• Journal of The Korean Association For Science Education
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• v.28 no.2
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• pp.130-138
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• 2008

The purpose of this study was to analyze the effects of Science Writing Heuristic(SWH) strategy on problem-solving type inquiry modules through student-student verbal interactions. The modules were applied to 23 students of the 3rd grade in middle school and the SWH strategy was applied to 3 experimental groups. The SWH is the strategy that each student, first of all, has a chance to think and propose ways of problem-solving by individual writing a blue card when problems were emerged, and then students discuss ways of problem-solving with group members by writing a green card. Verbal interactions during small group discussions were audio- and video-taped, transcribed and analyzed to compare the effect of the SWH strategy. As a results, experimental groups tended to force solely on questions and suggestions about problem-solving, but controlled groups executed experiment and discussed about problem-solving simultaneously. The analysis also showed that the experimental students dialogued more on the deep-leveled argumental interactions than the controlled students did; in particular, show more SS3 and SD1 verbal interaction regarding suggestions of problem solving. We argue, therefore, that the SWH strategy is effective to the problem-solving type inquiry modules.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.49-63
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• 2024

Objective : The purpose of this study was to investigate parental perspectives on cognitive rehabilitation using a combination of phenomenological research methodology and word cloud analysis. Methods : Interviews were conducted with five parents of children with developmental disabilities. Word cloud analysis was conducted using Python, and five researchers analyzed the meaning units and themes using phenomenological methods. Words with high frequency were considered as a heuristic tool. Results : A total of 43 meaning units and nine components related to the phenomenon of cognitive rehabilitation were derived, and three themes were finalized. The main themes encompassed the definition of cognitive rehabilitation, challenges associated with cognitive rehabilitation, and factors influencing the selection of a cognitive rehabilitation institute. Cognitive rehabilitation emerged as a treatment focused on improving learning, daily functioning, and cognitive abilities in children with developmental disabilities. The perceived issues with cognitive rehabilitation pertained to treatment methods, therapist expertise, and associated costs. In addition, parents highlighted the importance of therapist expertise, humane personality, and affordability of cost and schedule when choosing a cognitive rehabilitation institute. Conclusion : Parents expressed expectations for substantial improvements in their children's daily functioning through cognitive rehabilitation. However, challenges were identified in clinical practices. Going forward, we expect that cognitive rehabilitation will evolve into a better therapeutic support service addressing the concerns raised by parents.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.

• Information Systems Review
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• v.21 no.1
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• pp.135-152
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• 2019

Recently there are many development and support policies for start-up companies because of successful venture companies related to ICT services. However, as these policies have focused on the support for the initial stage of start-up, many start-up companies have difficulties to continuously grow up. The main reason for these difficulties is that they recognize start-up tasks as independent activities. However, many experts or related articles say that start-up tasks are composed of related processes from the initial stage to the stable stage of start-up firms. In this study, we models the start-up processes based on the survey collected by the start-up companies, and analyze the start-up process of ICT service companies with process mining techniques. Through process mining analysis, we can draw a sequential flow of tasks for start-ups and the characteristics of them. The analysis of start-up businessman, idea derivation, creating business model, business diversification processes are resulted as important processes, but marketing activity and managing investment funds are not. This result means that marketing activity and managing investment funds are activities that need ongoing attention. Moreover, we can find temporal and complementary tasks which could not be captured by independent individual-level activity analysis. Our process analysis results are expected to be used in simulation-based web-intelligent system to support start-up business, and more cumulated start-up business cases will be helpful to give more detailed individual-level personalization service. And our proposed process model and analyzing results can be used to solve many difficulties for start-up companies.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• Journal of the Korean Chemical Society
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• v.68 no.1
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• pp.40-53
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• 2024

The study investigates the number and proportion of questions in each area by examining Chemistry I questions from the College Scholastic Ability Test from 2019 to 2022. The analysis was conducted using a three-dimensional framework that included key concepts in chemistry, behavioral domains in chemistry, and behavioral domains in mathematics. The results indicated that Chemistry I questions on the College Scholastic Ability Test had a relatively even distribution of questions across core individual topics, but highly difficult questions were predominantly biased toward stoichiometry. In terms of the behavioral domains in chemistry, there was a remarkably low proportion of questions related to problem recognition and hypothesis establishment, as well as designing research and implementing research. Conversely, highly difficult questions were more inclined towards drawing conclusions and evaluations. Regarding behavioral domains in mathematics, there was a limited number of questions addressing heuristic reasoning and deductive reasoning. On the other hand, high-difficulty questions favored internal problem-solving ability. Additionally, certain key concepts in chemistry and behavioral domains in chemistry exhibited a strong correlation with specific behavioral domains in mathematics. This characteristic was particularly evident in questions that encompassed higher-dimensional behavioral domains in mathematics, which students tend to find challenging.

• Journal of Distribution Research
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• v.14 no.2
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• pp.67-95
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• 2009

The purpose of this study is to empirically examine the transference of trust process, an important factor to consumer's purchase decision-making. Even though several researchers have discussed the trust transference process, there is no research related to this concept. Specifically we have focused on the transference of trust from the retailer to low involvement private label (PL) products. PL products were chosen as transference of trust occurs under ambiguity due to lack of information about the product and their manufacturer. PL products provide relatively less information than national brand (NB) products. In addition, retailers have been rapidly expanding their PL product categories. To identify the theoretical and empirical limitations of prior studies, we discuss several theories explaining the transference of trust: 'Balance theory' and 'availability heuristic' in transference of cognitive trust; 'affective transference' and 'affect as information' in transference of affective trust. An empirical test was performed. A self completion questionnaire was developed and administered to a convenience sample of PL users. 206 usable questionnaire were received. The results show that the transference of trust plays a mediating role linking the retailer to the manufacturer and to the product. Although our model, which included the transference process of trust as a mediating effect, did not improve the competitive model, the coefficients of the respective paths were found to be better. This study confirms the transference of cognitive trust from the retailer to both the manufacturer and the product, but not for affective trust. We offer the explanation that PL products may tend to have affective trust resulting from brand familiarity but not to their PL manufacturers.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Cartoon and Animation Studies
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• s.30
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• pp.201-219
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• 2013

Development of digital media in education field at America, UK, Japan etc bring big progress on digital device if education. Japan bring huge progress on digital education by nationally. UK use huge a national budget at digital education development and Sweden which is advanced country of education and a welfare state. Especially UK and Sweden's digital education markets are full now aspect more high quality design. Korea which is advanced country of IT adopted digital text book 2007 with mathematics, through science and English digital text book through the state. Korea's digital text book is in a transition period. that needs case study of advanced country of education for setting design guide and educational effect to digital education media and device plan. All researches are based on LG europe design center at London. Analysis by using KJ method, survey of questionnaire, heuristic method at 4 schools in UK and Sweden. Through analytical researches want to more reality simulation at digital education, and high quality contents with digital socialization. co-work with analog, can get any where, anytime user want without any difficulty. Also interactive GUI design of digital education device to easy to access for user. When plan Digital text book content and design needs methodical design guide for target who students and environment an in-depth study of the appraisal and method. The results of the research are introduce the design plan as a basic research and giving useful design plan to make digital educational media in Korea industrial aspect.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.