• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.243-243
• /
• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.1
• /
• pp.1-8
• /
• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• PNF and Movement
• /
• v.17 no.1
• /
• pp.11-18
• /
• 2019

Purpose: This study verifies the muscle activity around the amputation site during proprioceptive neuromuscular facilitation (PNF) pattern exercise for the upper extremities on the non-amputated part in upper extremity amputees and provides basic data on effective exercise around an amputation site. Methods: Manual resistance was applied to the PNF upper extremity pattern of the non-amputated part to generate muscle activity around the amputation site. The resistance was adjusted to an intensity that could cause maximal isometric contraction. The muscle activity of the amputation site and the non-amputated part was measured using a surface electromyogram for the upper trapezius, middle trapezius, infraspinatus, serratus anterior, and pectoralis major. Results: During the scapular exercise in the painless range, the amputated side showed significantly lower muscle activity and a lower muscle contraction ratio compared with the non-amputated side. During the PNF pattern exercise in the painless range, the amputated side showed lower muscle activity and a lower muscle contraction ratio compared with the non-amputated side. When the direct scapular exercise of the amputated side was compared with the PNF pattern exercise of the non-amputated side, their muscle contraction ratios were similar. Conclusion: This study confirmed the effectiveness of the PNF pattern exercise of the non-amputated part as a way to indirectly train the injured site with no pain for rehabilitation of patients with serious body injuries, such as amputation. It is necessary to develop effective exercise programs for the rehabilitation of the amputation site based on the results of this study.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.9
• /
• pp.781-789
• /
• 2021

In this paper, we quantitatively analyzed the required UAV(Unmanned Aerial Vehicle) failure rate of small UAV (≤25kg) based on the harm to human caused by UAV crash to fly over the populated area. We compute the number of harm to human when UAV falls to the ground at certain descent point by using population density, car traffic, building to land ratio, number of floors of building data of urban area and UAV descent trajectory modeling. Based on this, the maximum allowable UAV failure rate is calculated to satisfy the Target Level of Safety(TLS) for each UAV descent point. Then we can generate the failure rate requirement in the form of map. Finally, we divide UAV failure rate into few categories and analyze the possible flight area for each failure rate categories. Considering the Youngwol area, it is analyzed that the UAV failure rate of at least 10^-4 (failure/flight hour) is required to access the residential area.

• Journal of the Korean Society of Radiology
• /
• v.14 no.7
• /
• pp.937-946
• /
• 2020

The grating interferometer provides the differential phase contrast image of an phase object due to refraction of the wavefront by the object, and it needs to be converted to the phase contrast image. The line-integration process to obtain the phase contrast image from a differential phase contrast image accumulates noise and generate stripe artifacts. The stripe artifacts have noise and distortion increases to the integration direction in the line-integrated phase contrast image. In this study, we have configured and compared several machine learning methods to reduce the artifacts. The machine learning methods have been applied to simulated numerical phantoms as well as experimental data from the X-ray and neutron grating interferometer for comparison. As a result, the combination of the wavelet preprocessing and machine learning method (WCNN) has shown to be the most effective.

• The Korean Journal of Franchise Management
• /
• v.10 no.3
• /
• pp.13-23
• /
• 2019

Purpose - The recent franchise industry is rapidly developing. Some franchisees have a low barriers to entry and competition among companies is intensifying. In this dynamic competitive environment, companies need to focus on customer preferences, quality, and technical interfaces to gain competitive advantage. As a result, companies are required to measure the performance of service values in order to provide differentiated services from competitors. In the franchise industry, customer experience marketing of service values will enable companies to create new businesses. Franchise firms should explore a variety of services to increase service value and reduce failures. Research design, data, methodology - The questionnaire of this study was based on the previous research. Surveys were conducted on panels of online surveys. Surveys were conducted on the panel who had visited the restaurant franchise within the past month. The survey was conducted for about 7 days from February 13, 2019 to February 19, 2019. Total 300 samples, 293 were used in the analysis except for seven unfair questionnaires. Results - The findings of this study are as follows: Emotional, monetary, and reputation values have positive effects on cognitive and affective attitudes. Quality value and behavioral value did not effect cognitive attitude and affective attitude significantly. In addition, affective attitude has positive effect on loyalty, but cognitive attitude did not significant effect on loyalty. Conclusions - First, food-service franchise company should develop a service that enables customers to use the store conveniently. We need to develop a comfortable environment for our customers and provide intangible services. Second, food-service franchise company should provide a reasonable price service. Food-service franchise company needs to sell a high quality menu at a reasonable price to generate profits. Third, food-service franchise companies need to strategically respond to their reputation. In other words, food-service franchise company needs to constantly monitor the reputation of its customers and respond appropriately to market conditions. Fourth, food-service franchise company needs to develop a service method capable of emotional interaction with customers. Food-service franchise firms need to develop ongoing service methods and educate their staff.

• Journal of The Geomorphological Association of Korea
• /
• v.24 no.1
• /
• pp.77-91
• /
• 2017

Many debris landforms in the mountains of Korea have formed in the periglacial environment during the last glacial stage when the generation of sediments was active. Because these landforms are generally located on steep slopes and mostly covered by vegetation, however, it is difficult to observe and access them through field investigation. A scientific method is required to reduce the survey range before performing field investigation and to save time and cost. For this purpose, the use of remote sensing and GIS technologies is essential. This study has extracted the potential area of debris landform formation using a fuzzy set model as a mathematical data integration method. The first step was to obtain information about the location of debris landforms and their related factors. This information was verified through field observation and then used to build a database. In the second step, we conducted the fuzzy set modeling to generate a map, which classified the study area based on the possibility of debris formation. We then applied a cross-validation technique in order to evaluate the map. For a quantitative analysis, the calculated potential rate of debris formation was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). The prediction accuracy of the model was found to be 83.1%. We posit that the model is accurate and reliable enough to contribute to efficient field investigation and debris landform management.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.6
• /
• pp.15-24
• /
• 2006

Recently, the importance of the area efficient implementation of cryptographic algorithm for the portable device is increasing. Previous ARIA(Academy, Research Institute, Agency) implementation styles that usually concentrate upon speed, we not suitable for mobile devices in area and power aspects. Thus in this paper, we present an area efficient AR processor which use 32-bit architecture. Using new implementation technique of diffusion layer, the proposed processor has 11301 gates chip area. For 128-bit master key, the ARIA processor needs 87 clock cycles to generate initial round keys, n8 clock cycles to encrypt, and 256 clock cycles to decrypt a 128-bit block of data. Also the processor supports 192-bit and 256-bit master keys. These performances are 7% in area and 13% in speed improved results from previous cases.

• Journal of Broadcast Engineering
• /
• v.26 no.1
• /
• pp.61-69
• /
• 2021

Image outpainting is a very interesting problem in that it can continuously fill the outside of a given image by considering the context of the image. There are two main challenges in this work. The first is to maintain the spatial consistency of the content of the generated area and the original input. The second is to generate high quality large image with a small amount of adjacent information. Existing image outpainting methods have difficulties such as generating inconsistent, blurry, and repetitive pixels. However, thanks to the recent development of deep learning technology, deep learning-based algorithms that show high performance compared to existing traditional techniques have been introduced. Deep learning-based image outpainting has been actively researched with various networks proposed until now. In this paper, we would like to introduce the latest technology and trends in the field of outpainting. This study compared recent techniques by analyzing representative networks among deep learning-based outpainting algorithms and showed experimental results through various data sets and comparison methods.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.199-208
• /
• 2021

Automatic Post Editing(APE) is the study that automatically correcting errors included in the machine translated sentences. The goal of APE task is to generate error correcting models that improve translation quality, regardless of the translation system. For training these models, source sentence, machine translation, and post edit, which is manually edited by human translator, are utilized. Especially in the recent APE research, multilingual pretrained language models are being adopted, prior to the training by APE data. This study deals with multilingual pretrained language models adopted to the latest APE researches, and the specific application method for each APE study. Furthermore, based on the current research trend, we propose future research directions utilizing translation model or mBART model.