• Korean Parent-Child Health Journal
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• v.3 no.1
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• pp.35-45
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• 2000

The object of this research was to examine closely through the document about how much important location Sanhoopoong had and how it influences in the women's health. In addition to that, from the result of this research. I would like to contribute to korean nursing theory development and korean traditional nursing. Sanhoopoong is only the pain of women, come out to whom experienced delivery and abortion and especially it is an important cultural disease to Korean women. This is, once be taken ill, even reputed doctors cannot cure, so this make women suffer from pain, fall ill, decrease the quality of women's life, and it is appealed by women constantly, and is just made known by traditionally. The cause of Sanhoopoong has not been recognized medically The disease is not included in the disease classification. Thus, Sanhoopoong has no special pathological cause, and because it is the desease when be not taken care after childbirth, so this is able to be prevented and relieved sufficiently. But Sanhoopoong is the important health matter to dominate women's whole life's health. It has to be researched and lightened quikly. We have to help the women to get out of the difficulties from the pain of Sanhoopoong as soon as possible.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.777-784
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• 2009

Recently, the importance of geoCRM (geographic Customer Relationship Management) systems are growing rapidly. So, result of the recognition that their applications extend well beyond the traditional CRM systems with the advent of ubiquitous environment and generalized location based services. A majority of traditional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. On the other hand, the geoCRM systems can be built as providing the geographic-based functions about CRM, including spatial and market analyses and the visualization of customer data, etc. However, it lacks the specific model and implementation of the geoCRM systems, being caused by the incomprehension of needs, the absence of related standards and the difficulties of development, and so on. In this paper, we develop a new spatial analysis supporting system that to enhance productivity through the convenient use and management of spatial data. The functionality provided by our system includes a set of analysis functions based on data mining techniques which allow a user to affect powerful transformation on spatial data. Particularly, both spatial data and non-spatial attributes can be efficiently handled as an object through our OODBMS.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.406-410
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• 2005

A depth measurement system that consists of a single camera, a laser light source and a rotating mirror is investigated. The camera and the light source are fixed, facing the rotating mirror. The laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The camera detects the laser light location on object surfaces through the same mirror. The scan over the area to be measured is done by mirror rotation. Advantages are 1) the image of the light stripe remains sharp while that of the background becomes blurred because of the mirror rotation and 2) the only rotating part of this system is the mirror but the mirror angle is not involved in depth computation. This minimizes the imprecision caused by a possible inaccurate angle measurement. The detail arrangement and experimental results are reported.

• Smart Structures and Systems
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• v.17 no.1
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• pp.17-29
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• 2016

Ultrasonic tomography is a powerful tool for identifying defects within an object or structure. But practical application of ultrasonic tomography to solids is often limited by time consuming transducer coupling. Air-coupled ultrasonic measurements may eliminate the coupling problem and allow for more rapid data collection and tomographic image construction. This research aims to integrate recent developments in air-coupled ultrasonic measurements with current tomography reconstruction routines to improve testing capability. The goal is to identify low velocity inclusions (air-filled voids and notches) within solids using constructed velocity images. Finite element analysis is used to simulate the experiment in order to determine efficient data collection schemes. Comparable air-coupled ultrasonic signals are then collected through homogeneous and isotropic solid (PVC polymer) samples. Volumetric (void) and planar (notch) inclusions within the samples are identified in the constructed velocity tomograms for a variety of transducer configurations. Although there is some distortion of the inclusions, the experimentally obtained tomograms accurately indicate their size and location. Reconstruction error values, defined as misidentification of the inclusion size and position, were in the range of 1.5-1.7%. Part 2 of this paper set will describe the application of this imaging technique to concrete that contains inclusions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1689-1701
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• 2019

In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras. A recent report predicted that 80% of the Internet traffic will be video content by the year 2021. Several studies have been conducted on the detection of main video events to manage a large scale of videos. These studies show fairly good performance in certain genres. However, the methods used in previous studies have difficulty in detecting events of personal video. This is because the characteristics and genres of personal videos vary widely. In a research, we found that adding a dataset with the right perspective in the study improved performance. It has also been shown that performance improves depending on how you extract keyframes from the video. we selected frame segments that can represent video considering the characteristics of this personal video. In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module. The proposed method showed mAP 78.4% performance through experiments using LSVC data.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.51-55
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• 2017

In this paper, we designed a LBS-based smart platform for Lifelog service that can utilize the other's lifelog information. The conventional Lifelog service means that the system records the daily activities of the smart device user so the user can retrieve the early-recorded information later. The proposed Lifelog service platform uses the GPS/UFID location information and the various information extracted from the image as the lifelog data. Further, the proposed Lifelog platform using DB can provide the user with the Lifelog data recorded by the other service user. The system usually provide the other's Lifelog data within the 500m distance from the user and the range of distance can be adjustable. The proposed smart platform based on image recognition for Lifelog can acquire the image from the smart device directly and perform the various image recognition processing to produce the useful image attributes. And it can store the location information, image data, image attributes and the relevant web informations on the database that can be retrieved by the other use's request. The attributes stored and managed in the image information database consist of the followings: Object ID, the image type, the capture time and the image GPS coordinates. The image type attribute has the following values: the mountain, the sea, the street, the front of building, the inside of building and the portrait. The captured image can be classified into the above image type by the pattern matching image processing techniques and the user's direct selection as well. In case of the portrait-attribute, we can choose the multiple sub-attribute values from the shirt, pant, dress and accessory sub-attributes. Managing the Lifelog data in the database, the system can provide the user with the useful additional services like a path finding to the location of the other service user's Lifelog data and information.

• Korean Institute of Interior Design Journal
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• v.19 no.6
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• pp.30-37
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• 2010

The purpose of this study is to analyze the architectural publicness of Hague city hall by Richard Meier. The Hague city hall shows the systematic and comprehensive configuration of architectural publicness. Meier applied publicness to the city hall from concept and the configuration of space to object design. Architectural publicness of Hague city hall can be realized by providing large public space including atrium, programs about public events, linear space for street activities and the lower location of public facilities in the city hall without the limitation of weather, lime and size of space. Architectural publicness can obtain its real meaning when physical conditions meet the standard of public activities. Besides this, the Hague city hall case shows the desirable role model of city administrator and NGOs in that public events are organized by the independent body. The publicness of architecture is influenced by various factors including physical and political systems. The case study of the architectural publicness is very important in that it is very hard to generalize. In these basis, the study for architectural publicness can be complemented by concrete and positive case studies as well as existing humane and general studies.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.11
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• pp.30-36
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• 2010

Previous researches show that linearly integrated Hall sensor arrays (LIHaS) can detect cracks in the steel structure fast and effectively This paper proposes an algorithm that estimates the size and shape of cracks for the developed LIHaS. In most nondestructive testing (NDT), just crack existence and location are obtained by processing 1-dimensional data from the sensor that scans the object with relative speed in single direction. The proposed method is composed with two steps. The first step is constructing 2-dimensionally mapped data space by combining the converted position data from the time-based scan data with the position information of sensor arrays those are placed in the vertical direction to the scan direction. The second step is applying designed Laplacian filter and smoothing filter to estimate the size and shape of cracks. The experimental results of express train wheels show that the proposed algorithm is not only more reliable and accurate to detecting cracks but also effective to estimate the size and shape of cracks.