• Korean Journal of Community Nutrition
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• v.29 no.3
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• pp.199-211
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• 2024

Objectives: Online grocery shopping has gained traction with the digital transformation of retail. This study constructs a behavioral model combining values, attitudes, and reasons for behavior-specifically, facilitators and resistance-to provide a more novel discussion and further understand the relative influences of the various factors affecting continuance intention in online grocery shopping. Methods: Data were collected through an online questionnaire from consumers who had engaged in online grocery shopping during the past month in Seoul, Korea. All collected data were analyzed using descriptive analysis, and model validation was performed using partial least squares structural equation modeling. Results: Continuance intention is primarily driven by facilitative factors (compatibility, relative advantage, and ubiquity). Attitude can also positively influence continuance intention. Although resistance factors (price, tradition, and risk) do not significantly affect continuance intention, they negatively affect attitude. Values significantly influence consumers' reasoning processes but not their attitude. Conclusions: These findings explain the key influences on consumers' online grocery shopping behavior in Seoul and provide additional discussion and literature on consumer behavior and market management. To expand the online grocery market, consumers should be made aware of the potential benefits of the online channel; the barriers they encounter should be reduced. This will help sustain online grocery shopping behavior. Furthermore, its positive impact on attitude will further strengthen consumers' continuance intention.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1108-1115
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• 2003

This paper Introduces a method to automate process design using Case Based Reasoning technique. A case is represented hierarchically through functional requirement, behavioral model, and process mechanism model Specifically, a case retrieval algorithm to use function and behavior, case adaptation method to use hierarchical case representation, and a machine selection method are presented.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.522-537
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• 2013

This study analyzed the relationship among the construct level of analogical reasoning, prediction and uncertainty, and the construction of an explanatory model that were produced during small group discussions for scientific problem solving. This study was participated in by 8 students of K University divided into 2 teams conducting scientific problem solving. The participants took part in discussions in groups after achieving scientific problem solving individually. Through individual interviews afterwards, changes in their thinking through discussion activities were looked into. The results are as follows: The analogy at the Entities/Attributes level was used to make people clearly understand the characteristics of certain objects or entities in the discussions. The analogy at the Configuration/Motion level that was produced during the discussions ensured other participants to predict the results of problem solving. The analogy at the Mechanism/Causation level changed the structure of problem situations either to help other participants to reconstruct the explanatory model or to come up with a new situation that was never been through before to justify the created mechanism and through this, the case of creating Thought Experiments during the discussions were observed. if looking into the changes of analogies, each individual's analogic paradigm during the discussions were shown as production paradigm, reception-production paradigm, production-reception paradigm, and reception paradigm. The construction and reconstruction of the explanatory model were shown in analogic production paradigm, and in the reception paradigm of an analogy, participants changed their predictions or their certainty.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.82-91
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• 2009

Sign language spotting is the task of detecting and recognizing the signs in a signed utterance. The difficulty of sign language spotting is that the occurrences of signs vary in both motion and shape. Moreover, the signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and non-sign patterns(which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing a threshold model in a conditional random field(CRF) model is proposed. The proposed model performs an adaptive threshold for distinguishing between signs in the vocabulary and non-sign patterns. A hand appearance-based sign verification method, a short-sign detector, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experimental results show that the proposed method can detect signs from continuous data with an 88% spotting rate and can recognize signs from isolated data with a 94% recognition rate, versus 74% and 90% respectively for CRFs without a threshold model, short-sign detector, subsign reasoning, and hand appearance-based sign verification.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Journal of Elementary Mathematics Education in Korea
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• v.18 no.2
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• pp.189-209
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• 2014

This study designed and developed a model of ill-structured problem solving and ill-structured problems for the 4th, 5th, and 6th graders. In addition, two sets of ill-structured problems has been explored to 23 4th graders, 33 5th graders, and 23 6th graders in elementary schools in order to investigate their problem solving, creative personality, and mathematical reasoning. The model of ill-structured problem solving was suggested ABCDE (Analyze-Browse-Create-DecisionMaking-Evaluate) model and analyzed participants' problem solving procedure. As results, participants showed improvement between pretest and posttest in problem solving and the high graders showed the greater creative personality.

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.87-106
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• 2002

The behavioral and dynamic implications of an ERP implementation/installation are, to say the least, not well understood. Getting the switches set to enable the ERP software to go live is becoming straightforward. The really difficult part is understanding all of the dynamic interactions that accrue as a consequence. Dynamic causal and connectionist models are employed to facilitate an understanding of the dynamics and to enable control of the information-enhanced processes to take place. The connectionist model ran be analyzing (behind the scenes) the information accesses and transfers and coming If some conclusions about strong linkages that are getting established and what the behavioral implications of those new linkages and information accesses we. Ultimately, the connectionist model will come to an understanding of the dynamic, behavioral implications of the larger ERP implementation/installation per se. The underlying connectionist model will determine information transfers and workflow. Once a map of these two infrastructures is determined by the model, it becomes a relatively easy job for an analyst to suggest improvements in both. Connectionist models start with analog object structures and then use learning to produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Based on earlier work performed and published by the author[10][11], a Connectionist ReasOning and LEarning System(CROLES) is developed that mimics the real-world reasoning infrastructure. Coupled with an explanation subsystem, this system can provide explanations as to why a particular reasoning structure behaved the way it did. Such a system operates in the backgmund, observing what is happening as every information access, every information response coming from each and every intelligent node (whether natural or artificial) operating within the ERP infrastructure is recorded and encoded. The CROLES is also able to transfer all workflows and map these onto the decision-making nodes of the organization.

• Korean Journal of Cognitive Science
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• v.17 no.1
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• pp.53-74
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• 2006

Similarity has been considered as one of basic concepts of cognitive psychology which is useful for explaining cognitive structure and process. MDS models(Shepard, 1964; Nosofsky, 1991) and Contrast model(Tversky, 1977) were proposed as early models of similarity comparison process. But, there have been a lot of theoretical doubts about the conceptual validity of similarity as a result of empirical findings which could not be explained by early models. Goldstone(1994) assumed that similarity could be defined by alignment processes, and suggested structural alignment as a prospective alternative for solving conceptual controversies so far. In this study, basic assumption and algorithms of MDS models(Shepard, 1944; Nosofsky, 1991) and Contrast model(Tversky, 1977) were described shortly and some theoretical limitations such as arbitrariness of selective attention and correlated structures were discussed as well. The conceptual characteristics and algorithms of SIAM(Goldstone, 1994) were described and how it has been applied to cognitive psychology areas such as categorization, conceptual combination, and analogical reasoning were reviewed. Finally, some theoretical limitations related with data-driven processing and alternative processing and possible directions for structural alignment were discussed.

• The Journal of Korean Association of Computer Education
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• v.16 no.2
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• pp.9-18
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• 2013

In secondary education, learning a computer science subject has the purpose to improve creative problem solving ability of students by learning computational thinking and principles. In particular, learning algorithm has been emphasized for this purpose. There are studies that learning algorithm has the effect of creative problem solving based on the leading studies that learning algorithm has the effect of problem solving. However, relatively the importance of the learning algorithm can weaken, because these studies depend on creative problem solving model or special contents for creativity. So this study proves that learning algorithm has the effect of creative problem solving in the view that common problem solving and creative problem solving have the same process. For this, analogical reasoning was selected among common thinking skills and divide-and-conquer algorithm was selected among abstractive principles for analogical reasoning in sorting algorithm. The frequency which solves the search problem by using the binary search algorithm was higher than the control group learning only sequence of sorting algorithm about the experimental group learning divide-and-conquer algorithm. This result means that learning algorithm including abstractive principle like divide-and-conquer has the effect of creative problem solving by analogical reasoning.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.19-29
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• 2007

This paper introduces a robot knowledge framework which is represented with multiple classes, levels and layers to implement robot intelligence at real environment for mobile robot. Our root knowledge framework consists of four classes of knowledge (KClass), axioms, rules, a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). Four KClasses including perception, model, activity and context class. One type of rules are used in a way of unidirectional reasoning. And, the other types of rules are used in a way of bi-directional reasoning. The robot knowledge framework enable a robot to integrate robot knowledge from levels of its own sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. With the integrated knowledge, a robot can have any queries not only through unidirectional reasoning between two adjacent layers but also through bidirectional reasoning among several layers even with uncertain and partial information. To verify our robot knowledge framework, several experiments are successfully performed for object recognition and navigation.