• Journal of Distribution Science
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• v.18 no.2
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• pp.49-58
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• 2020

Purpose: This study focuses on the performance of the strategy of Indonesia's companies in facing the development of e-commerce business. The relationship between Knowledge Management (Organizational Memory, Knowledge Sharing, Knowledge Absorption, Knowledge Acceptance), Organizational Innovativeness, Competitive Advantage (Time, Quality, Cost, Flexibility) and E-Commerce (Humanistic Factors: Management, Competence, Organizational Structures) examined in this case study. Research design, data, and methodology: This study uses two types such us qualitative and quantitative. A survey approach were conducted to collect data from the Group of Companies (Director and Manager), Academician (Lecturer), Regulator (Head of Government Institution Division), and Master of Management (at least five years). Total of 114 samples was collected and processed for statistical analysis using Smart PLS. Results: This study provide the findings proved that Knowledge Management and Organization Innovativeness simultaneously have positive influence on Competitive Advantage, while Knowledge Management, Organization Innovativeness, and Competitive Advantage simultaneously have positive influence on E-commerce where Competitive Advantage positively influence to E-commerce. Conclusions: The implementation of strategies or steps in this study are expected to steer and motivate an organization to successfully implement a good knowledge management system to pass on knowledge from generation to generation in the company Organizational Innovativeness strategies to improve e-commerce performance.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.88-98
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• 2006

The purpose of this study was to analyze the process of scientists' science knowledge generation by employing four creative scientists as participants. Raw protocols were collected by an intensive interview method and then analyzed by a psychological modelling procedure. The present study showed that the process of knowledge generation divided into the processes of inductive, abductive, and deductive thinking. Furthermore, the inductive process in simple and operative observation was involved in the processes of generating a question, conjecture/prediction, designing an operational method, operation, and simple observation. Also, the abductive process had two components; question generation, and hypothesis generation which consisted of analyzing questions, searching explicans, and constructing hypothesis. Finally, the deductive process involved inventing abstract test methods, inventing abstract criteria, inventing concrete test methods, inventing concrete criteria, collecting results, and evaluating hypotheses and stating conclusions.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.333-343
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• 2022

Humans are facing many environmental challenges. Climate change, water pollution, global warming, and hazardous waste disposal are all issues that many countries throughout the world are dealing with. People's psychology and consumer behavior are significantly affected by these challenges, particularly generation Z, which is immediately affected by environmental changes. Young people have a strong sense of curiosity and have access to readily updated knowledge. Today's youth, in particular, live a civilized and responsible lifestyle. As a result, people recognize the significance of their own consumption behavior in affecting environmental change and are increasingly replacing them with green, ecologically friendly products as a fantastic method to mitigate their harmful consequences. In this research, there are four factors related to the young generation and environmental awareness that affect green consumption intention: perceived environmental responsibility, green knowledge, green attitude, and green product value. The goal of this study is to look into how detrimental environmental changes affect Generation Z's green consumption habits. This study used primary data from over 1000 people in the age group, which was processed using the AMOS 20 software. All the characteristics described above had an impact on Generation Z's green consumption intentions, according to the findings.

• Journal of The Korean Association For Science Education
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• v.23 no.5
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• pp.458-469
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• 2003

Hypothesis is defined as a proposition intended as a possible explanation for an observed phenomenon. The purpose of this study was to generate a grounded theory on the process of undergraduate students' generating hypothesis-knowledge about scientific episodes. Three hypothesis-generating tasks were administered to four college students majored in science education. The present study showed that college students represented five types of intermediate knowledge in the process of hypothesis generation, such as question situation, hypothetical explicans, experienced situation, causal explicans, and final hypothetical knowledge. Furthermore, students used six types of thinking methods, such as searching knowledges, comparing a question situation and an experienced situation, borrowing explicans, combining explicans, selecting an explican, and confirming explicans. In addition, hypothesis-generating process involves inductive and deductive reasoning as well as abductive reasoning. This study also discusses the implications of these findings for teaching and evaluating in science education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• Journal of The Korean Association For Science Education
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• v.23 no.3
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• pp.215-228
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• 2003

The present study is to analyze the arguments about the generation of declarative scientific-knowledge in the philosophy of science and invent a structured model of the process of scientific-knowledge generation with the types of the generated scientific-knowledge. The invented model shows that scientific-knowledge generation is a distinctive process with the processes of inductive, abductive, and deductive thinking. Furthermore, inductive process is included with observation, which is consisted of simple observation and operative observation, and rule-discovery which is involved with the processes of commonness discovery, classification, pattern discovery, and hierarchical relationship. Also, abductive process has two components. One component generates question and second component generates hypothesis in which the process consists of representing question situation, identifying experienced situation, identifying causal explicans, and generating hypothetical explicans. Finally, deductive process is involved with logical inventing test method and evaluation criteria, concrete inventing test method and evaluation criteria, evaluating hypothesis, and making conclusion.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.79-111
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• 1991

This paper suggests a system which analyzes facial images through hypotheses generation and verification.A model knowledge on faical structures is to be represented in a hieratchical frame system and the process of hypothesis generation and verification is to be embodied through linking upper lower-level frames.This paper especially addresses the problem of how to selec an approproate knowledge at each stage of hypotheses generation and verification.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• Journal of The Korean Association For Science Education
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• v.24 no.6
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• pp.1106-1117
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• 2004

The purposes of this study were to analyze types of scientific emotion word and to investigate the relationship between the ISE(Index of Scientific Emotion) and the ability of science-knowledge generation in subjects' scientific observation and rule-discovery. The subjects were asked to perform four scientific tasks. The tasks were developed that are suitable for scientific observation and rule-discovery. In performing tasks, the subjects were asked to describe their generated science-knowledge and scientific emotion through self-report questionnaire, performing each task. The strength of their scientific emotion was also measured using adjective emoticon check lists. In subjects' scientific observing, they showed 33.3% of interest emotion which was the biggest, 15.0% of acceptance emotion, and 11.3% of love emotion, respectively. In scientific rule-discovering, types of emotion were shown as 23.8% of interest, 21.5% of disgust, and 10.8% of acceptance, respectively. In addition, ability of science-knowledge generation was significantly correlated to ISE.

• Journal of Korean Library and Information Science Society
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• v.16
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• pp.129-157
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• 1989

Through this study, some considerations to be taken into account in order to construct the user model for the user friendly system which can provide each individuals user armed with varied intellectual level with the relevant information, can be summarized as follows : (1) The user' ability to use the system and users' subject knowledge, the distribution of the users' level knowledge should be considered for the decision of the typed of interaction between the users and the system. (2) the knowledge of the user models should include the following kinds of knowledge inharmony with one another, 1. Standard user knowledge which represents a general characteristic of user group, 2. individual user knowledge which represents an individual's unique characteristic, 3. Long-term user knowledge which represents the education level and subject background of users, 4. short-term user knowledge which represents the purpose of information science and information need by users (3) As knowledge generation technique, both the implicit method and explicit method should be a n.0, pplied, observation of the system during the interaction, and explicit method generates the knowledge by the user's answering the questions already made by the system. (4) The frame technique as the knowledge representation for the user-modelling in which user-knowledge is represented in a limited situation and in a qualitative aspects, can be recommended. The frame is adequated for the explanation of structured situation, and for the processing the present situation by inferring the previous experiences.