• Journal of The Korean Association For Science Education
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• v.25 no.5
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• pp.610-623
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• 2005

This was a theoretical study of which the goal was to provide a foundation for developing and implementing earth science inquiry activities based on abduction as a scientific inquiry method. Through a review of relevant literature, the study examined the nature of earth science in terms of the goals of earth science inquiry and the characteristics of what is investigated in earth science. It also explored the forms and meanings of abduction, thinking strategies used in the abductive inference, and the abductive inquiry model. Abduction is the process of inferring certain rules (e.g., scientific facts, principles, laws) and providing explanatory statements or hypotheses in order to explain some phenomena. This method was found to be well-suited to the earth science inquiry which studies the causes and processes of natural phenomena in the earth and space environment. Abduction has the nature of ampliative, selective, evaluative, and creative inference, and several thinking strategies, including reconstruction of data, heuristic generalization, analogy, existential, conceptual combination, and elimination strategies, are employed for inferring rules and suggesting hypotheses. This study found the abductive inquiry model to be adaptable to earth science classrooms, and it is therefore suggested that earth science instructions should be based on the abductive method and that research work concerning the abductive inquiry in the classroom should follow.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.109-116
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• 1996

In this paper, I would like to explore the possibility that the nature of place assimilation can be captured in terms of the OCP within the Optimality Theory (Mccarthy & Prince 1999. 1995; Prince & Smolensky 1993). In derivational models, each assimilatory process would be expressed through a different autosegmental rule. However, what any such model misses is a clear generalization that all of those processes have the effect of avoiding a configuration in which two consonantal place nodes are adjacent across a syllable boundary, as illustrated in (1):(equation omitted) In a derivational model, it is a coincidence that across languages there are changes that have the result of modifying a structure of the form (1a) into the other structure that does not have adjacent consonantal place nodes (1b). OT allows us to express this effect through a constraint given in (2) that forbids adjacent place nodes: (2) OCP(PL): Adjacent place nodes are prohibited. At this point, then, a question arises as to how consonantal and vocalic place nodes are formally distinguished in the output for the purpose of applying the OCP(PL). Besides, the OCP(PL) would affect equally complex onsets and codas as well as coda-onset clusters in languages that have them such as English. To remedy this problem, following Mccarthy (1994), I assume that the canonical markedness constraint is a prohibition defined over no more than two segments, $\alpha$ and $\beta$: that is, $^{*}\{{\alpha, {\;}{\beta{\}$ with appropriate conditions imposed on $\alpha$ and $\beta$. I propose the OCP(PL) again in the following format (3) OCP(PL) (table omitted) $\alpha$ and $\beta$ are the target and the trigger of place assimilation, respectively. The '*' is a reminder that, in this format, constraints specify negative targets or prohibited configurations. Any structure matching the specifications is in violation of this constraint. Now, in correspondence terms, the meaning of the OCP(PL) is this: the constraint is violated if a consonantal place $\alpha$ is immediately followed by a consonantal place $\bebt$ in surface. One advantage of this format is that the OCP(PL) would also be invoked in dealing with place assimilation within complex coda (e.g., sink [si(equation omitted)k]): we can make the constraint scan the consonantal clusters only, excluding any intervening vowels. Finally, the onset clusters typically do not undergo place assimilation. I propose that the onsets be protected by certain constraint which ensures that the coda, not the onset loses the place feature.

• Culinary science and hospitality research
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• v.19 no.2
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• pp.28-39
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• 2013

The purpose of this study is to examine the causality among customer orientation, job satisfaction, and organizational commitment of food service employees using a non-recursive model. To do this, literary and empirical research was carried out. In the literary research, this study examined previous studies related to the concepts such as customer orientation, job satisfaction, and organizational commitment. The empirical research was analyzed based on the questionnaire answered by 203 food service employees. Main results of this study are indicated below. First, customer orientation had a positive effect on job satisfaction(t=2.404, p=0.016). Second, job satisfaction had a positive effect on organizational commitment(t=8.555, p=0.000). Third, organizational commitment had a positive effect on customer orientation(t=6.071, p=0.000). Our main theoretical and practical implication includes the significance of extending the range of research by verifying the non-recursive causality among customer orientation, job satisfaction, and organizational commitment. The limitations of this study are possible problems with generalization by convenience sampling in limited areas.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.773-782
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• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.

• Journal of the Korean Chemical Society
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• v.55 no.2
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• pp.290-303
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• 2011

The purpose of this study was to introduce the practical model on the enhancement of the divergent and convergent thinking skills through inquiry instruction in science class. In this study, the creative thinking skill has been defined by divergent thinking skill as the narrow sense. In the science field, the problem solving thinking skill is just same as the inquiry thinking skill. Also, the problem solving thinking skill has been defined by convergent thinking skill as the critical thinking skill. This new instruction had been used for the college student in the class of general chemistry laboratory for the one semester. The first results had been founded that the students' divergent thinking skill had been increased significantly. Especially, the skills of recognition of problems, the skills of making hypothesis, and the skills of transformation and interpretation of data had been increased significantly. The second results had been founded that the students' convergent thinking skill had been increased significantly. Especially, the skills of making hypothesis, the skills of transformation and interpretation of data, and the skills of making conclusion and generalization had been increased significantly. The third results had been founded that the students' the creative and problem solving thinking skill had been increased significantly. Especially, the rest of all skills exception the skill of control variables had been increased significantly.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.183-191
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• 2021

The advanced development of the world's economies requires a detailed study of the impact of factors on the level of digitalization, to ensure economic growth and promote the use of information and communication technologies in the digital economy. Digitalization of the world's economies is ensured through the implementation of relevant regulations and policy decisions to implement public policy and strategy of the digital economy. The purpose of the study is to establish the pattern of the impact of factors on the level of digitalization of world economies by conducting a regression analysis to reflect the dependence of the impact of factors on the level of digitalization in 25 economies (by IMD digital competitiveness), to check the level of digitalization of the world's economies. It is necessary to analyze the ranking of countries in the world according to the DiGiX Index, IMD, and DESI Digital Competitiveness Rating. Research methods: information synthesis method; regression analysis; systematization, and generalization. Results. It was found that because of regression analysis, the value of the coefficient of determination indicates that the regression model by 78% explains the relationship between future readiness of countries to implement digital technologies and information and communication technologies, but there are still a small number of other factors not included in the regression model. It is determined that the greatest progress among EU member states for the period 2015-2020 according to the DESI index belongs to Ireland, the Netherlands, Malta, and Spain. It is established that Estonia, Spain, and Denmark are in the lead in the DESI rating, in terms of e-government implementation. The study found that the impact of factors on the level of digitalization of world economies contributes to solving current economic problems through further implementation of information and communication technologies and improving legislation in the digital economy, which will ensure the implementation of effective digital policy. It is established that ensuring the appropriate level of digitalization of the world's economies should solve the problems in the digital economy sector faced by governments and businesses, which requires the implementation of measures to regulate and ensure the continued operation of the digital economy.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.408-416
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• 2022

The new millennium is characterized by an unprecedented breakthrough in knowledge and information and communication technologies, and the challenges of the XXI century require modernized paradigms of interaction in all spheres of life. Education continues to play a key role in national and global growth. The key role of education and its leadership in developing creative potential, as the main paradigm of the countries' stability, have significantly influenced educational centers. The developers of educational programs use information technologies as an incentive to develop creative potential of educational process. Professional training of the educational candidate is enhanced by the use of information technologies, so the educational applicants should develop technological skills to be productive members of society. Using the latest achievements in the field of information technologies for the organization of the educational process helps to form the operational style of education applicants' thinking, which provides the ability to acquire skills of processing information, that is presented in the text, graphic, tabular form, and increase the level of general and informational culture necessary for better orientation in the modern information space. The purpose of the research is to determine the effectiveness of information technologies as an incentive to develop creative potential of educational process on the basis of the survey, to establish advantages and ability to provide high-quality education in the context of using information technologies. Methods of research: comparative analysis; systematization; generalization, survey. Results. Based on the survey conducted among students and teachers, it has been found out that the teachers use the following information technologies for the development of creative potential of the educational process: to provide video and audio communication process (100%), Moodle (95,6%), Duolingo (89,7%), LinguaLeo (89%), Google Forms (88%) and Adobe Captivate Prime (80,6%). It is determined that modular digital learning environments (97,9%), interactive exercises tools (96,3%), ICT for video and audio communication (96%) and interactive exercises tools (95,1%) are most conducive to the development of creative potential of the educational process. As a result of the research, it was revealed that implementation of information technologies for the development of creative potential of educational process in educational institutions is a complex process due to a large number of variables, which should be taken into account both on the educational course and on the individual level. It has been determined that the using the model of implementation information technologies for the development of creative potential in educational process, which is stimulated due to this model, benefits both students and teachers by establishing a reliable bilateral connection between teacher and education applicant.

• Journal of the Korean Regional Science Association
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• v.39 no.3
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• pp.3-12
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• 2023

The issue to be discussed is set as the relationship between urban fragmentation and urban heat phenomena. The fragmentation is recognized as a negative form that commonly occurs in the process of urbanization. The purpose of this study is to examine the relationship between urbanization entropy and heat phenomenon by looking at the five major cities in Korea. The employed methods are InVEST Urban Cooling Model and MSPA (Morphological Spatial Pattern Analysis) by using the meteological data for the July 2018. The major results are as follows; First, a low rank correlation(rho=-0.3) is found in the relation between entropy and Cooling Capacity Index (CCi). Second, a very high level of rank correlation is observed between entropy and Average Temperature(℃)(rho=0.9). The implications are that 1) a city with a large degree of sprawling development can have a negative effect on urban heat phenomena; 2) the composition of land use including dispersion and concentration in non-urbanized areas, which has the characteristics of open space, can affect the urban thermal environment. Due to the limited number of case studies, it is appropriate to understand that a possibility, not generalization, is observed between entropy and heat phenomena in urbanized areas.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.