• The Journal of Internal Korean Medicine
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• v.31 no.3
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• pp.612-619
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• 2010

Background : Low level laser therapy may be an effective method to protect tissue damage in acute stroke. Recently, series of clinical studies on the basis of animal experiments report efficacy and safety of laser therapy at early stages of acute stroke. Laser promotes mitochondrial ATP synthesis to reduce cell death by ischemic infarction. Objectives : To report possibility of non-invasive laser therapy for acute stroke by reviewing literature about its effectiveness, safety and mechanism. Methods : We searched papers using PubMed and 'Web of Knowledge' of Thomson ISI, using the keywords "Laser Therapy, Low-Level" and "Stroke". Limitations were last 10 years of publications and only in English. Search range includes RCTs, clinical reports, reviews and animal experiments. Papers not matched with inclusion criteria were excluded. Results : A total 223 studies were found, 203 excluded during title and extract screening. After scanning 20 papers the final 2 serial RCTs were selected and analyzed. They reported that transcranial laser therapy led in neuroprotective effect for acute stroke patents. Clinical evaluation factors showed favorable trend and initial safety. Conclusions : Non-invasive laser secured safety of clinical application. It may be a favorable choice for the acute stage of stroke.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.3
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• pp.413-426
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• 2020

This study researches the association between associative vocabulary and fashion image language in order to extract ideas that can be used as basic data for design ideas. Classic - avant-garde imaged language were chosen as theme words and each 70 questionnaires per a final image word were used for analysis. We obtained the following results by researching keywords that explained classic image words through a word cloud technique. It was found to have high central representation in the order of suit, classical, basic, music, Chanel, black and traditional. The core key words explaining avant-garde image language were found to have a central representation in the order of : peculiar, huge, Comme des Garçons, artistic, creative, deconstruction and individuality. We extracted the necessary idea dimensions needed for design ideas through associative network graph analysis. In the case of classical image language, it was named as the Mannish Item, Music, Modern Color, and the Traditional Classicality dimensions. In the case of avant-garde image language, it was named as the Key Image, Artistic Aura, Key Design and Designers dimensions.

• 대한상한금궤의학회지
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• v.5 no.1
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• pp.101-111
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• 2013

Objective : The purpose of this study is to find out the proper dosage of Ma-huang for clinical use of Shanghanlun(傷寒論) Methods : To achive the purpose of this study, web-databases(pubmed, naver, google) were searched with the keywords including 'dose of Ma-huang Ephedra Ephedrine','dosage of Ma-huang Ephedra Ephedrine', and 'water extract of Ma-huang Ephedra'. The searched 30 papers and articles were reviewed. Results & Conclusions : 1. Proper dosage of Ma-huang 1) Adult: up to 9-12g/day 2) Adolescent: up to 6g/day 3) Hypertension disorder patient: up to 6g/day 4) lactating women: up to 6g/day 5) child: <2 years 0.7-2.5g/day, $${\geq_-}2$$ years 2.6-6g/day (Different from body weight) Although administration of Ma-huang to hypertension disorder patient, lactating women, child is safe on the paper, It is not recommended to these people because Ma-huang is one of toxic herbs. 2. Dosage form of Ma-huang There's no safety paper about pill or powdered Ma-huang(麻黃). There's not pill or powdered prescription of Ma-hunag in Shanghanlun(傷寒論), either. So it is recommended to administrate water exetract of Ma-huang.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.19-26
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• 2018

This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.387-397
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• 2013

This paper proposes an approach to big data analytics for phon call data. The analytical models for phon call data is composed of the PVPF (Parallel Variable-length Phrase Finding) algorithm for identifying verbal phrases of natural language and the word count algorithm for measuring the usage frequency of keywords. In the proposed model, we identify words using the PVPF algorithm, and measure the usage frequency of the identified words using word count algorithm in MapReduce. The results can be interpreted from various viewpoints. We design and implement the model based HDFS (Hadoop Distributed File System), verify the proposed approach through a case study of phon call data. So we extract useful results through analysis of keyword correlation and usage frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.85-93
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• 2018

In recent years, there has been an increasing number of ways to distribute document-based malicious code using vulnerabilities in document files. Because document type malware is not an executable file itself, it is easy to bypass existing security programs, so research on a model to detect it is necessary. In this study, we extract main features from the document structure and the JavaScript contained in the stream object In addition, when JavaScript is inserted, keywords with high occurrence frequency in malicious code such as function name, reserved word and the readable string in the script are extracted. Then, we generate a machine learning model that can distinguish between normal and malicious. In order to make it difficult to bypass, we try to achieve good performance in a black box type algorithm. For an experiment, a large amount of documents compared to previous studies is analyzed. Experimental results show 98.9% detection rate from three different type algorithms. SVM, which is a black box type algorithm and makes obfuscation difficult, shows much higher performance than in previous studies.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.469-476
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• 2018

In order to jointly utilize research infrastructure and to build efficient construction, which are essential in science and technology research and development process. Although various classification methods have been introduced for efficient utilization of registered information, functions that can be directly utilized such as similar research infrastructure search is not yet been implemented due to limitations of collection information. In this study, we analyzed the similar search technique so far, presented the methodology for the calculation of similarity of research infrastructure, and analyzed the learning result. Study suggested that a technique can be use to extract meaningful keywords from information and analyze the similarity between the research infrastructure.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.