• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.15-20
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• 2022

An improvement of computational resources with a large scale cluster service is available to the individual person, which has been limited to the original industry and research institute. Therefore, the application of powerful computational resources to the engineering design has been increased fast. In naval and marine industry, the application of Computational Fluid Dynamics, which requires a huge computational effort, to a design of ship and offshore structure has been increased. Floating bodies such as the ship or offshore structure is exposed to ocean waves, current and wind in the ocean, therefore the precise modelling of those environmental disturbances is important in Computational Fluid Dynamics. Especially, ocean waves has to be nonlinear rather than the linear model based on the superposition due to a nonlinear characteristics of Computational Fluid Dynamics. In the present study, a fast reconstruction technique is suggested and it is validated from a series of simulations by using the Computational Fluid Dynamics.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.25-34
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• 2022

With the development of online services, the ripple effect of negative comments is increasing, and the damage of cyber violence is rising. Various methods such as filtering based on forbidden words and reporting systems prevent this, but it is challenging to eradicate negative comments. Therefore, this study aimed to increase the accuracy of the classification of negative comments using deep learning and blur the parts corresponding to profanity. Two different conditional training helped decide the number of deep learning layers and filters. The accuracy of 88% confirmed with 90% of the dataset for training and 10% for tests. In addition, Grad-CAM enabled us to find and blur the location of swear words in negative comments. Although the accuracy of classifying comments based on simple forbidden words was 56%, it was found that blurring negative comments through the deep learning model was more effective.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.58-73
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• 2022

The purpose of this study is to construct a predictable estimation model that reflects the relationship between the variables of mobile app performance and to verify how app reviews affect app performance. In study 1 and 2, the relationship between app performance indicators was derived using correlation analysis and random forest regression estimation of machine learning, and app performance estimation modeling was performed. In study 3, sentiment scores for app reviews were by using sentiment analysis of text mining, and it was found that app review sentiment scores have an effect one lag ahead of the number of daily installations of apps when using multivariate time series analysis. By analyzing the dissatisfaction and needs raised by app performance indicators and reviews of apps, companies can improve their apps in a timely manner and derive the timing and direction of marketing promotions.

• Journal of Creative Information Culture
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• v.7 no.3
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• pp.157-165
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• 2021

As the use of various application services utilizing Web and IoT and the need for large amounts of data management expand accordingly, the importance of efficient data expression and exchange scheme and data query processing is increasing. JSON, characterized by its simplicity, is being used in various fields as a format for data exchange and data storage instead of XML, which is a standard data expression and exchange language on the Web. This means that it is important to develop indexing and query processing techniques to effectively access and search large amounts of data expressed in JSON. Therefore, in this paper, we modeled JSON data with a hierarchical structure in a tree form, and proposed indexing and query processing using the path concept. In particular, we designed an index structure using a suffix array widely used in text search and introduced simple and complex path-based JSON data query processing methods.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.21-29
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• 2022

Based on the importance of dialect research, preservation, and communication, this paper conducted a study on machine translation of Korean dialects for dialect users who may be marginalized. For the dialect data used, AIHUB dialect data distributed based on the highest administrative district was used. We propose a many-to-one dialect machine translation that promotes the efficiency of model distribution and modeling research to improve the performance of the dialect machine translation by applying Copy mechanism. This paper evaluates the performance of the one-to-one model and the many-to-one model as a BLEU score, and analyzes the performance of the many-to-one model in the Korean dialect from a linguistic perspective. The performance improvement of the one-to-one machine translation by applying the methodology proposed in this paper and the significant high performance of the many-to-one machine translation were derived.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.453-461
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• 2022

This study designed a research model to analyze the relationship between organizational commitment and work performance for outsourcing workers. The path relationship was analyzed using the PLS-SEM of the sample collected through the survey. As a result of the study, organizational support perception had a direct effect on the work performance of outsourcing workers, and the company commitment and customer company commitment had a mediating effect. In addition, it was confirmed that the workers showed dual commitment to the company and the customer company, and the organizational commitment to both companies was complementary. It was also suggested that the outsourcing company's organizational support activities are more important for improving the work performance of workers.

• Journal of Industrial Convergence
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• v.21 no.4
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• pp.1-12
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• 2023

The era of digital transformation is rapidly emerging in industries and academia, including finance and logistics, and the consulting market for digital transformation is also growing. According to previous studies, the need for digital transformation is also mentioned in consulting institutions. In this process, the role of consultants should be changed according to the times, and customer relationship management and attitude toward customers are emphasized. However, consulting research has the point that research on this has not been studied in depth. Therefore, the purpose of this study is to develop an element of attitude focusing on consultant attitudes in the field of digital transformation. As a result of research using literature analysis and modified Delphi techniques, 'customer orientation', achievement orientation', professional dignity', 'maintenance of expertise', and 'ethics' were found to be key attitude factors. This study is meaningful in that consultant attitude elements in the digital transformation field were explored and developed by verifying content validity, and consultants in the digital transformation field can recognize the importance of attitude and use it as a basic tool for capacity improvement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.147-163
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• 2023

Recently, there has been an increase in the research and development of automated vehicles worldwide. Research focused on automated vehicles in Korea is steadily progressing as a national R&D project. Since automated driving technology comprises diverse technology fields, it is necessary to identify the current position of the research. In this study, we propose a methodology for analyzing research trends using the NTIS data. In addition, we review the effectiveness of the currently developed research trend methodology by deriving primary keywords and major topics using the proposed method. We expect that the methodology developed in this study can be applied to identify and analyze future automated vehicle research trends.

• The Journal of Engineering Geology
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• v.33 no.3
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• pp.489-502
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• 2023

Urban floods pose significant challenges to cities worldwide, driven by the interplay between urbanization and climate change. This review examines recent studies of urban floods to understand their causes, impacts, and potential mitigation strategies. Urbanization, with its increase in impermeable surfaces and altered drainage patterns, disrupts natural water flow, exacerbating surface runoff during intense rainfall events. The impacts of urban floods are far-reaching, affecting lives, infrastructure, the economy, and the environment. Loss of life, property damage, disruptions to critical services, and environmental consequences underscore the urgency of effective urban flood management. To mitigate urban floods, integrated flood management strategies are crucial. Sustainable urban planning, green infrastructure, and improved drainage systems play pivotal roles in reducing flood vulnerabilities. Early warning systems, emergency response planning, and community engagement are essential components of flood preparedness and resilience. Looking to the future, climate change projections indicate increased flood risks, necessitating resilience and adaptation measures. Advances in research, data collection, and modeling techniques will enable more accurate flood predictions, thus guiding decision-making. In conclusion, urban flooding demands urgent attention and comprehensive strategies to protect lives, infrastructure, and the economy.