• Journal of Korean Medicine Rehabilitation
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• v.34 no.3
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• pp.27-41
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• 2024

Objectives This study aims to evaluate the efficacy and safety of electroacupuncture for rotator cuff disorder. Methods We searched nine online databases (PubMed, Embase, Cochrane Library, Chinese Academic Journals, Korean studies Information Service System, Rsearch Information Sharing Service, ScienceON, KMbase, Oriental Medicine Advanced Searching Integrated System) and two related journals up to April 2024 to identify randomized controlled trials that applied electroacupuncture to rotator cuff disorder. Selected studies were analyzed for risk of bias using the Cochrane risk of bias tool, and a meta-analysis was performed with RevMan version 5.4.1. Results Out of 175 studies, eleven randomized controlled trials were selected for final analysis. Most studies showed that electroacupuncture had effect on rotator cuff disorder. In the meta-analysis, electroacupuncture combined with rehabilitation treatment was significantly more effective than rehabilitation treatment alone in improving visual analog scale (p<0.00001). Almost studies did not report any side effects or adverse reactions to electroacupuncture treatment. Conclusions This systematic review suggests that electroacupuncture is an effective treatment for pain management in rotator cuff disorder. However, the lack of adverse effect reporting and a high risk of bias indicate the need for high-quality randomized controlled trials from various countries.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.105-122
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• 2024

Myanmar was successful in increasing its sustainable development index during the three years period from 2018 to 2020. However, the index began to decline since 2021. This study aims to analyze both the success factors and obstacles for sustainable development in Myanmar. Using the search terms 'Myanmar' and 'sustainability', online news items were collected from January 2018 to December 2023 and were examined through text network analysis. The study identified the following success factors that contribute to sustainable development in Myanmar: foreign investments, private companies' participation in the effort, human resource development projects, and the use of new and renewable energy. The inhibition factors for the development efforts identified were: government's coercive/restrictive policies, labor rights violations, and forest degradation. The findings of this study provide useful insights for understanding the current status of sustainability in Myanmar from academic and practical perspectives. The results also present benchmarking information for policy-makers in Myanmar and other similar developing countries that are searching for strategic directions in their sustainable development efforts.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.640-655
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• 2014

Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.

• The Journal of Korean Obstetrics and Gynecology
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• v.33 no.3
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• pp.20-39
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• 2020

Objectives: This study was performed to examine the domestic study trends on premature ovarian failure (POF) treated with Korean medicine. Methods: We investigated the studies on Korean medicine treatment for premature ovarian failure via searching 4 online databases. Results: 13 clinical studies were selected. All studies were the noncomparative studies, and mainly case reports. Studies were publicated from 2001 to 2019 and the number of studies with 1 subject was the greatest (76.9%). In accompanying symptoms, there was a study which did not report entire cases. In the 11 case reports (13 subjects), the most accompanying symptoms was hot flush (69.2%). In treatment of premature ovarian failure, the most used treatment was herbal medicine, which was used in all studies. Acupuncture treatment was used in 6 studies (46.2%) and moxibustion treatment was used in 8 studies (61.5%). The most common acupuncture point was 內關 (SP6) in acupuncture treatment, and was 關元 (CV4) in moxibustion treatment. The duration of treatment was between 1 month and 20 months, the average 7.76 months. Used outcome measurements were hormone test (84.6%), menstruation (76.9%), Visual analog sclale (VAS) (15.4%), ultrasonography (15.4%), Numeral rating scale (NRS), Body basal temperature (BBT) and Kupperman index (7.7%). In total 70 subjects, 13 subjects (18.6%) became pregnancy and 25 subjects (35.7%) had no effect. Follow up was reported in 6 studies, and the average duration of follow up was 141.63 days. Conclusions: Further clinical trials are needed to establish the evidence for Korean Medicine treatment for premature ovarian failure.

• Archives of design research
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• v.17 no.2
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• pp.289-300
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• 2004

Recently, web casting on the internet has been expanding in number and size. Web Casting is different from the conventional television broadcasting since it can transmit various types of information through multimedia and it also can include interaction between users and broadcasting server. User experience by interaction becomes more important. Therefore, it is needed to win over customers by supplying satisfied experience through creative and different services, especially when there are consider competitions among music casting sites. In order to know how to make customers satisfied, we have to try to inspect what real customers in the Web Sites are acting and thinking, namely 'customer experience'. The 'customer experience' means every experience what users are expecting, doing, thinking and feeling when they stay in the Web site and online. In this thesis, the evaluation Guideline for music casting websites is developed by understanding customer experience on the music casting websites. The process of understanding customer experience was implemented through user observation methods, such as web Diary, group interview, and questionnaire. As a result of the study, 67 evaluation Guidelines with weight rate in 6 categories which are searching music, listening music, music video, music broadcasting, music mailing and other contents are developed. It can be used to analyze strengths and weakness of music casting sites and to establish business strategy for the more satisfied customer experience.

• Journal of Physiology & Pathology in Korean Medicine
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• v.31 no.1
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• pp.20-24
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• 2017

The aim of this study is to investigate the effect of Okchun-hwan on Diabetes Mellitus. We searched articles from Oasis and Chinese Academic Journals(CAJ) online databases. Searching keywords were '玉泉丸', '옥천환'. Among the articles published from January 2000 to September 2016, The 79 articles were found. After review the title, abstract and original, The five articles were selected finally to rule out a completely different prescriptions and an animal test articles. Okchun-hwan is effective in improving the symptoms(dry mouth and throat, fatigue, eat easy to hunger, shortness of breath, sweating, palpitation, cardiac heat, insomnia, and tongue)of a patient with deficiency of both qi and yin diagnosis(氣陰兩虛證) on Diabetes Mellitus, as well as act on protection of endothelial cells and regulation of insulin sensitivity, insulin resistance that cause the diabetic chronic vascular complications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.446-457
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• 2016

This study analyzed the Korean nurses association news described nurses' work-family balance for fifteen years by drawing on the qualitative thematic approach. From September 14, 2012 to February 10, 2015, data were collected by searching news articles associated with nurses' work and family balance published from 2000 to 2014 in the Korean nurses' association news online. A total of 73 news articles were used for data analysis. Two themes and ten sub-themes were derived; under the first theme of the government policy on work-family balance, the 'policies of maternity leave', 'parenting support', 'working condition improvement', and 'family-friendly culture' were identified as the sub-themes. For the second theme of Korean nurses association activities on work-family balance, the 'activities for various working shifts', 'constructing 24 hours childcare facilities', 'supporting unemployed nursing workforce development', 'healthy birth and parenting environment', 'family-friendly work environment', and 'securing nurses for nursing shortage' were identified as sub-themes. The Korean nurses association news in terms of work-family balance providing a voice for nurses regarding the benefit of maternity leave, increasing awareness of gender equality from a gender perspective, and leading the public attention to it in depth.

• The Journal of Bigdata
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• v.5 no.1
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• pp.77-88
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• 2020

Recently, many researches habe been done to organize and analyze various complex relationships in real world, represented in the form of graphs. In particular, the computer field literature data system, such as DBLP, is a representative graph data in which can be composed of papers, their authors, and citation among papers. Becasue graph data is very complex in storage structure and expression, it is very difficult task to search, analysis, and visualize a large size of bibliographic big data. In this paper, we develop a graphic user interface tool, called EEUM, which visualizes bibliographic big data in the form of graphs. EEUM provides the features to browse bibliographic big data according to the connected graph structure by visually displaying graph data, and implements search, management and analysis of the bibliographc big data. It also shows that EEUM can be conveniently used to search, explore, and analyze by applying EEUM to the bibliographic graph big data provided by DBLP. Through EEUM, you can easily find influential authors or papers in every research fields, and conveniently use it as a search and analysis tool for complex bibliographc big data, such as giving you a glimpse of all the relationships between several authors and papers.