• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.261-268
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• 2017

The development of ICT technology centered on mobile smart platforms have been emerging as a shared economic platform based on collaborative consumption. In this study, we analyze what factors affect success and failure in commercialized shared economic platforms from 2008 to 2016, and present what policy factors are needed to activate shared economic platform. To do this, we analyze successful cases of shared economic platforms and failed cases, derive key variables that affect success and failure, and conduct inductive analysis based on ID 3 algorithm based on them. Through this, we present the policy factors for the commercial success of the shared economic platform by deriving the rules for the success and failure of the shared economic platform.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.686-693
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• 2022

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.151-160
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• 2017

The purpose of this study was to investigate the current status of information and communications technology(ICT) uses of the faculty members at national universities of education and to provide guidelines for ICT in education. The survey was conducted to examine factors related to the use of ICT, including faculty members' abilities to use ICT with respect to gender, age, and previous experiences with computers and to analyze their relevance to instructional methods. The results of the survey showed that female faculty members and male faculty members in their sixties and those with less years of teaching career were more likely to need support for using ICT than the others. The direct instructional method and discussion were the most frequently used; discussion was positively correlated with ICT uses. It was also found that faculty members anticipated more difficulties in using ICT in class and needed to work in a collaborative way and learn more effective use of ICT in the teaching and learning process.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.207-215
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• 2020

The purpose of this study is to examine the effectiveness of PBL in a college AD/PR class. This class was run under the theme of 'Sinchon Regeneration Project' in the 'Seodaemun-gu & University Collaborative Project Program'. As a result of the pre-post surveys, the authors found a significant increase in the place attachment to Sinchon, which was considered as a critical variable in this program. This research is an important study with high practicality in the area of cultural content production education PBL. The PBL teaching method can contribute to enhancing the class satisfaction and strengthening the competitiveness of students in cultural content related majors.

• 대한공업교육학회지
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• v.45 no.1
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• pp.20-41
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• 2020

The purpose of this study is to explore ways to improve teaching and learning in specialized high schools by investigating the relationship between creativity and class satisfaction in classes using the NFTM-TRIZ model for specialized high school students. In order to achieve the purpose of the research, first, the differences between the applied effects, experiments, and control groups were analyzed when the NFTM-TRIZ model was applied. Second, when the NFTM-TRIZ model was applied, it was analyzed whether there was a significant difference in creativity and class satisfaction by group size. The conclusions of this study are as follows. First, as a result of comparing the preand post-tests of the experimental group and the control group applying the NFTM-TRIZ model through the t-test, the experimental group showed significant differences in creative spontaneity, identity, attachment, curiosity and class satisfaction. Second, in experimental groups with the NFTM-TRIZ model, the size of groups of 4 and 6, rather than the size of groups of 2, had positive effects on class satisfaction. Therefore, the NFTM-TRIZ model based on collaborative learning was effective as a teaching and learning method that promoted creativity and satisfaction of students in specialized high schools.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.85-93
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• 2011

The music education improves the creative talent, social skills and academic achievement of the students. For the efficient music education, the learner centered study is highly recommended rather than the passive education, which supports self-control in selecting teaching materials, learning patterns and speed. For the successful self learning, it is requested to develop the collaborative educational learning tools, especially electronic collaborators such as H/W and S/W. Though there exist many commercialized electronic instruments and the PC MIDI based softwares, these tools have some limits and problems for the primary student to learn playing the musical instrument by himself. In this paper, we propose a supporting tool implementation method using an electronic keyboard instrument with USB Interface and PC-based software to help the primary student to learn playing the musical instrument. We implemented an electronic keyboard instrument module compactly and at low cost using a PIC18F4550 MCU. PC based software was developed to edit musical score, process the MIDI information, and interact with the electronic keyboard instrument module. This tool can offer a similar keyboard instrument environment and can be incorporated with self learning contents.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.

• Communications of Mathematical Education
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• v.23 no.1
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• pp.109-128
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• 2009

This study aimed to investigate students' learning process by examining their perception process of problem structure and mathematization, and further to suggest an effective teaching and learning of mathematics to improve students' problem-solving ability. Using the qualitative research method, the researcher observed the collaborative learning of two middle school students by providing problem-posing activities of five lessons and interviewed the students during their performance. The results indicated the student with a high achievement tended to make a similar problem and a new problem where a problem structure should be found first, had a flexible approach in changing its variability of the problem because he had advanced algebraic thinking of quantitative reasoning and reversibility in dealing with making a formula, which related to developing creativity. In conclusion, it was observed that the process of problem posing required accurate understanding of problem structures, providing students an opportunity to understand elements and principles of the problem to find the relation of the problem. Teachers may use a strategy of simplifying external structure of the problem and analyzing algebraical thinking necessary to internal structure according to students' level so that students are able to recognize the problem.