• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.203-210
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• 2017

In this paper, we propose an effective neural network model for image caption generation and model transfer. This model is a kind of multi-modal recurrent neural network models. It consists of five distinct layers: a convolution neural network layer for extracting visual information from images, an embedding layer for converting each word into a low dimensional feature, a recurrent neural network layer for learning caption sentence structure, and a multi-modal layer for combining visual and language information. In this model, the recurrent neural network layer is constructed by LSTM units, which are well known to be effective for learning and transferring sequence patterns. Moreover, this model has a unique structure in which the output of the convolution neural network layer is linked not only to the input of the initial state of the recurrent neural network layer but also to the input of the multimodal layer, in order to make use of visual information extracted from the image at each recurrent step for generating the corresponding textual caption. Through various comparative experiments using open data sets such as Flickr8k, Flickr30k, and MSCOCO, we demonstrated the proposed multimodal recurrent neural network model has high performance in terms of caption accuracy and model transfer effect.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.42-52
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• 2002

Information extraction(IE) is a process of recognizing and fetching particular information fragments from a document. In previous work, most IE systems generate the extraction rules called the wrappers manually, and although this manual wrapper generation may achieve more correct extraction, it reveals some problems in flexibility, extensibility, and efficiency. Some other researches that employ automatic ways of generating wrappers are also experiencing difficulties in acquiring and representing useful domain knowledge and in coping with the structural heterogeneity among different information sources, and as a result, the real-world information sources with complex document structures could not be correctly analyzed. In order to resolve these problems, this paper presents an agent-based information extraction system named XTROS that exploits the domain knowledge to learn from documents in a semi-structured information source. This system generates a wrapper for each information source automatically and performs information extraction and information integration by applying this wrapper to the corresponding source. In XTROS, both the domain knowledge and the wrapper are represented as XML-type documents. The wrapper generation algorithm first recognizes the meaning of each logical line of a sample document by using the domain knowledge, and then finds the most frequent pattern from the sequence of semantic representations of the logical lines. Eventually, the location and the structure of this pattern represented by an XML document becomes the wrapper. By testing XTROS on several real-estate information sites, we claim that it creates the correct wrappers for most Web sources and consequently facilitates effective information extraction and integration for heterogeneous and complex information sources.

• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.107-116
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• 1999

The effects of clarified mapping strategy and placement of analog on middle school students' conceptual understanding were investigated. According to the usage of clarified mapping strategy and the sequence in presenting analogy, four types of learning materials were developed and pilot tested. Prior to the treatment, the field dependence-independence test was administered and a previous achievement test scores were obtained. The scores were used as blocking variables. The learning materials were read by randomly assigned middle school students (N=111), and the conceptions test was administered immediately and four weeks later. In the recall problems of immediate and retention test, there were no significant differences. In the application problems of immediate and retention test, however, the students learned with clarified mapping strategy scored significantly higher than those with analog-only. Field-independent students learned with clarified mapping strategy scored significantly higher in the immediate application than those with analog-only, and higher-level students learned with clarified mapping strategy scored significantly higher in the retention application than those with analog-only. In the immediate application, higher-level students learned analog first with clarified mapping strategy scored significantly higher in the immediate application than those learned target concept first with clarified mapping strategy. However, lower-level students learned target concept first with clarified mapping strategy scored significantly higher than those learned analog first with clarified mapping strategy.

• Journal of The Korean Association For Science Education
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• v.35 no.5
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• pp.805-815
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• 2015

The Korean education curriculum is making efforts to improve education to foster competencies that the future society demands through the 2007 and 2009 revised curriculum. The revised curricula focus on enhanced articulation for the quality curriculum. In this study, the curriculum is analyzed for vertical and horizontal articulation. In addition, the study found a problem in Korea's curriculum through international comparison and sought improvement. Furthermore, the study compared internationally articulation of the concept of photosynthesis, of which the results are as follows. First, our science curriculum focuses on vertical articulation and has relatively neglected the problem of horizontal articulation. To compensate for this problem, curriculum design should introduce aspects of 'nature' and 'environment' and should consider the interests and concerns of students, as countries with high horizontal articulation do. Second, the actual education field has a problem with the a lack of continuity and sequence because of concentration of concept in a specific grade or simply repeating the concept across multiple grades. These results have led to alternative proposals that should arrange basis of concept configuration such as 'Big Idea' and should establish the adoption of 'systems' frequently appearing in the other curricula. Finally, there may be mentioned a lack of research on students' learning progression, which can be a common standard of horizontal and vertical articulation. Research on learning progression has been a trend overseas, but there exists no study to fit Korea's situation, so education fields need to conduct the appropriate research on learning progression as part of the commitment to high-quality curriculum.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.386-396
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• 2020

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

• 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."

• Journal of The Korean Association For Science Education
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• v.33 no.7
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• pp.1385-1402
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• 2013

This study analyzes pre-service teachers' PCK dealing with visualization of the contents related to boiling point elevation and teaching methods in mock-lessons. As a result of analyzing pre-service teachers' knowledge based on PCK factors, most of the pre-service teachers accentuated on understanding boiling point elevation conceptually, whereas some of the others inclined to make students understand boiling point elevation in a scientific way, let the kids use numerical formulas to describe the concept, and motivate them to learn through the examples in real life. The pre-service teachers represented majority of the important facts of boiling point elevation as the knowledge required to understand things conceptually. However, they did not focus on improving the scientific thinking and inquiring levels of the students. Also, the pre-service teachers tended to teach at the level and order of the textbook. In some other cases, they considered the vocabularies and materials in the textbook (which could have been highlighted in the editing sequence) as the main topic to learn, or regarded the goal as giving students the ability to solve exercises in the textbook. It turned out that the pre-service teachers had a low level of knowledge of their students. It is recommended that they should make use of the materials given (such as data related to the misconception of students) during the training session. The knowledge of teaching and evaluating students was described superficially by the pre-service teachers; they merely mentioned the applications of models, such as the cyclic model and discovery learning, rather than thinking of a method related to the goals, or listed general assessment methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1066-1072
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• 2011

In the method to analyze the relationship in the system call orders, the normal system call orders are divided into a certain size of system call orders to generates gene and use them as the detectors. In the method to consider the system call parameters, the mean and standard deviation of the parameter lengths are used as the detectors. The attack of which system call order is normal but the parameter values are changed, such as the format string attack, cannot be detected by the method that considers only the system call orders, whereas the model that considers only the system call parameters has the drawback of high positive defect rate because of the information obtained from the interval where the attack has not been initiated, since the parameters are considered individually. To solve these problems, it is necessary to develop a more efficient learning and detecting method that groups the continuous system call orders and parameters as the approach that considers various characteristics of system call related to attacking simultaneously. In this article, we detected the anomaly of the system call orders and parameters by applying the temporal concept to the system call orders and parameters in order to improve the rate of positive defect, that is, the misjudgment of anomaly as normality. The result of the experiment where the DARPA data set was employed showed that the proposed method improved the positive defect rate by 13% in the system call order model where time was considered in comparison with that of the model where time was not considered.

• Journal of the Korean Society of School Health
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• v.13 no.1
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• pp.63-84
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• 2000

Primary school is regarded as an important period when many health-related behaviors and life-styles begin to be formed. Acquiring them through school heath education has a strong influence on the health promotion of not only the family but also the community. Primary school health education in Korea has a systematic flaw in that health-related subjects are divided and taught under various subjects in primary school. In order to develop a proper school health curriculum, it is essential to assess what is currently being taught. In this study the current health education of the 6th grade primary curriculum was investigated to improve school health education. The purpose of this study is to identify the health education contents and time in textbooks of the 6th grade primary school curriculum. In this study, the textbooks & teacher's teaching manuals of the 6th grade curriculum were analyzed with a health instruction framework for Korean schools developed by the Korean Nurse Association & Korean School Health Education Association in 1993 and health care framework for health education curriculum presented by Kim in 1991. The results are as follows ; 1) Health education hours of the curriculum are 206 hours, about 34.3 hours a year. 2) The contents of health education were divided into nine subjects at primary schools. Organizing principles of learning experience(eg, integrity, sequence and continuity) were not considered sufficiently. The physical education & natural science subjects include a lot of health education contents. 3) The major content areas are community & environmental health areas and daily healthy life areas. 4) The major areas at each grade level are daily healthy concerns and safety & first-aid 5) The remarkable contrast to the 5th primary school curriculum are that environmental health is offered to the first grade step by step, and that drug use & abuse and mental health education are included in the 6th primary school curriculum. 6) The main contents of health education in 1st, 2nd. and 3rd grade curricula consist of treatment & recovery health functions. Those of the 4th grade curriculum consist of treatment & recovery health functions, and daily healthy life functions. Those of the 5th grade consist of growth & development functions. Those of the 6th grade consist of treatment & recovery health, growth & development functions. Most health care functions belong to physical health care. The results above suggest that we put together the divided contents of health education and manage them on the basis of systematic integration.