• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.7-13
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• 2019

This study aims to develop a FE Model to simulate dissimilar friction stir welding and to address its potential for fundamental analysis and practical applications. The FE model is based on Coupled Eulerian-Lagrangian approach. Multiphysics systems are calculated using explicit time integration algorithm, and heat generations by friction and inelastic heat conversion as well as heat transfer through the bottom surface are included. Using the developed model, friction stir welding between an Al6061T6 plate and an AZ61 plate were simulated. Three simulations are carried out varying the welding parameters. The model is capable of predicting the temperature and plastic strain fields and the distribution of void. The simulation results showed that temperature was generally greater in Mg plates and that, as a rotation speed increase, not the maximum temperature of Mg plate increased, but did the temperature of Al plate. In addition, the model could predict flash defects, however, the prediction of void near the welding tool was not satisfactory. Since the model includes the complex physics closely occurring during FSW, the model possibly analyze a lot of phenomena hard to discovered by experiments. However, practical applications may be limited due to huge simulation time.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.25-32
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 200 buildings that KEPCO headquarter and branch offices use. And K-BEMS system is composed of PV, battery, and hybrid PCS. KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. In this paper, the results of the project are shown. 9 modes of test results of K-BEMS system and are operational problems were analyzed. And measures to cure the trouble are also suggested. Batteries are operated more than 20% of SOC, and less than 20% of SOC battery protection switches are automatically shutting down the system and the system no longer respond to EMS, ending the supply of PV, and so therefore to continue the PV power supply it was turn out to be necessary that the EMS should automatically change its policy to change PV only supply mode automatically when the Battery Switch automatically operated. To operate the system continuously and automatically, it is necessary to modify the minimum operational SOC value, and in addition to that the EMS computer must remember the last shut-down SOC and Voltage which interrupted the system and add some margin to reflect the measurement error in the system.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.701-709
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• 2019

Recently, heart rate signal, which is one of biological signals, have been used in various fields related to healthcare. Conventionally, most of the proposed heart rate signal detection methods are contact type methods, but there is a problem of discomfort that the subject have to contact with the device. In order to solve the problem, detection study by non-contact method has been progressed recently. The detected heart rate signal can be used for finger vein liveness detection and various application using heart rate. In this paper, we propose a method to obtain heart rate signal by using finger vein imaging system. The proposed method detected the signal from the changes of the brightness value in the time domain of the infrared finger vein images and converted it into the frequency domain using the image processing algorithm. After the conversion, we removed the noise not related to the heart rate signal through band-pass filtering. In order to evaluate the accuracy of the signal, we analyzed the correlation with the signal obtained simultaneously with the finger vein acquisition device and contact type PPG sensor approved by KFDA. As a result, it was possible to confirm that the heart rate signal detected in non-contact method through the finger vein image coincides with the waveform of actual heart rate signal.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.463-470
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• 2022

Intravenous injection is widely used for patient treatment, including injection drugs, fluids, parenteral nutrition, and blood products, and is the most frequently performed invasive treatment for inpatients, including blood collection, peripheral catheter insertion, and other IV therapy, and more than 1 billion cases per year. Intravenous injection is one of the difficult procedures performed only by experienced nurses who have been trained in intravenous injection, and failure can lead to thrombosis and hematoma or nerve damage to the vein. Nurses who frequently perform intravenous injections may also make mistakes because it is not easy to detect veins due to factors such as obesity, skin color, and age. Accordingly, studies on auxiliary equipment capable of visualizing the venous structure of the back of the hand or arm have been published to reduce mistakes during intravenous injection. This paper is about the development of venous detection equipment that visualizes venous structure during intravenous injection, and the optimal combination was selected by comparing the brightness of acquired images according to the combination of near-infrared (NIR) LED and Filter with different wavelength bands. In addition, an image processing algorithm was derived to threshehold and making blood vessel part to green through grayscale conversion, histogram equilzation, and sharpening filters for clarity of vein images obtained through the implemented venous detection experimental module.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.119-129
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• 2024

The cutaneous lymphatic system in humans plays a crucial role in draining interstitial fluid and activating the immune system. Environmental factors, such as ultraviolet light and natural aging, often affect structural changes of such lymphatic vessels, causing skin dysfunction. However, some limitations still exist because of no alternatives to animal testing. To better understand the skin lymphatic system, a biomimetic microfluidic platform, skin-lymph-on-a-chip, was fabricated to develop a novel in vitro skin lymphatic model of humans and to investigate the molecular and physiological changes involved in lymphangiogenesis, the formation of lymphatic vessels. Briefly, the platform involved co-culturing differentiated primary normal human epidermal keratinocytes (NHEKs) and dermal lymphatic endothelial cells (HDLECs) in vitro. Based on our system, Lymphanax^TM, which is a condensed Panax ginseng root extract obtained through thermal conversion for 21 days, was applied to evaluate the lymphangiogenic effect, and the changes in molecular factors were analyzed using a deep-learning-based algorithm. Lymphanax^TM promoted healthy lymphangiogenesis in skin-lymphon-a-chip and indirectly affected HDELCs as its components rarely penetrated differentiated NHEKs in the chip. Overall, this study provides a new perspective on Lymphanax^TM and its effects using an innovative in vitro system.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.9-17
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• 2017

Purpose: The tissue description and electron density indicated by the Computed Tomography(CT) number (also known as Hounsfield Unit) in radiotherapy are important in ensuring the accuracy of CT-based computerized radiotherapy planning. The internal metal implants, however, not only reduce the accuracy of CT number but also introduce uncertainty into tissue description, leading to development of many clinical algorithms for reducing metal artifacts. The purpose of this study was, therefore, to investigate the accuracy and the clinical applicability by analyzing date from SMART MAR (GE) used in our institution. Methode: and material: For assessment of images, the original images were obtained after forming ROIs with identical volumes by using CIRS ED phantom and inserting rods of six tissues and then non-SMART MAR and SMART MAR images were obtained and compared in terms of CT number and SD value. For determination of the difference in dose by the changes in CT number due to metal artifacts, the original images were obtained by forming PTV at two sites of CIRS ED phantom CT images with Computerized Treatment Planning (CTP system), the identical treatment plans were established for non-SMART MAR and SMART MAR images by obtaining unilateral and bilateral titanium insertion images, and mean doses, Homogeneity Index(HI), and Conformity Index(CI) for both PTVs were compared. The absorbed doses at both sites were measured by calculating the dose conversion constant (cCy/nC) from ylinder acrylic phantom, 0.125cc ionchamber, and electrometer and obtaining non-SMART MAR and SMART MAR images from images resulting from insertions of unilateral and bilateral titanium rods, and compared with point doses from CTP. Result: The results of image assessment showed that the CT number of SMART MAR images compared to those of non-SMART MAR images were more close to those of original images, and the SD decreased more in SMART compared to non-SMART ones. The results of dose determinations showed that the mean doses, HI and CI of non-SMART MAR images compared to those of SMART MAR images were more close to those of original images, however the differences did not reach statistical significance. The results of absorbed dose measurement showed that the difference between actual absorbed dose and point dose on CTP in absorbed dose were 2.69 and 3.63 % in non-SMRT MAR images, however decreased to 0.56 and 0.68 %, respectively in SMART MAR images. Conclusion: The application of SMART MAR in CT images from patients with metal implants improved quality of images, being demonstrated by improvement in accuracy of CT number and decrease in SD, therefore it is considered that this method is useful in dose calculation and forming contour between tumor and normal tissues.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• The KIPS Transactions:PartC
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• v.9C no.2
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• pp.293-296
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• 2002

UP Link is One of the commercial product which converts HTML to HDML convertor in order to show the internet www contents in the mobile environments. When UP browser accesses HTML pages, the agent in the UP Link controls the converter to change the HTML to the HDML, I-Mode, which is developed by NTT-Docomo of Japan, has many contents through the long and stable commercial service. Micro Explorer, which is developed by Stinger project, also has many additional function. In this paper, we designed and implemented WAP convertor which can accept C-HTML contents and mHTML contents. C-HTML format by I-Mode is a subset of HTML format, mHTML format by ME is similar to C-HTML, So the content provides can easily develop C-HTML contents compared with WAP and the other case. Since C-HTML, mHTML and WML are used under the mobile environment, the limited transmission capacity of one page is also similar. In order to make a match table. After that, we apply conversion algorithm on it. If we can not find matched element, we arrange some tags which only can be supported by WML to display in the best shape. By the result, we can convert over 90% contents.