• Journal of Digital Convergence
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• v.19 no.12
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• pp.231-241
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• 2021

The consulting industry is also growing as the use of management consulting increases as the importance of management strategies for the survival of companies due to rapid environmental changes is highlighted. As a result, competition among consulting firms is intensifying, and securing competitiveness is essential. This study tried to confirm the method of securing competitiveness of consulting firms from the perspective of organizational competitiveness through organizational effectiveness. As the consulting service is a knowledge service field, an empirical analysis was conducted for consultants who performed it because professional human resources were a core technology. Exploratory factors, reliability analysis, and regression analysis were performed using SPSS 22.0. As a result of a study on the effect of consultants' organizational effectiveness on customer orientation, it was found that the consultant's organizational commitment and organizational performance had a significant effect. It was possible to confirm the importance of intrinsic motivation to secure the competitiveness of consulting firms in the changing environment. This presented practical implications for organizational management regarding the continuous growth of consulting firms.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.403-410
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• 2021

Given an undirected simple graph, k-club is one of the proposed structures to model social groups that exist in various types in Social Network Analysis (SNA). Maximum k-Club Problem (MkCP) is to find a k-club of maximum cardinality in a graph. This paper introduces a Genetic Algorithm called HGA+DROP which can be used to approximate maximum k-club in graphs. Our algorithm modifies the existing k-CLIQUE & DROP algorithm and utilizes Heuristic Genetic Algorithms (HGA) to obtain multiple k-clubs. We experiment on DIMACS graphs for k = 2, 3, 4 and 5 to compare the performance of the proposed algorithm with existing algorithms.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.63-73
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• 2021

Sudden implementation of online classes in higher education due to Covid19 pandemic implies a lot of worries about academic performance declining. Thus, this paper analyzed a class(CalculusI) taught 100% online which was the same as the offline class before. This class tried to maintain the same quality as the offline one by utilizing various teaching strategies. The result shows the academic achievement level was similar or higher than that of offline class, and so was the students' perception and satisfaction degree. However, this was not just the outcome of online class, but it came from the professor's well-designed teaching plan and smooth operation of the class. It implicates successful teaching methodology is more important factor than such medium difference as online or offline. This study suggests the potential of online classes after the Covid19 pandemic, and expects further studies verify the result in a lot more curricula.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3406-3412
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• 2021

A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and ²²Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.341-345
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• 2021

Known to improve resistance to chloride ingress, blast furnace slag is a widely used supplementary cementitious material. However, a detailed characterization of cements blended with slag exposed to seawater remains unavailable. This study employs thermodynamic modeling as a toolkit for assessing the long-term phase evolution of slag cement in seawater. The modeling result shows that slag incorporation leads to the formation of phases that are less prone to structural alteration in seawater. Formation of more ettringite is expected to induce expansion in both plain and blended cements, while brucite is unstable in the blended systems. Despite this, the porosity is expected to increase in the blended cements, and aluminate hydrates with a higher chloride binding capacity are more abundant in the blended cements. The results suggest that the use of slag in concrete improves the durability performance of concrete in marine environments.

• Atmosphere
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• v.31 no.4
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• pp.421-431
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• 2021

The Yeongdong region is frequently vulnerable to heavy snowfall in winter in terms of societal and economical damages. By virtue of a lot of previous efforts, snowfall forecast has been significantly improved, but the performance of light snowfall forecast is still poor since it is very conducive to synoptic and mesoscale interactions, largely attributable to Taeback mountains and East Sea effects. An intensive observation has been made in cooperation with Gangwon Regional Meteorological Office and National Institute of Meteorological Studies in winter seasons since 2019. Two distinctive Cold Air Damming (CAD) events (14 February 2019 and 6 February 2020) were observed for two years when the snowfall forecast was wrong specifically in its location and timing. For two CAD events, lower-level temperature below 2 km ranged to lowest limit in comparisons to those of the previous 6-years (2014~2019) rawinsonde soundings, along with the stronger inversion strength (> 2.0℃) and thicker inversion depth (> 700 m). Further, the northwesterly was predominant within the CAD layer, whereas the weak easterly wind was exhibited above the CAD layer. For the CAD events, strong cold air accumulation along the east side of Taeback Mountains appeared to prevent snow cloud and convergence zone from penetrating into the Yeongdong region. We need to investigate the influence of CAD on snowfall in the Yeongdong region using continuous intensive observation and modeling studies altogether. In addition, the effect of synoptic and mesoscale interactions on snowfall, such as nighttime drainage wind and land breeze, should be also examined.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.505-525
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• 2021

Purpose: The purpose of this study was to investigate factors influencing the intention to use Fintech Digital Sand(D-Testbed), which facilitate digital innovation in the financial sectors and allow fintech startups to simulate the PoC their innovative ideas before starting a business. Methods: This study used the Extended Technology Acceptance Model (TAM2), with independent variables such as social influence, personal innovativeness, service quality, relative advantage, and security concerns used in previous studies, for analysis. For mediator variables, the perceived usefulness and perceived ease of use were used in this study. Results: The results indicated that social influence and perceived usefulness have a positive effect on the intention to use. It was also analyzed that relative advantage has a mediating effect on perceived usefulness whereas service quality nor personal innovativeness are not statistically significant mediation. On the other hand, perceived ease of use on the intention is not statistically significant. By this, it was confirmed that the intention to use Fintech Digital Sand(D-Testbed) was to improve the business performance of fintech companies, but not because it was easy to learn and take less effort. Conclusion: The finding of the study provides valuable implications for invigorating the use of fintech digital sandbox(D-testbed) and identifying the factors that affect the perception and intention to use among employees in fintech companies in advance.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.30-38
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• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.65-74
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• 2021

Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.