• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.25-40
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• 2022

This study is about how the quality of M2M big data that provides real-time information affects users. Recently, there are many difficulties in acquiring and managing data because data types such as variety, data volume, and data velocity are changing rapidly and diversified. This not only leads to a decrease in data quality but also it can give a negative impact when making decisions using data. Generally, the quality of data is defined as 'suitability for use', which means that data quality must meet the expectations of user needs. Therefore, data providers need activities to improve data quality for this purpose, and the key is to identify data quality dimensions in each field where data is used and provide data suitable for the level of user needs. In this study, the relationship between the quality area of real-time M2M data used in the traffic information system and user satisfaction was analyzed. Research models and hypotheses were established to analyze the effects between variables related to M2M big data. In order to test the hypothesis, a causal relationship between the major factors was identified by conducting a survey and analyzing the data users.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.4
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• pp.243-248
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• 2022

Weight optimization was performed for a rotorcraft shaft system using one-dimensional Euler-Bernoulli beam elements. Torsion, shaft support stiffness such as bearings, flange mass are all considered. To guarantee structural dynamic stability, eigenvalue analysis was performed to avoid critical speed and tooth mesh excitation form the gearbox. The weight optimization was performed by adjusting the thickness and radius while the length of the shaft was fixed, and the optimization process was divided into two stages. In the first, the weight is optimized with the torsional strength constraint. In the second, the difference between the primary mode of shaft and the critical speed is maximized so that the primary mode of the shaft can avoid the critical speed while the constraint on the torsional strength of the shaft is satisfied according to the standard for shaft system stability (AMC P 706-201, 1974). The proposed method was verified by comparing the results of the optimal design using the given one-dimensional beam elements with the stress results of the 3D finite element and the actual manufactured shaft.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.53-60
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• 2022

YouTube realistically shows fake news and biased content based on facts that have not been verified due to low entry barriers and ambiguity in video regulation standards. Therefore, this study aims to analyze the influence of the media and YouTube on individual behavior and their relationship. Data from YouTube and Twitter are randomly imported with selenium, beautiful soup, and Twitter APIs to classify the 31 most frequently mentioned keywords. Based on 31 keywords classified, data were collected from YouTube, Twitter, and Naver News, and positive, negative, and neutral emotions were classified and quantified with NLTK's Natural Language Toolkit (NLTK) Vader model and used as analysis data. As a result of analyzing the correlation of data, it was confirmed that the higher the negative value of news, the more positive content on YouTube, and the positive index of YouTube content is proportional to the positive and negative values on Twitter. As a result of this study, YouTube is not consistent with the emotion index shown in the news due to its secondary processing and affected characteristics. In other words, processed YouTube content intuitively affects Twitter's positive and negative figures, which are channels of communication. The results of this study analyzed that YouTube plays a role in assisting individual discrimination in the current situation where accurate judgment of information has become difficult due to the emergence of yellow media that stimulates people's interests and instincts.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.971-978
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• 2022

As a marketing method in a non-face-to-face society, the purpose of this study is to test how AR experience affects purchase intention in the process of consumers recognizing product information to purchase products and to secure the basis for the effectiveness of developing and introducing augmented reality functions in future product brand applications. Literary research methods and empirical research methods were used to verify the research purpose, and to measure this, an application of domestic tableware brand 'Odense', which implements augmented reality functions, was produced and used as an experimental tool. Also, a direct causal relationship was attempted by constituting a questionnaire by deriving a measurement scale for perceived usefulness, perceived ease, perceived pleasure, and purchase, which are factors of technology acceptance theory (TAM), and empirical analysis was conducted using the SPSS 25.0 statistical package to achieve the purpose of the study. As a result of the study, significant results were derived from all factors in the effect of perceived usefulness, ease, and pleasure on purchase intention, and several significant differences were found among factors according to gender, age, and internet shopping usage time in general characteristics. In conclusion, the user experience of the medium in which the augmented reality function is introduced in the information recognition stage of the product has a positive effect on purchase compared to the user experience of existing applications.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.529-541
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• 2023

Due to the growing concern over forecasting extreme weather events such as droughts caused by climate change, there has been a rising interest in seasonal meteorological forecasts that offer ensemble predictions for the upcoming seven months. Nonetheless, limited research has been conducted in South Korea, particularly in assessing their effectiveness at the catchment-scale. In this study, we assessed the accuracy of ECMWF's seasonal forecasts (including precipitation, temperature, and evapotranspiration) for the period of 2011 to 2020. We focused on 12 multi-purpose reservoir catchments and compared the forecasts to climatology data. Continuous Ranked Probability Skill Score method is adopted to assess the forecast skill, and the linear scaling method was applied to evaluate its impact. The results showed that while the seasonal meteorological forecasts have similar skill to climatology for one month ahead, the skill decreased significantly as the forecast lead time increased. Compared to the climatology, better results were obtained in the Wet season than the Dry season. In particular, during the Wet seasons of the dry years (2015, 2017), the seasonal meteorological forecasts showed the highest skill for all lead times.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.1
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• pp.27-41
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• 2023

Submerged floating tunnels must be connected to the ground to connect continents. The displacement imbalance at the shore connection between the underground bored tunnel and submerged floating tunnel can cause stress concentration, accompanying a fracture at the shore connection. The elastic joint has been proposed as a method to relive the stress concentration, however, the effect of the elastic joints on the dynamic behavior should be evaluated. In this study, the submerged floating tunnel and shore connection under dynamic load conditions were simulated through numerical analysis using a numerical model verified through a small-scaled physical model test. The resonant frequency was considered as a dynamic behavioral characteristic of the tunnel under the impact load, and it was confirmed that the stiffness of the elastic joint and the resonant frequency exhibit a power function relationship. When the shore connection is designed with a soft joint, the resonant frequency of the tunnel is reduced, which not only increases the risk of resonance in the marine environment where a dynamic load of low frequency is applied, but also greatly increases the maximum velocity of tunnel when resonance occurs.