• Journal of the Korean Institute of Rural Architecture
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• v.22 no.4
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• pp.43-50
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• 2020

The purpose of this study is to extract deficiency areas as basic data of policies and projects in the future Living SOC introduction and planning. In order to extract living SOC deficient areas, accessibility data for living SOC and density data for main users by facility were overlapped, focusing on the living SOC indicators presented in the National Urban Regeneration Basic Policy. According to the analysis of accessibility of the Iksan-si Living SOC, the gap between deficiency in urban and township areas was large in common with the accessibility of the village and local base units. As a result of overlapping life SOC accessibility data and density data analysis of the main users by facility, areas where accessibility is weak but not inhabited by the main users of each facility were extracted. It is meaningful that more accurate deficient areas can be extracted by simultaneously utilizing the density distribution of the main users, rather than simply accessing the facilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.126-131
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• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• The Journal of Korean Medicine
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• v.28 no.2 s.70
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• pp.1-12
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• 2007

Objective : Gagamchongmyung-tang is clinically one of the most popular prescriptions as an herbal medicine for the treatment of amnesia. In order to evaluate its neuroprotective effects on the ischemia-induced cognitive deficits caused by middle cerebral artery occlusion (MCAO), we examined its ability to reduce impairments of learning and memory of rats in the Morris water maze. Method and Result : Focal cerebral ischemia produced a decrease in acetylcholine transmission in the hippocampus, and deficits of learning and memory in the Morris water maze task. Treatment with two types of Gagamchongmyung-tang, methanol and water extracts, produced a substantial increase in acquisition in the Morris water maze. Treatment with methanol extract of Gagamchongmyung-tang increased the performance of the retention test in the Morris water maze. Consistent with behavioral data, immunohistochemical data showed that treatment with methanol extract, but not water extract, of Gagamchongmyung-tang significantly recovered reduction of AchE and ChAT reactivity in the hippocampal CAl area. Conclusion : These results demonstrated that methanol extract of Gagamchongmyung-tang has protective effects against ischemia-induced learning and memory impairments, and provided evidence of methanol extract of Gagamchongmyung-tang as a putative treatment for amnesia, vascular dementia, and longer memory.

• Korean Journal of Microbiology
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• v.54 no.4
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• pp.320-324
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• 2018

Chunkookjang, fermented soybean is rich in diverse oligopeptides which derived from cleavage of soybean proteins during fermentation. Microarray data containing differently expressed genes in breast cancer cells treated with fermented soybean extract and well known breast cancer metastasis markers were combined, and a new network was constructed. It is used to check interactions between the marker proteins and the differently expressed genes. Based on the network analysis, PLAU (plasminogen activator, urokinase, uPA) and ERBB2 (epidermal growth factor receptor 2) are chosen as possible metastasis genes. We treated breast cancer MCF7 cells with fermented soybean extract and measured expression levels of PLAU and ERBB2. Fermented soybean extract suppressed PLAU and ERBB2 expressions conspicuously. In the cancer cells treated with fermented soybean extracts, an inflammation marker, NO production was also reduced. It will be interesting to find specific peptides to suppress PLAU and ERBB2 expressions in human breast cancer cells.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.317-317
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• 2022

Seakso 5, a maize hybrid, was applied for variety in 2021 by Gangwon Agricultural Research and Extension Services in Korea. This variety was bred to produce a purple extract of com husk. It shows purple color in the husk and cob and contains abundant anthocyanins. In this study, to obtain basic data on purple com 'Seakso 5' husk extract (PCHE), a husk extract was prepared and the contents of vitamins, amino acids and fatty acids were analyzed. The Seakso 5 husks were extracted with 30% alcohol and concentrated, after adding dextrin to the concentrate, it was spray-dried to prepare an extract. The PCHE were analyzed for vitamins, amino acids, and fatty acids by referring to the Food Code. As a result, three types of vitamins were detected, and their contents were vitamin B₁ 0.14 mg/100g, vitamin B₂ 2.30 mg/100g, and vitamin C 11.32 mg/100g. A total of 15 amino acids were detected in the PCHE, including aspartic acid and glutamic acid, and the content of aspartic acid was the highest at 3.02 mg/g. Simultaneous analysis of fatty acids Among 37 types, saturated fatty acids were palmitic acid (C16:0), stearic acid (C18:0), monounsaturated fatty acids were oleic acid (C18:1(n-9)), polyunsaturated fatty acids were linoleic acid(C18:2(n-6)c) and a-Linolenic Acid (C18:3(n-3)) were detected. Among the detected fatty acids, the content of palmitic acid was the highest at 40.75 mg/100g. In the future, These analysis results will be used as reference data for temporary food ingredient applications by the Ministry of Food and Drug Safety.

• Earthquakes and Structures
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• v.15 no.3
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• pp.263-273
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• 2018

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating. In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.472-473
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• 2013

The growth in number and efficiency of smart devices such as GPS enabled smart phones and PDAs present an unparalleled opportunity for diverse areas of life. However extraction of GPS traces for provision of services demand a huge storage space as well as computation overhead. This is a challenging task especially for the applications which provide runtime services. In this paper we provide a simplified model to extract GPS traces of moving objects at runtime. Road segment partitioning and measure of deviation in angle of trajectory path is incorporated to identify the significant data points. The number of these data points is minimized by our proposed approach in an efficient manner to overwhelm the storage and computation overhead. Further, the competent reconstruction of complete itinerary based on gathered data, is also ensured by proposed method.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.521-526
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• 2017

Data format of Big data is diverse and vast, and its generation speed is very fast, requiring new management and analysis methods, not traditional data processing methods. Textual mining techniques can be used to extract useful information from unstructured text written in human language in online documents on social networks. Identifying trends in the message of politics, economy, and culture left behind in social media is a factor in understanding what topics they are interested in. In this study, text mining was performed on online news related to a given keyword using topic - oriented analysis technique. We use Latent Dirichiet Allocation (LDA) to extract information from web documents and analyze which subjects are interested in a given keyword, and which topics are related to which core values are related.