• Hwankyungkyoyuk
• /
• v.15 no.1
• /
• pp.68-82
• /
• 2002

My responses to Kim Kyung-Ok's Critique on my critique on the Hungerford approach can be summarized as follows; First, it was argued that possible confusions and misunderstandings around the concept of behavior in REB were mainly caused by Hungerford himself who has used the word in several different ways, from a bunch of overt actions to almost all kinds of responses including cognitive skills, without any clear operational definition of it for more than 20 years. It seems to be needed for future users of the word, 'Behavior' to Prevent unnecessary confusions by providing their operational definition of it. Second, REB is too ambiguous to be a legitimate goal of environmental education and too outcome-oriented to be a meaningful measure for environmental education research. Anyone who accept REB as a goal of EE or a measure for research should clearly suggest procedures and criteria for judging the environmental responsibility of actions under consideration. Third, the Hungerford approach has begun by realizing the limit of a linear traditional behavior change system and has been evolving toward a complex model with dynamic interactions among/between cognitive variables and affective variables. However, it still has one-way structural orientation toward 'Behavior' with no feedbacks. Addition of some feedback processes would make the model more flexible and realistic. Finally, both the Hines model and the Hungeford model were established based on a series of behavioristic studies including three doctoral dissertations equiped with a list of actions which were prejudged to be environmentally responsible by the researchers, not by the learners. What they were primarily interested in was not how mind functions during the learning processes but how learners' behavior can be effectively changed. Considering uncertainty and complexity associated with environmental problems, a great deal of efforts ought to be made toward more context-based and less normative studies applying cognitive psychology and quantitative approaches.

• Nano
• /
• v.13 no.11
• /
• pp.1850128.1-1850128.10
• /
• 2018

Glutathione (GSH), the protective agent and reducing agent, has been widely used to prepare gold nanoclusters (GSH-Au NCs) with stable fluorescence properties and negative charge of the surface. Meanwhile, polyethyleneimine (PEI) was used as the modification agent to synthesize magnetic ferroferric oxide nanoparticles ($Fe_3O_4$) with fantastic dispersibility and positive charge of the surface. Based on the electrostatic adsorption force, magnetic nano-$Fe_3O_4@GSH-Au$ NCs core-shell microspheres composed of magnetic $Fe_3O_4$ nanoparticles modified by PEI as the core and GSH-Au NCs as the shell were assembled. The prepared $Fe_3O_4@GSH-Au$ NCs microspheres harbored a uniform size (88.6 nm), high magnetization (29.2 emu/g) and excellent fluorescence. Due to the coordination bond action between Au atom and sulfhydryl (-SH), amino ($-NH_2$), carboxyl (-COOH) in sweat, $Fe_3O_4@GSH-Au$ NCs could combine with latent fingerprints. In addition, $Fe_3O_4@GSH-Au$ NCs with good fluorescence and magnetism could detect fingerprints on various objects. Significantly, the powders were not easy to suspend in the air, which avoided the damage to the health of forensic experts and the fingerprints by only powder contacting. Above all, $Fe_3O_4@GSH-Au$ NCs was successfully applied to the latent fingerprint visualization, which has great potential in forensic science.

• Journal of the Korea Convergence Society
• /
• v.7 no.4
• /
• pp.123-131
• /
• 2016

In this paper, we discussed learner's capability enhancement education suitable for the age of artificial intelligence (AI) using game analysis and archival research based on the 2016 Google Deepmind Challenge match between AI that possessed the finest deep neural networks and the master Baduk player that represented the best of the human minds. AlphaGo was a brilliant move that transcended the conventional wisdom of Baduk and introduced a new paradigm of Baduk. Lee Sedol defeated AlphaGo via the 'divine move and Great idea' that even AlphaGo could not have calculated. This was the triumph of human intuition and insights, which are deeply embedded in human nature as well as human courage and strength. Convergence science education that cultivates student abilities that can help them control machines in the age of AI must be in the direction of developing diverse human insights and positive spirits embedded in human nature not possessed by AI via implementing hearts-on experience and topophilia education obtained from the nature.

• The Journal of Korean Association of Computer Education
• /
• v.8 no.3
• /
• pp.77-89
• /
• 2005

Design of a new information technology curriculum is very difficult work. It has relatively short history of operation. It advances very fast. Furthermore, people have very different understandings between information application and information knowledge. Therefore making decision for information technology contents is very difficult work. So we try to get effectiveness of the curriculum design through the comparison with other countries' cases. In this paper we tried to search and analyze about the historical trends, current states, and characteristics of Chinese information technology curriculum. China has strong focus on information technology education while she makes great success in economic development. We also suggested some good points of Chinese information technology curriculum which can be adapted to the design of a new Korean computer curriculum. The China's information technology curriculum is summarized by four categories. They are (1) educations on the basic knowledges and functions of the stable level of information technology, (2) educations on the information technology applications for the problem solving, (3) educations for the individualized learning through the research and project implementations in depth, and (4) systematic arrangements and integrated operations of the information technology curriculum.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.6
• /
• pp.117-125
• /
• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• Journal of the Economic Geographical Society of Korea
• /
• v.11 no.3
• /
• pp.334-349
• /
• 2008

In this study, the network formation process of personal actors in industries for the elderly was analyzed. This process is applied to the knowledge creation model of the SECI (Nonaka-Takeuchi learning cycle), that is socialization, externalization, combination, internalization. There are some kinds of opportunities to interact in these industries in the forms of field survey teams to overseas, some seminars and symposiums, many kinds of meetings, education and training programs, trade fairs and on-line forums. These palces(ba) - originating ba, interacting ba, cyber ba, exercising ba - played great roles in the formation of personal actor networks. Personal actors had opportunities to interconnect with distant actors through those places(ba). In the spatial perspective, personal actors could make face-to-face contact and build trust through temporary geographical proximity or temporary clusters with the help of personal mobility. Relations in the virtual spaces such as the Internet community did much toward building personal networks.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.353-359
• /
• 2021

Information technology and the need for timely and effective communication during the Covid-19 have made most governments adopt technological approaches to provide their services. E-government services have been adopted by most governments especially in developed countries to quickly and effectively share information. This study discusses the reasons why governments in the Gulf region should develop a new model for information technology knowledge management practices. To achieve this, the author identified possible benefits of adopting information technology knowledge management practices and why most governments in the Gulf find it hard to adopt them. Knowledge management allows for learning, transfer as well as sharing of information between government organizations and citizens and with the development of technology, the effectiveness of electronic services can easily be achieved. Also, effective adoption of information technology can improve knowledge management with the help of techniques that enhance capture, storage, retrieval as well as sharing of information. The author used systematic literature review to select 28 journals and articles published post 2019. IEEE, Google Scholar and Science Direct were used to select potential studies from which 722 journals and articles were selected. Through screening and eligibility assessment, 21 articles were retained while the back and forward search had 7 more articles which were also included in the study. Using information gathered from these articles and journals a new conceptual model was developed to help improve information technology knowledge management for governments in the Gulf region to effectively deliver e-services during Covid-19. This model was developed based on the process of KM, Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model. From UTAUT model, performance expectancy, effort expectancy as well as social influence had a great impact.

• The Journal of the Korea Contents Association
• /
• v.21 no.7
• /
• pp.448-458
• /
• 2021

As the fourth industrial revolution accelerates, universities have made great efforts to develop and reform creative convergence courses for improving the students' creative convergence capabilities. Although various subjects such as "Capstone" and "Design Thinking" to the improvement of students' creative convergence competences, many courses focus on creativity education in the direction of creating new products or outputs such as engineering, design, and art, so there is still a lack of systematic education and subjects on creative convergence capabilities from a humanities and sociological perspective. In order to overcome their limitations of creative courses, "H" University developed a 'Imaginative Innovators' class with the purpose of solving creative problems on social issues related to sciences, culture, politics, economics, and so on. In this study, we introduced the purpose, methodology, students' best practices etc. of the "Imaginative innovator" course. In addition, we discussed the limitations and complements as well as the advantages and possibilities of the course. These findings are expected to contribute to the development and expansion of creativity education.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1212-1223
• /
• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.4
• /
• pp.595-601
• /
• 2018

Recently, as the fine dust level has risen rapidly, there is a great interest. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and has been reported to increase death rate. In addition, there exist damage to fine dusts continues at industrial sites. However, exposure to fine dust is inevitable in modern life. Therefore, predicting and minimizing exposure to fine dust is the most efficient way to reduce health and industrial damages. Existing fine dust prediction model is estimated as good, normal, poor, and very bad, depending on the concentration range of the fine dust rather than the concentration value. In this paper, we study and implement to predict the PM10 level by applying the Artificial neural network algorithm and the K-Nearest Neighbor algorithm, which are machine learning algorithms, using the actual weather and air quality data.