• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.169-180
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• 2018

White light-emitting diodes (LEDs) are more economical than fluorescent lights, and provide high brightness, a high lifetime expectancy, and greater durability. As LEDs are closely connected with people's daily lives, dimming control of LED is an important component in providing energy savings and improving quality of life. In visible light communications systems using these LEDs, multiple input multiple output (MIMO) technology has attracted a lot of attention, in that it can attain the channel capacity in proportion to the number of antennas. This paper analyzes the power performance of three kinds of modulation in visible light communications (VLC) systems applied space-time block code (STBC) techniques. The modulation schemes are return-to-zero on-off keying (RZ-OOK), variable pulse position modulation (VPPM), and overlapping pulse position modulation (OPPM), and dimming control was applied. The power requirements and power consumption were used as metrics to compare the power efficiency in $2{\times}2$ STBC-VLC environments under the three kinds of modulation. We confirm that dimming control affects the communications performance of each modulation scheme. VPPM showed greater consumption among the three modulations, and OPPM showed energy savings comparable to VPPM.

• International Area Studies Review
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• v.22 no.2
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• pp.61-91
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• 2018

Credit Guarantee scheme is one of the most effective tools for the small business policy. The performance analysis on domestic institution level is relevant in terms of various factors of assisting tools factor. This study measured comparative global efficiency by DEA model and Super-efficiency model among 70 credit guarantee institutions in Japan, Taiwan, and Korea who are operating the schemes. At the result of the analysis, Korean credit guarantee institutions are comparatively efficient than Japanese institutions, and the DMU shows moderate in operation efficiency. The Super-efficiency ranked by Hiroshima, Taiwan SMEG, Pusan, Chiba, Shizuoka, Ulsan, and KOTEC. Most of the Credit Guarantee Institutions showed increasing returns to scale, and it indicates increasing input strategy. The statistical difference of efficiency level in Japan and Korea shows very meaning numbers. This research suggest that (1)Periodical Analysis are needed on Japanese Schemes, (2)The analysis on the impact of credit guarantee scale to the national economy and SME policy, (3) Analysis on the conclusive factors of the efficiency, (4)The policy direction has to be made by inefficient factor analysis, (5) The measurement tools of efficiency of the schemes in various aspects.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.468-475
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• 2019

An end-line flame arrester allows free venting in combination with flame protection for vertical vent applications. End-line flame arresters are employed in various fields, especially in shipping. In flame arresters, springs are essential parts because the spring load and the spring's elasticity determine the hood opening moment. In addition, the spring has to work under a high-temperature condition because of the burning gas flame. Therefore, it is necessary to analyze the mechanical load and elasticity of the spring when the flame starts to appear. Based on simulations of the working process of a specific end-line flame arrester, a thermal and structural analysis of the spring is performed. A three-dimensional model of a burned spring is built using computational fluid dynamics (CFD) simulation. Results of the CFD analysis are input into a finite element method simulation to analyze the spring structure. The research team focused on three cases of spring loads: 43, 93, and 56 kg, correspondingly, at 150 mm of spring deflection. Consequently, the spring load was reduced by 10 kg after 5 min under a $1,000^{\circ}C$ heat condition. The simulation results can be used to predict and estimate the spring's load and elasticity at the burning time variation. Moreover, the obtained outcome can provide the industry with references to optimize the design of the spring as well as that of the flame arrester.

• Smart Media Journal
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• v.8 no.3
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• pp.23-30
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• 2019

With the popularization of the Internet and smartphone uses, people in the modern era are living in a multi-channel environment in which they access the information system freely through various methods and media. In the process of utilizing such services, users must authenticate themselves, the typical of which is ID & password authentication. It is considered the most convenient method as it can be authenticated only through the keyboard after remembering its own credentials. On the other hand, modern web services only allow passwords to be set with high complexity by different combinations. Passwords consisting of these complex strings also increase proportionally, since the more services users want to use, the more user authentication information they need to remember is recommended periodically to prevent personal information leakage. It is difficult for the blind, the disabled, or the elderly to remember the authentication information of users with such high entropy values and to use it through keyboard input. Therefore, this paper proposes a user authentication method using Google Assistant, MFCC and DTW algorithms and speaker authentication to provide the handicapped users with an easy user authentication method in the login process.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Journal of Korean Society on Water Environment
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• v.35 no.3
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• pp.224-233
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• 2019

Nitrogen components in liquid manure can reduce safety and quality of environment harmfully. To minimize the environmental risks of manure, understanding fate of manure in environment is necessary. This study aimed at investigating applicability of a simplified Level III fugacity model for simulating $NH_3-N$ component to analyze environmental fate and transport of $NH_3-N$ in liquid manure and to provide basis for improving management of N in the liquid manure system and for minimizing the environmental impacts of N. The model simulation conducted for four environmental compartments (air, water, soil, and rice plants) during rice-cropping to trace $NH_3-N$ component and provided applicability of the Level III fugacity model in studying the environmental fate of $NH_3-N$ in manure. Most of $NH_3-N$ was found in water body and in rice plants depending upon the physicochemical properties and proper removal processes. For more precise model results, the model is needed to modify with the detailed removal processes in each compartment and to collect proper and accurate information for input parameters. Further study should be about simulations of various N-typed fertilizers to compare with the liquid manure based on a modified and relatively simplified Level III fugacity model.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.