveness of the experimental students and their mathematics proficiency. 2. Is there any correlation between aggressiveness and mathematics anxiety\ulcorner 3. Is there any correlation between aggressiveness and academic proficiency\ulcorner The conclusion of this study is as follows : 1. There are some negative correlations between the degree of mathematics anxiety and mathematics proficiency. This is mainly because negative emotional state developed from one's uneasiness for fear of failure disturbs one's learning process and thus weakens the will for achieving the task. 2. While aggressiveness doesn't show any significant correlations with academic proficiency, it does have some with mathematics anxiety.