• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.83-89
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• 1999

For the object-oriented development methods authorized by OMG a number of official methodologies such as Booch, Coad/Yourdon methods are used in the development of the object-oriented soft ware and each methodology represents its process of analysis and design using different symbols. Between those notation methodologies there exist some differences as a matter of development, information for transfer, accordingly they act on the transfer of the information in the object-oriented development as obstacle and give rise to they natural outcome of error. The aim of this study is in the presentation of ideas that decrease the probability of errors by means of verify8ing the transferring ability of information which the existing diagrammatic notation methodologies have had on the basis of cognitive science, minimizing the cognitive load from the object-oriented development through enhancing symbols in each obstacles to transferring information and removing them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.745-750
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• 2008

In a cognitive radio based system, quality of service (QoS) for the secondary user must be maintained as much as possible even while that of the primary user is protected all he time. In particular, switching wireless links for the secondary user during the transmission of multimedia data causes delay and information loss, and QoS degradations occur inevitably. The efficient resource management scheme is necessary to support the seamless multimedia service to the secondary user. This paper proposes a novel frequency selection method based on Multi-Criteria Decision Making (MCDM), in which uncertain parameters such as received signal strength, cell load, data rate, and available bandwidth are considered during the decision process for the frequency selection with the fuzzy set theory. Through simulation, we show that our proposed frequency selection method provides a better performance than the conventional methods which consider the received signal strength only.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.1-7
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• 2012

Cognitive radio, a new paradigm for wireless communication, is being recently expected to support various types of multimedia traffics. To guarantee Quality of Service (QoS) from SUs, a static packet priority policy can be considered. However, this approach can easily satisfy Quality of Service of high priority application while that of lower priority applications is being degraded. In the paper, we propose a fuzzy-based dynamic packet scheduling algorithm to support multimedia traffics in which the dynamic packet scheduler modifies priorities of packets according to Fuzzy-rules with the information of priority and delay deadline of each packet, and determines which packet would be transmitted through the channel of the primary user in the next time slot in order to reduce packet loss rate. Our simulation result shows that packet loss rate can be improved through the proposed scheme when overall traffic load is not heavy.

• Journal of The Korean Association of Information Education
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• v.20 no.6
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• pp.629-644
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• 2016

The purpose of this study was to investigate the effects of whole-task sequencing emphasis manipulation on expertise acquisition in web based complex task. To achieve the purpose, emphasis manipulation sequencing type is composed of a simple emphasis manipulation, a snowballing manipulation, and a full emphasis manipulation sequencing and participants was drawn from a pool of 93 undergraduate students sampled for the study. According to the findings, a snowballing manipulation group invested significantly lower cognitive load than a full emphasis manipulation group but did not a simple emphasis manipulation group. Based on these findings, though complex task is included of high interactivity owing to real task, learner cannot suffer cognitive overload because emphasis manipulation which can view the whole task and the part task in parallel provides meta cognition for learner. And whole-task sequencing emphasis manipulation affects to transfer. The snowballing emphasis manipulation group invested significantly higher than simple emphasis manipulation group and full emphasis manipulation group. Based on these findings, the snowballing manipulation which learner use whole-task sequencing and part-task sequencing simultaneously contribute to understandings and ability to solve problems for complex task and it will in turn, lead to expertise acquisition.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.237-244
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• 2016

The purpose of this study examines the influence of anchoring and adjustment according to regularity of snack's shape. Especially this study choose 'choco chip' as a snack with regularity to compare with 2012's study. The results of this study are as follows. 1) The anchoring and adjustment also appears in 'choco chip' with regularity. But statistical meaning is different from the result of prior study. 2) The difference of anchoring effect reduced according to additional verbal information in 'choco chip' with regularity. but has no statistical meaning of interaction effect. However the causes of two results are different. 3) The interaction effect of cognitive load(without vs. moderate vs. high) is meaningful statistically. difference of anchoring effect reduced according to additional verbal information in 'choco chip' with regularity in 'choco chip' with regularity. This study provides strategic implication that it overcomes the limitation of the prior resuarch and may reinforce the biased judgement by cognitive overload.

• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.209-239
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• 2015

Previous studies on the effect of incidental semantic priming on judgment, have focused mainly on mutually exclusive hypotheses. However, the present study explored whether incidental semantic activation affects diagnostic inference depending on the type of the hypothesis: mutually exclusive hypotheses vs compatible hypotheses. In Experiment 1, in case of mutually exclusive hypotheses, the final hypothesis was selected according to the incidental semantic priming, but there was no difference in the number of generated hypothesis in comparison with the control. However, for compatible hypotheses (i.e., both hypotheses can be true), the semantic priming affected the number of generated hypotheses, but not the selection of the final hypothesis. The same pattern of results was observed even when the cognitive load was increased. In Experiment 2, we found a boundary condition of incidental semantic activation on diagnostic inference. When cues related to each of the hypotheses were presented simultaneously, the incidental semantic effect disappeared. These results suggest that people consider all possible cues when making diagnostic inference in daily life. In light of these findings, further research on hypothesis generation/evaluation should take the type of hypothesis into account.

• Korean Journal of Cognitive Science
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• v.16 no.3
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• pp.155-174
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• 2005

Oh and Kim (2004) and Woodman and Lurk (2004) demonstrated that spatial working memory (SWM) load Interfered concurrent visual search and that search process also impaired the maintenance of spatial information implying that visual search and SWM task both require access to the same limited-capacity mechanism. Two obvious possibilities have been suggested about what this shared limited-capacity mechanism is: common demand for attention to the locations where the items f9r the two tasks were presented (spatial attention load hypothesis), and common use of working memory to maintain a record of locations have been processed(SWM load hypothesis). To test these two hypothetical explanations, Experiment 1 replicated the mutual interference between visual search and SWM task in spite of difference of procedure with preceding researches; possible areas where the items for two tasks were presented were not separated. In Experiment 2, we presented the items for visual search either in the same quadrants where the items for SWM task had appeared (same-location rendition) or in the different quadrants (different-location condition). As a result, search efficiency was more impaired in the different-location condition than in the same-location condition. The memory accuracy was worse in the different-location rendition than in the same-location rendition. Overall results of study indicate that the mutual interference between SWM and visual search might be related to the overload of spatial attention, but not to that of SWM.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.481-496
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• 2012

Purpose: This paper aims at improving inefficiency of an existing posterior preference articulation method proposed for dual response surface optimization. The method generates a set of non-dominated solutions and then allows a decision maker (DM) to select the best solution among them through an interval selection strategy. Methods: This paper proposes an iterative posterior preference articulation method, which repeatedly generates the predetermined number of non-dominated solutions in an interval which becomes gradually narrower over rounds. Results: The existing method generates a good number of non-dominated solutions not used in the DM's selection process, while the proposed method generates the minimal number of non-dominated solutions necessitated in the selection process. Conclusion: The proposed method enables a satisfactory compromise solution to be achieved with minimal cognitive burden of the DM as well as with light computation load in generating non-dominated solutions.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.24-33
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• 2017

Since 'teaching coding' or 'programming' classes for computational thinking (CT) education in K-12 are renowned around the world, a pre-service teachers' readiness for integrating CT into their teaching subjects is important due to the fact that CT is considered to be another 'R' from algoRitm for 21st century literacy, in addition to the traditional 3R (Reading, Writhing, and Arithmetic) [2] and CT roles to other disciplines. With this rationale, we designed a guided instruction based CT course for pre-service teachers. We show the effectiveness of the program with respect to the teachers' attitude toward combining CT into their teaching subjects, and mindset changes of learning computing connected with the career development of the teacher themselves. The research focused on the instructional methodology of teaching programing for non-Computer Science Education (CSE) majors who are not familiar with computer science for alleviating the cognitive load of first exposure to programming course under the CT concepts.