• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.145-158
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• 2022

Although the rapid development of technology has led to a swift increase in the number of companies using social networking services (SNS), it will not be accurate to say that they have fully "utilized" the functionality of SNS simply by "using" these services. Therefore, this study aims to increase the convenience of using digital technology and help SNS users in extending the functionality of these services beyond their regular use and thus, revitalize the field by increasing the service providers' efficiency. In this study, the Facebook usage status of a graduate school newspaper from an undisclosed university in Seoul was analyzed from February to December, 2021 using the participant observation method. The results of the study revealed the following: First, it is necessary to diversify the subject and type of content to ensure a continuous supply of quality content; Second, there is a need to examine the user categories and characteristics by utilizing SNS functionalities such as, the target reports and insights, and based on this, supply content that meets the needs of the users; Third, to resolve the problem of low levels of user participation and an inactive Facebook account, it is necessary to mobilize new marketing tools like online events. The significance of this study is that it confronts the real problems faced by some companies that cannot keep pace with market changes in a digital environment, identifies failure factors, and proposes solutions to them.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.1
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• pp.25-38
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• 2022

Urban problems are constantly occurring around the world due to rapid industrialization and population decline. In particular, as the number of vacant houses is gradually increasing as the population decreases, it is necessary to prepare countermeasures. A plan to utilize vacant houses has emerged to restore the natural environment of the urban ecosystem where forest destruction, damage to habitats of wild animals and plants, and disconnection have occurred due to large-scale development. Through connectivity analysis, it is possible to understand the overall ecosystem flow based on the movement of species and predict the effect when vacant houses are converted into green spaces. Therefore, this study analyzed the green area network to confirm the possibility of greening of vacant houses neglected in Jeonju based on circuit theory. Using Circuitscape and Least-cost path, we tried to identify the connectivity of green areas and propose an ecological axis based on the analysis. In order to apply the resistance values required for analysis based on previous studies, the 2020 subdivision land cover data were integrated into the major classification evaluation items. When the eight forests in the target site were analyzed as the standard, the overall connectivity and connectivity between forests in the area were high, so it is judged that the existing green areas can perform various functions, such as species movement and provision of habitats. Based on the results of the connectivity analysis, the importance of vacant houses was calculated and the top 20 vacant houses were identified, and it was confirmed that the higher the ranking, the more positive the degree of landscape connectivity was when converted to green areas. In addition, it was confirmed that the results of analyzing the least-cost path based on the resistance values such as connectivity analysis and the existing conceptual map showed some differences when comparing the ecological axes in the form. As a result of checking the vacant houses corresponding to the relevant axis based on the width standards of the main and sub-green areas, a total of 30 vacant houses were included in the 200m width and 6 vacant houses in the 80m width. It is judged that the conversion of vacant houses to green space can contribute to biodiversity conservation as well as connectivity between habitats of species as it is coupled with improved green space connectivity. In addition, it is expected to help solve the problem of vacant houses in the future by showing the possibility of using vacant houses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.315-324
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• 2022

The conventional LESS (LEast-Squares Solution) is calculated under the assumption that there is no errors in independent variables. However, the coordinates of a point, either from traditional ground surveying such as slant distances, horizontal and/or vertical angles, or GNSS (Global Navigation Satellite System) positioning, cannot be determined independently (and the components are correlated each other). Therefore, the TLS (Total Least Squares) adjustment should be applied for all applications related to the coordinates. Many approaches were suggested in order to solve this problem, resulting in equivalent solutions except some restrictions. In this study, we calculated the normal vector of the 3D plane determined by the trace of the VLBI targets based on TLS within GHM (Gauss-Helmert Model). Another numerical test was conducted for the estimation of the Helmert transformation parameters. Since the errors in the horizontal components are very small compared to the radius of the circle, the final estimates are almost identical. However, the estimated variance components are significantly reduced as well as show a different characteristic depending on the target location. The Helmert transformation parameters are estimated more precisely compared to the conventional LESS case. Furthermore, the residuals can be predicted on both reference frames with much smaller magnitude (in absolute sense).

• Journal of the Korean GEO-environmental Society
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• v.22 no.12
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• pp.15-24
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• 2021

In Korea, which is mostly mountainous, the proportion of tunnel and underground space development are increasing. Although the ground is reinforced by applying the ground improvement method during underground space development, accidents still occur frequently in Korea. In the grouting method, a representative ground reinforcement method, the effect was judged by comparing the total amount of injection material with the amount of injection material used during the actual grouting construction. However, it is difficult to determine whether the ground reinforcement is properly performed during construction or within the target ground. In order to solve this problem, it is necessary to study a new method for quality control during or after construction by measuring electrical resistivity after performing grouting by mixing carbon fiber, which is a conductive material, and microcement, which is a grout material. In this study, as a basic study, a cement specimen mix ed with 0%, 3%, 5%, 7% of carbon fiber was prepared to evaluate the performance of the grout material mixed with carbon fiber, which is a conductive material. The prepared specimens were wet curing for 3 days, 7 days, and 28 days under 99% humidity, and then compression wave velocity and shear wave velocity were measured. As a result of the compression wave velocity and shear wave velocity measurement, it showed a tendency to increase with the increase in the compounding ratio of carbon fibers and the number of days of age, and it was confirmed that the elastic modulus and shear modulus, which are the stiffness of the material, also increased.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.70-81
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• 2019

The garment industry is one of the most labor-intensive manufacturing industries, with its sewing process relying almost entirely on manual labor. Its costs highly depend on the efficiency of this production line and thus is crucial to determine the production rate in real-time for line balancing. However, current production tracking methods are costly and make it difficult for many Small and Medium-sized Enterprises (SMEs) to implement them. As a result, their reliance on manual counting of finished products is both time consuming and prone to error, leading to high manufacturing costs and inefficiencies. In this paper, a production tracking system that uses the sewing machines' energy consumption data to track and count the total number of sewing tasks completed through Convolutional Neural Network (CNN) classifiers is proposed. This system was tested on two target sewing tasks, with a resulting maximum classification accuracy of 98.6%; all sewing tasks were detected. In the developing countries, the garment sewing industry is a very important industry, but the use of a lot of capital is very limited, such as applying expensive high technology to solve the above problem. Applied with the appropriate technology, this system is expected to be of great help to the garment industry in developing countries.

• Journal of Appropriate Technology
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• v.5 no.1
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• pp.18-24
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• 2019

Citarum river in West Java, Indonesia plays strategic roles for Jakarta metropolitan areas. Besides it provides major source of water supply such as domestics and drinking water including Jakarta, it also provides water for hundreds of industries through its cascade reservoirs. However, recently, Citarum river basin has been seriously suffering from water and groundwater pollution as well as the lowering-down of groundwater level due to the extreme use of water resources in dry season by domestic and industrial activities. This project objectives are design and installation of industrial wastewater treatment/recycle facilities to overcome the problem of water pollution and the lowering-down of groundwater level in Bandung. For these, cyclone type dissolved air flotation (DAF), CYFLOAT, was successfully installed as the appropriate technology for the target textile industry with 100 ton/day of capacity. The CYFLOAT system can remove the 96.8% of particulates, which are known as a critical factor to recycle the wastewater, within 40 min of residence time. Furthermore, The CYFLOAT system can reduce the operational cost and land use. The project was carried out in strong partnership with local institute including UNPAR, IBT, and PUSKIM for the sustainability of the technology to textile industry complex in Indonesia.