• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.199-204
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• 2021

Digital substation refers to a substation that digitizes functions and communication methods of power facilities such as monitoring, measuring, control, protection, and operation based on IEC 61850, an international standard for the purpose of intelligent power grids. Based on the intelligent operating system, efficient monitoring and control of power facilities is possible, and automatic recovery function and remote control are possible in the event of an accident, enabling rapid power failure recovery. With the development of digital technology and the expansion of the introduction of eco-friendly renewable energy and electric vehicles, the spread of direct current distribution systems is expected to expand. MVDC is a system that utilizes direct current lines with voltage levels and transmission capacities between HVDCs applied to conventional transmission systems and LVDCs from consumers. Converting existing lines in substations, where most power equipment is alternating current centric, to direct current lines will reduce transmission losses and ensure greater current capacity. The process bus of a digital substation is a communication network consisting of communication equipment such as Ethernet switches that connect installed devices between bay level and process level. For MVDC linkage to existing digital substations, the process level was divided into two buses: AC and DC, and a system that can be comprehensively managed in conjunction with diagnostic IEDs as well as surveillance and control was proposed.

• Journal of agriculture & life science
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• v.51 no.6
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• pp.137-144
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• 2017

A diverse of recommendation has been made for the structure and management of dairy cows, despite demanding research, the relationship between lactation number and various factors is yet to be established. The present study was aimed to investigate the covariance among lactation number, growth performance, calving interval, and milk production was considered to increase an efficiency of selection schemes and to manage more efficiently Holstein dairy cows that have been raised on small-scale family farms in Republic of Korea. For that purpose, the data were observed from 850 Holstein dairy cows, which a total of 3929 milking, since April 2016 - January 2017. We measured the body weight, height, age, calving interval, and milk production of the each dairy cow. Also, information about the date of lactation, calving interval, and milk production was recorded using an automatic milking system(AMS) with identification numbers. Milk production was calculated per udder quarter in the AMS. Our study results showed the increased average body weight(p>0.05) in 1, 2, 3, and $4^{th}$ lactating dairy cows and afterwards, we noticed the tendency on the average body weight(p<0.05) per lactation progressed. There was no significant difference noticed on height measurement of dairy cows. From the processing data of 850 Holstein dairy cows, the lactation number 1 and 7 had a greater calving interval with significantly lowered milk production, and the lactation number 2, 3, 4, 5, and 6 had significantly lowered the calving interval(p<0.05) with a greater milk production. From our study results, we evidenced that there is a significant relationship between the lactation number, growth performance, calving interval, and milk yield, and the maximum production of milk occurring in the $3^{rd}$ and $4^{th}$ lactation dairy cows. The achieved results from this study can be used by the small-scale farmers to encourage the structure and management of growth performance, calving interval, and milk yield in Holstein dairy cows in Korea.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.251-256
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• 2019

A capsule endoscopy is a medical device that can capture an entire digestive organ from the esophagus to the anus at one time. It produces a vast amount of images consisted of about 8~12 hours in length and more than 50,000 frames on a single examination. However, since the analysis of endoscopic images is performed manually by a medical imaging specialist, the automation requirements of the analysis are increasing to assist diagnosis of the disease in the image. Among them, this study focused on automatic detection of polyp images. A polyp is a protruding lesion that can be found in the gastrointestinal tract. In this paper, we propose a weighted-image generation method to enhance the polyp image learning by multi-scale analysis. It is a way to extract the suspicious region of the polyp through the multi-scale analysis and combine it with the original image to generate a weighted image, that can enhance the polyp image learning. We experimented with SVM and RF which is one of the machine learning methods for 452 pieces of collected data. The F1-score of detecting the polyp with only original images was 89.3%, but when combined with the weighted images generated by the proposed method, the F1-score was improved to about 93.1%.

• Journal of Biomedical Engineering Research
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• v.40 no.3
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• pp.105-115
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• 2019

Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.517-524
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• 2019

The overdrive hub clutch is attached to a 6-speed automatic transmission to reduce fuel consumption by using the additional power of the engine. This paper proposes a means to minimize the load and roll-over ratio on the punch during the piercing process for the overdrive hub clutch product. Die clearance, shear angle, and friction coefficient, which can affect the load and roll-over ratio of the punch during processing, were set as the design variables. Sensitivity analysis was also conducted to determine the influence of each design variable on the punch load and roll-over ratio. As a result, shear angle, friction coefficient and die clearance were found to be sensitive to load and roll-over ratio. The punch load and roll-over ratio were set as the objective function and the equation of each design variable and objective function was derives using the Response Surface Method. Finally, the optimal value of the design variables was derived using the Response Surface Method. Application of this model to finite element analysis resulted in 22.14% improvement in the roll-over ratio of the punch load and material.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.4
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• pp.483-491
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• 2021

The identification of regional collision risks in water areas is significant for the safety of navigation. This paper introduces a new method of collision risk assessment that incorporates a clustering method based on the distance factor - hierarchical clustering - and uses real-time data in case of several surrounding vessels, group methodology and preliminary assessment to classify vessels and evaluate the basis of collision risk evaluation (called HCAAP processing). The vessels are clustered using the hierarchical program to obtain clusters of encounter vessels and are combined with the preliminary assessment to filter relatively safe vessels. Subsequently, the distance at the closest point of approach (DCPA) and time to the closest point of approach (TCPA) between encounter vessels within each cluster are calculated to obtain the relation and comparison with the collision risk index (CRI). The mathematical relationship of CRI for each cluster of encounter vessels with DCPA and TCPA is constructed using a negative exponential function. Operators can easily evaluate the safety of all vessels navigating in the defined area using the calculated CRI. Therefore, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of life and property. To illustrate the effectiveness of the framework proposed, an experimental case study was conducted within the coastal waters of Mokpo, Korea. The results demonstrated that the framework was effective and efficient in detecting and ranking collision risk indexes between encounter vessels within each cluster, which allowed an automatic risk prioritization of encounter vessels for further investigation by operators.