• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.55.5-56
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• 2021

The optical design of the Linear-Astigmatism-Free Three-Mirror-System (LAF-TMS) for KASI-Deep Rolling Imaging Fast-optics Telescope(K-DRIFT) is presented. LAF-TMS is an all-reflective imaging system consists of three freeform mirrors. Due to its well-corrected aberrations and obstruction-free clear aperture, the LAF-TMS provides a wide field of view with very low scattered lights.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.457-464
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• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• The Journal of Fisheries Business Administration
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• v.42 no.1
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• pp.1-18
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• 2011

This paper analyze a description of Korean fleet using bottom gears on the high seas. The need for this study arises from international moves to address the effects of fishing with bottom gears on vulnerable marine ecosystems (VMEs) and in view of a communication on the Korean policy in respect of this. There is growing concern over the impact of fishing using gears that come into contact with the seabed (bottom gears), in particular in deep-sea areas where vulnerable marine ecosystems including seamounts, hydrothermal vents and cold water corals are located. Korea is an important stakeholder in high seas bottom gear fisheries. For the past eight years, the issue of protecting biodiversity in the deep-sea in areas beyond national jurisdiction has been extensively debated by the United Nations General Assembly (UNGA) and in other international fora. As a result of the report and a review by the UNGA of the effectiveness of the measures called for in resolution 59/25, the UN General Assembly called for a series of specific actions to be taken by States and RFMOs in UNGA resolutions 61/105 in 2006 and 64/72 in 2009 adopted by consensus. Korea attaches great importance to the protection of marine ecosystems and has made active efforts to implement the UNGA Resolution 61/105 in areas where there is a regional fisheries management organization, a process of establishing such organization or no such multilateral regime. For the effective implementation of the UNGA Resolution 61/105 and 64/72, Korea views that the development of support tools and, most importantly, the development of a global database on VMEs are urgently needed because many countries lack the ability to identify VMEs and to assess whether individual bottom fishing activities would have significant adverse impacts on their own.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.248-259
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• 2010

In the case of relatively good ground and construction condition in the deep excavation for the construction of subway, railway, building etc., flexible earth retaining systems are often used in an economical point of view. It is generally known that the mechanism of behavior in the flexible earth retaining system is relatively more complicated than the rigid earth retaining system. Moreover in the case of long span strut supporting system the analysis of strut axial force change becomes more difficult when the differences of ground condition and excavation work progress on both sides of excavation section are added. When deeper excavation than the specification or installation delay of supporting system is done or change of ground condition is faced due to the construction conditions during construction process, lots of axial force can be induced in some struts and that can threaten the safety of construction. This paper introduces one example of long span deep excavation where struts and rock bolts were used as a supporting system with flexible wall structure. The characteristics of ground deformation and strut axial force change, the measured data obtained during construction process, were analysed, the effects of relatively deeper excavation than the specification on one excavation side and rapid drawdown of ground water level on the other excavation side were deeply investigated from the viewpoint of mutual influences between ground deformations of both excavation sides and strut axial force changes. The effort of this article aims to improve and develop the technique of design and construction in the coming projects having similar ground condition and supporting method.

• Journal of the Korean Arthroscopy Society
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• v.15 no.2
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• pp.117-120
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• 2011

For the treatment of deep extensive abscess, the incision and drainage is necessary. But in order to make a thorough incisional drainage we can't avoid the skin incision enlarged. Even if the incision is enlarged, it is often impossible to obtain a satisfactory view of operative field. Also, the additional damage to surrounding normal tissue during operation and the scars made by large incision may be problematic. To solve these problems, we performed the endoscopic treatment for extensive deep abscesses occurred in hematoma after rupture of gastrocnemius muscle and obtained the satisfactory results. It has not been previously described, so we report it with a review of the literature.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.42.1-42.1
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• 2018

Deep Ecliptic Patrol of the Southern Sky (DEEP-South) observation is being made during the off-season for exoplanet survey, using Korea Microlensing Telescope Network (KMTNet). An optimal combination of its prime focus optics and the 0.3 billion pixel CCD provides a four square degrees field of view with 0.4 arcsec/pixel plate scale which is also best suited for small body studies. Normal operation of KMTNet started in October 2015, and a significant portion of the allocated telescope time for DEEP-South is dedicated to targeted observation, Opposition Census (OC), of near-Earth asteroids for physical and taxonomic characterization. This is effectively achieved through multiband, time series photometry using Johnson-Cousins BVRI filters. Uninterrupted monitoring of the southern sky with KMTNet is optimized for spin characterization of a broad spectrum of asteroids ranging from the near-Earth space to the main-belt, including binaries, asteroids with satellites, slow/fast- and non-principal axis-rotators, and thus is expected to facilitate the debiasing of previously reported lightcurve observations. Our software subsystem consists of an automated observation scheduler, a pipelined data processing system for differential photometry, and an easy-to-use lightcurve analysis toolkit. Lightcurves, spin periods and provisional determination of class of asteroids to which the lightcurve belongs will be presented, using the dataset from first year operation of KMTNet. Our new taxonomic classification scheme for asteroids will also be summarized.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.308-319
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• 2009

In the case of relatively good ground and construction condition in the deep excavation for the construction of subway, railway, building etc., flexible earth retaining systems are often used in an economical point of view. It is generally known that the mechanism of behavior in the flexible earth retaining system is relatively more complicated than the rigid earth retaining system. Moreover in the case of long span strut supporting system the analysis of strut axial force change becomes more difficult when the differences of ground condition and excavation work progress on both sides of excavation section are added. When deeper excavation than the specification or installation delay of supporting system is done or change of ground condition is faced due to the construction conditions during construction process, lots of axial force can be induced in some struts and that can threaten the safety of construction. This paper introduces two examples of long span deep excavation where struts and rock bolts were used as a supporting system with flexible wall structure. And the sections of two examples are 50 meters apart in one construction site, they have almost similar design and construction conditions. The characteristics of ground deformation and strut axial force change were analysed, the similarity and difference between measurement results of tow examples were compared and investigated. The effort of this article aims to improve and develop the technique of design and construction in the coming projects having similar ground condition and supporting method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.