• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2018년도 공동국제학술발표회
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• pp.32-32
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• 2018

• Journal of Computing Science and Engineering
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• 제5권2호
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• pp.150-160
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• 2011

Automatic text classification has a long history and many studies have been conducted in this field. In particular, many machine learning algorithms and information retrieval techniques have been applied to text classification tasks. Even though much technical progress has been made in text classification, there is still room for improvement in text classification. In this paper, we will discuss remaining issues in improving text classification. In this paper, three improvement issues are presented including automatic training data generation, noisy data treatment and term weighting and indexing, and four actual studies and their empirical results for those issues are introduced. First, the semi-supervised learning technique is applied to text classification to efficiently create training data. For effective noisy data treatment, a noisy data reduction method and a robust text classifier from noisy data are developed as a solution. Finally, the term weighting and indexing technique is revised by reflecting the importance of sentences into term weight calculation using summarization techniques.

• 천문학논총
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• 제21권2호
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• pp.101-112
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• 2006

We developed a new program for automatic continuum normalization of Echelle spectrographic data. Using this algorithm, we have determined spectral continuum of almost BOES data. The first advantage of this algorithm is that we can save much time for continuum determination and normalization. The second advantage is that the result of this algorithm is very reliable for almost spectral type of spectrum. But this algorithm cannot be applied directly to the spectrum which has very strong and broad emission lines, for example Wolf-Rayet type spectrum. We implanted this algorithm to the program which was developed in the previous study. And we introduced more upgraded BOES data reduction program. This program has more convenient graphical user interface environment, so users can easily reduce BOES data. Lastly, we presented the result of study on line profile variation of magnetic Ap/Bp stars analyzed using this program.

• 천문학논총
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• 제20권1호
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• pp.97-107
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• 2005

We developed a data reduction program (RX) to process BOES data automatically. It processes a whole set of data taken during one night automatically - preprocessing, extraction to one-dimensional spectra and wavelength calibration. The execution is very fast and the performance looks pretty good. We described the performance of this program, comparing its procedure with that of IRAF. RX does not have functions for continuum normalization yet. We will develop those functions in the next works.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 춘계학술발표회 논문집
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• pp.479-484
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• 2003

The renewal of digital map takes much time and the manual process. LiDAR data allows reduction of time, automatic manner, and acquisition of the precise position. So it is used to renew the digital map of 1:5,000 scale. From the accuracy test results using aerial imagery and digitizing, renewal of digital map are feasible in automatic manner to some extent.

• 한국환경과학회지
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• 제21권3호
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• pp.383-390
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• 2012

For the cost-effective biological nitrogen removal (BNR) process whose characteristics of influent have low COD/N ratios, the automatic control system for the addition of external carbon based on oxidation-reduction potential (ORP) data in an anoxic reactor has been developed. In this study, it was carried out with a pilot-scale Bardenpho process which was consisted of anoxic 1, aerobic 1, aerobic 2, anoxic 2, aerobic 3 tank and clarifier. Firstly, the correlation coefficient ($R^2$) of the dosage of external carbon source and ORP value was about 0.97. Consequently, the automatic control system using ORP showed that the dosage of external carbon source was decreased by about 20% compared with a stable dosage of 75 mg/L based on the COD/N ratio of the anoxic influent.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.256-267
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• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• 한국CDE학회논문집
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• 제10권1호
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• pp.11-16
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• 2005

CAD/CAM system may have such advantages of cost reduction, production time shortening, and product quality improvement. But current advanced versions of CAD/CAM system for 3-D NC data generation are too much expensive to purchase and too difficult to make full use for small-scale manufacturers whose main products are of 2-D simple shapes. The objective of this paper is to introduce a cost-effective way to 2-D NC data generation with a widely spread CAD software. Using VISUAL LISP in the well-known AUTO-CAD, the contents and steps of an automatic NC data generation program are presented for 2-D machining of contours and pockets. To approve the usefulness of program, a test application to a real part is exhibited also.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.113-122
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• 2018

In this paper, IoT(Internet of things) technology, which is the core of the 4th industrial revolution, was applied to the study of reduction of consumption current. The IoT is a sensor that collects data, a sensor communication, a gateway that processes and stores the collected data. Data application of IoT technology is applied to smart home, smart city, healthcare, smart factory, etc. and it needs to be applied to various industrial fields. By sensing the location of the sensor device, the specific functions of the gateway and the platform are turned ON and OFF to reduce the consumption current of the equipment during the OFF period. When the sensor device accesses the gateway, the specific function of the gateway is turned ON and When the device is separated from the gateway, it senses the sensitivity of the wireless signal and automatically turns off the certain functions. As a resurt, it has reduced the consumption of current. In this paper, we propose a novel system for detecting the location of sensor devices by applying IoT technology. The system implementation is realized by software based, and defines the requirements for the implementation of the sensor device gateway. The gateway automatically detects the location, movement of the device and performs necessary functions. Finally verifies the automatic detection performance of the gateway according to the location of the device. It will contribute greatly to the development of the smart city and office.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.