• Journal of radiological science and technology
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• v.36 no.2
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• pp.99-110
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• 2013

In the medical field, the necessity of education growth for the professional Radiation Technologists has been emphasized to become experts on radiation and the radiation field is important of the society. Also, in hospitals and companies, important on thesis is getting higher in order to active and cope with rapidly changing internal and external environment and a more in-depth expert training, the necessity of new teaching and learning model that can cope with changes in a more proactive has become. Thesis writing classes brought limits to the in-depth learning as to start a semester and rely on only specific programs besides, inevitable on passive participation. In addition, it does not have a variety opportunity to present, an actual opportunity that can be written and discussed does not provide much caused by instructor-led classes. As well as, it has had a direct impact on the quality of the thesis, furthermore, having the opportunity to participate in various conferences showed the limitations. In order to solve these problems, in this study, writing thesis has organized training operations as a consistent gradual deepening of learning, at the same time, the operational idea was proposed based on the connectivity integrated operating and effective training program & instructional tool for improving the ability to perform the written actual thesis. The development of teaching and learning model consisted of 4 system modeling, scaffolding, articulation, exploration. Depending on the nature of the course, consisting team following the personal interest and the topic allow for connection subject, based on this, promote research capacity through a step-by-step evaluation and feedback and, fundamentally strengthen problem-solving skills through the journal studies, help not only solving the real-time problem by taking wiki-space but also efficient use of time, increase the quality of the thesis by activating cooperation through mentoring, as a result, it was to promote a positive partnership with the academic. Support system in three stages planning subject, progress & writing, writing thesis & presentation and based on cognitive apprenticeship. The ongoing Coaching and Reflection of professor and expert was applied in order to maintain these activities smoothly. The results of this study will introduce actively, voluntarily and substantially join to learners, by doing so, culture the enhancement of creativity, originality and the ability to co-work and by enhance the expertise of based-knowledge, it is considered to be help to improve the comprehensive ability.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.49-61
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• 2009

This article provides training exercises for executives into interpreting subroutine maps of executives' thinking in processing business and industrial marketing problems and opportunities. This study builds on premises that Schank proposes about learning and teaching including (1) learning occurs by experiencing and the best instruction offers learners opportunities to distill their knowledge and skills from interactive stories in the form of goal.based scenarios, team projects, and understanding stories from experts. Also, (2) telling does not lead to learning because learning requires action-training environments should emphasize active engagement with stories, cases, and projects. Each training case study includes executive exposure to decision system analysis (DSA). The training case requires the executive to write a "Briefing Report" of a DSA map. Instructions to the executive trainee in writing the briefing report include coverage in the briefing report of (1) details of the essence of the DSA map and (2) a statement of warnings and opportunities that the executive map reader interprets within the DSA map. The length maximum for a briefing report is 500 words-an arbitrary rule that works well in executive training programs. Following this introduction, section two of the article briefly summarizes relevant literature on how humans think within contexts in response to problems and opportunities. Section three illustrates the creation and interpreting of DSA maps using a training exercise in pricing a chemical product to different OEM (original equipment manufacturer) customers. Section four presents a training exercise in pricing decisions by a petroleum manufacturing firm. Section five presents a training exercise in marketing strategies by an office furniture distributer along with buying strategies by business customers. Each of the three training exercises is based on research into information processing and decision making of executives operating in marketing contexts. Section six concludes the article with suggestions for use of this training case and for developing additional training cases for honing executives' decision-making skills. Todd and Gigerenzer propose that humans use simple heuristics because they enable adaptive behavior by exploiting the structure of information in natural decision environments. "Simplicity is a virtue, rather than a curse". Bounded rationality theorists emphasize the centrality of Simon's proposition, "Human rational behavior is shaped by a scissors whose blades are the structure of the task environments and the computational capabilities of the actor". Gigerenzer's view is relevant to Simon's environmental blade and to the environmental structures in the three cases in this article, "The term environment, here, does not refer to a description of the total physical and biological environment, but only to that part important to an organism, given its needs and goals." The present article directs attention to research that combines reports on the structure of task environments with the use of adaptive toolbox heuristics of actors. The DSA mapping approach here concerns the match between strategy and an environment-the development and understanding of ecological rationality theory. Aspiration adaptation theory is central to this approach. Aspiration adaptation theory models decision making as a multi-goal problem without aggregation of the goals into a complete preference order over all decision alternatives. The three case studies in this article permit the learner to apply propositions in aspiration level rules in reaching a decision. Aspiration adaptation takes the form of a sequence of adjustment steps. An adjustment step shifts the current aspiration level to a neighboring point on an aspiration grid by a change in only one goal variable. An upward adjustment step is an increase and a downward adjustment step is a decrease of a goal variable. Creating and using aspiration adaptation levels is integral to bounded rationality theory. The present article increases understanding and expertise of both aspiration adaptation and bounded rationality theories by providing learner experiences and practice in using propositions in both theories. Practice in ranking CTSs and writing TOP gists from DSA maps serves to clarify and deepen Selten's view, "Clearly, aspiration adaptation must enter the picture as an integrated part of the search for a solution." The body of "direct research" by Mintzberg, Gladwin's ethnographic decision tree modeling, and Huff's work on mapping strategic thought are suggestions on where to look for research that considers both the structure of the environment and the computational capabilities of the actors making decisions in these environments. Such research on bounded rationality permits both further development of theory in how and why decisions are made in real life and the development of learning exercises in the use of heuristics occurring in natural environments. The exercises in the present article encourage learning skills and principles of using fast and frugal heuristics in contexts of their intended use. The exercises respond to Schank's wisdom, "In a deep sense, education isn't about knowledge or getting students to know what has happened. It is about getting them to feel what has happened. This is not easy to do. Education, as it is in schools today, is emotionless. This is a huge problem." The three cases and accompanying set of exercise questions adhere to Schank's view, "Processes are best taught by actually engaging in them, which can often mean, for mental processing, active discussion."

• Korea Science and Art Forum
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• v.32
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• pp.319-334
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• 2018

This research is a case study to make sure the enhancement of students' imagination and creativity through developing and applying the Literature-Art-Science Integrated Education Program. Its research object was totally 25 persons of 29 students of the 1st to the 4 th Grades from Gunsan Sulsan Elementary School. Its research period lasted for 4 months from September to December, 2017, and I, as the research place, used the art room at Gunsan Sulsan Elementary School. The programs were totally 10 sessions with a unit of 1 session per each grade for 2 hours from 1:00 to 3:00 in the afternoon from Monday through Friday. I fixed ten themes of this program-eight plane modeling, and two solid modeling, and finished the work of storytelling during summer vacation. And I arranged their levels as low:middle:high(3:5:2) ones. The former was 'A Film of Monster Gorilla'(L), 'Learning the Spirit of Gyeongju Choi's Family'(M), 'A Tale of My Friend Made of Natural Materials'(L), 'The Reading of My Dream'(M), 'Gathering the Objects in My Mobile'(M), 'A Mock Trial of Marrying Off'(M), 'Painting My Favorite Children's Poem'(H), and 'Painting My Favorite Children's Song'(H), and the latter was 'Seeking for a Bluebird in My Mind'(L), and 'Making My Cherished Object' (M). Then I used the unique art expression technique per each theme, which were in sequence marbling, Korean paper art, combine painting, collage, imaginary painting, imaginary painting, play dough art, imaginary painting techniques. And I delivered to the students the scientific knowledge in terms of growing or manufacturing processes of materials used for making artworks. Prior to and after the processing this program, I surveyed about the students' ability of integrated thinking and emotional experience by 'Figure B Type' and 'Figure A Type' of The Torrance Tests of Creative Thinking, and took statistics with the resultant data. And I executed a paired t-test in order to verify the significance of mean difference in the result of investigation with those data. From the analyzed result according to the elements of creativity and the mean quotients of creativity, there showed a significant difference (t=3.47, p<.01) in 'fluency', and also a significant difference(t=3.59, p<.01) in 'creativity.' Judging from the statistic values of two fields such as the student's ability of integrated thinking and emotional experience, I estimate that over the majority of the students showed the enhancement in self-confident creative expression as well as higher interest and concern through this program. The result that I arranged and analyzed the making process of artworks, the photos of the resultant, etc. as such is as follows : Firstly, from this program being proceeded as art-centered STEAM class, the student's systematic problem-solving ability was improved in his ability of integrated thinking to transform the literary contents into artistic one. Secondly, the student obtained the emotional experience such as interest in the class, self-confidence, intellectual satisfaction, self-fulfillment, etc. through art-centered STEAM class using ten art expression techniques. Thirdly, the student's mind willing to cooperate, communicate with his friends, and care for them was ripened in the process of problem-solving. Fourth, the student's self-confidence was further instilled when presenting famous artists and their artworks in the introduction and finale of ten art expression techniques. Likewise, the statistic values on the fields of student's ability of integrated thinking and emotional experience illustrate that over the majority of the students showed improvement in the ability of creative expression with confidence as well as higher interest and concern upon this program.

• Korean Journal of Construction Engineering and Management
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• v.1 no.3 s.3
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• pp.124-134
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• 2000

In construction projects, the design information, which should contain accurate product information in a systematic way, needs to be applicable through the life-cycle of projects. However, paper-based 2D drawings and relevant documents has difficulties in communicating and sharing the owner's and architect's intention and requirement effectively and building a corporate knowledge base through on-going projects due to Tack of interoperability between specific task or function-oriented software and handling massive information. Meanwhile, computer and information technologies are being developed so rapidly that the practitioners are even hard to adapt them into the industry efficiently. 3D modeling capabilities in CAD systems are enormously developed and enables users to associate 3D models with other relevant information. However, this still requires a great deal of efforts and costs to have all the design information represented in CAD system, and the sophisticated system is difficult to manage. This research focuses on the transition period from 2D-based design Information management to 3D-based, which means co-existence of 2D and 3D-based management. This research proposes a model of a compound system of 2D and 3D-based CAD system which presents the general design information using 3D model integrating with 2D CAD drawings for detailed design information. This research developed an integrated information management system for design and specification by associating 2D drawings and 3D models, where 2D drawings represents detailed design and parts that are hard to express in 3D objects. To do this, related management processes was analyzed to build an information model which in turn became the basis of the integrated information management system.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.85-112
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• 2015

This study examined the relationship structure among personal and social cognitive factors contributing to professional help-seeking decisions to relieve distress of those in low-income, then suggested an appropriate model to inform knowledge for better social work practice. Using data of a purposive sampling from 331 low-income people, covariance structural analyses were conducted in two stages of model exploration, one for TPB model and another for its extended model including the level of distress, family support, and willingness. As results, in the path analyses with the observed variables of the basic components of the TPB, subjective norm showed the strongest effect on the intention, following by attitudes towards help-seeking, then behavioral control the least; in turn both the intention, positively, and behavioral control, negatively, contributed to help-seeking decisions. In the second stage of the path analyses with the extended model of the TPB, each of distress and family support demonstrated direct positive effect on each of attitudes, subjective norm, and behavioral control; each of the attitudes, subjective norm, and behavioral control showed positive effect on both intention and willingness; in turn, while intention showed strong positive effect on help-seeking decisions, willingness had no significant effect and behavioral control had negative effect on decisions. There were significant indirect effects of behavioral control on intention through willingness and of willingness on decisions through intention. These results suggested that the TPB model is useful for modeling help-seeking decisions through personal and social cognitions, especially the significance of subjective norm implied the importance of social cognition for the people in low-income with distress. Further, it was implied that the extended model needs to address particularity of those people in low-income and the mechanism shown by behavioral control and willingness implied the importance of practicing respect for the client's autonomy and will for self-support in social work practice.

• Asia pacific journal of information systems
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• v.21 no.3
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• pp.71-96
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• 2011

Within the traditional medical delivery system, patients residing in medically vulnerable areas, those with body movement difficulties, and nursing facility residents have had limited access to good healthcare services. However, Information and Communication Technology (ICT) provides us with a convenient and useful means of overcoming distance and time constraints. ICT is integrated with biomedical science and technology in a way that offers a new high-quality medical service. As a result, rapid technological advancement is expected to play a pivotal role bringing about innovation in a wide range of medical service areas, such as medical management, testing, diagnosis, and treatment; offering new and improved healthcare services; and effecting dramatic changes in current medical services. The increase in aging population and chronic diseases has caused an increase in medical expenses. In response to the increasing demand for efficient healthcare services, a telehealth service based on ICT is being emphasized on a global level. Telehealth services have been implemented especially in pilot projects and system development and technological research. With the service about to be implemented in earnest, it is necessary to study its overall acceptance by consumers, which is expected to contribute to the development and activation of a variety of services. In this sense, the study aims at positively examining the structural relationship among the acceptance factors for telehealth services based on the Technology Acceptance Model (TAM). Data were collected by showing audiovisual material on telehealth services to online panels and requesting them to respond to a structured questionnaire sheet, which is known as the information acceleration method. Among the 1,165 adult respondents, 608 valid samples were finally chosen, while the remaining were excluded because of incomplete answers or allotted time overrun. In order to test the reliability and validity of the assessment scale items, we carried out reliability and factor analyses, and in order to explore the causal relation among potential variables, we conducted a structural equation modeling analysis using AMOS 7.0 and SPSS 17.0. The research outcomes are as follows. First, service quality, innovativeness of medical technology, and social influence were shown to affect perceived ease of use and perceived usefulness of the telehealth service, which was statistically significant, and the two factors had a positive impact on willingness to accept the telehealth service. In addition, social influence had a direct, significant effect on intention to use, which is paralleled by the TAM used in previous research on technology acceptance. This shows that the research model proposed in the study effectively explains the acceptance of the telehealth service. Second, the research model reveals that information privacy concerns had a insignificant impact on perceived ease of use of the telehealth service. From this, it can be gathered that the concerns over information protection and security are reduced further due to advancements in information technology compared to the initial period in the information technology industry, and thus the improvement in quality of medical services appeared to ensure that information privacy concerns did not act as a prohibiting factor in the acceptance of the telehealth service. Thus, if other factors have an enormous impact on ease of use and usefulness, concerns over these results in the initial period of technology acceptance may become irrelevant. However, it is clear that users' information privacy concerns, as other studies have revealed, is a major factor affecting technology acceptance. Thus, caution must be exercised while interpreting the result, and further study is required on the issue. Numerous information technologies with outstanding performance and innovativeness often attract few consumers. A revised bill for those urgently in need of telehealth services is about to be approved in the national assembly. As telemedicine is implemented between doctors and patients, a wide range of systems that will improve the quality of healthcare services will be designed. In this sense, the study on the consumer acceptance of telehealth services is meaningful and offers strong academic evidence. Based on the implications, it can be expected to contribute to the activation of telehealth services. Further study is needed to assess the acceptance factors for telehealth services, such as motivation to remain healthy, health care involvement, knowledge on health, and control of health-related behavior, in order to develop unique services according to the categorization of customers based on health factors. In addition, further study may focus on various theoretical cognitive behavior models other than the TAM, such as the health belief model.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.