• Title/Summary/Keyword: various processing methods

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Automatic Processing Techniques of Rotorcraft Flight Data Using Data Mining (회전익항공기 운동모델 개발을 위한 데이터마이닝을 이용한 비행데이터 자동 처리 기법)

  • Oh, Hyeju;Jo, Sungbeom;Choi, Keeyoung;Roh, Eun-Jung;Kang, Byung-Ryong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.46 no.10
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    • pp.823-832
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    • 2018
  • In general, the fidelity of the aircraft dynamic model is verified by comparison with the flight test results of the target aircraft. Therefore, the reference flight data for performance comparisons must be extracted. This process requires a lot of time and manpower to extract useful data from the vast quantity of flight test data containing various noise for comparing fidelity. In particular, processing of flight data is complex because rotorcraft have high non-linearity characteristics such as coupling and wake interference effect and perform various maneuvers such as hover and backward flight. This study defines flight data processing criteria for rotorcraft and provides procedures and methods for automated processing of static and dynamic flight data using data mining techniques. Finally, the methods presented are validated using flight data.

A Method of Risk Assessment for Multi-Factor Authentication

  • Kim, Jae-Jung;Hong, Seng-Phil
    • Journal of Information Processing Systems
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    • v.7 no.1
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    • pp.187-198
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    • 2011
  • User authentication refers to user identification based on something a user knows, something a user has, something a user is or something the user does; it can also take place based on a combination of two or more of such factors. With the increasingly diverse risks in online environments, user authentication methods are also becoming more diversified. This research analyzes user authentication methods being used in various online environments, such as web portals, electronic transactions, financial services and e-government, to identify the characteristics and issues of such authentication methods in order to present a user authentication level system model suitable for different online services. The results of our method are confirmed through a risk assessment and we verify its safety using the testing method presented in OWASP and NIST SP800-63.

A Study on Edge Detection using Local Mask in AWGN Environments (AWGN 환경에서 국부 마스크를 이용한 에지 검출에 관한 연구)

  • Lee, Chang-Young;Hwang, Yeong-Yeun;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2014.05a
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    • pp.801-803
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    • 2014
  • In the modern society, image processing is utilized in various fields. Edge detection used for image processing as such is essential for most of the applications. Accordingly, there are studies conducted both in and out of Korea in order to detect edge. Representative edge detection methods include Sobel, Prewitt and Roberts. However, these methods are rather limited when it comes to the edge detection characteristics when used for the image with damaged AWGN(additive white Gaussian noise). Thus, this paper presented edge detection method utilizing local mask in order to overcome the shortcomings of the existing methods.

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Discovering Community Interests Approach to Topic Model with Time Factor and Clustering Methods

  • Ho, Thanh;Thanh, Tran Duy
    • Journal of Information Processing Systems
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    • v.17 no.1
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    • pp.163-177
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    • 2021
  • Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

Development of the KnowledgeMatrix as an Informetric Analysis System (계량정보분석시스템으로서의 KnowledgeMatrix 개발)

  • Lee, Bang-Rae;Yeo, Woon-Dong;Lee, June-Young;Lee, Chang-Hoan;Kwon, Oh-Jin;Moon, Yeong-Ho
    • The Journal of the Korea Contents Association
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    • v.8 no.1
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    • pp.68-74
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    • 2008
  • Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix's main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.

Image processing of brush grinding system (화상처리를 이용한 브러시 연삭공구의 인식)

  • 신관수;유송민
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2002.10a
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    • pp.111-116
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    • 2002
  • In this study, a vision system with image processing method have been introduced to find the deflection of brush filaments. Several preprocessing methods including Scale-space filter with various threshold levels have been applied. In order to evaluate the deflection of the filaments, deformed filaments have been assessed using the processed profile

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Comparison of Document Clustering Performance Using Various Dimension Reduction Methods (다양한 차원 축소 기법을 적용한 문서 군집화 성능 비교)

  • Cho, Heeryon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2018.05a
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    • pp.437-438
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    • 2018
  • 문서 군집화 성능을 높이기 위한 한 방법으로 차원 축소를 적용한 문서 벡터로 군집화를 실시하는 방법이 있다. 본 발표에서는 특이값 분해(SVD), 커널 주성분 분석(Kernel PCA), Doc2Vec 등의 차원 축소 기법을, K-평균 군집화(K-means clustering), 계층적 병합 군집화(hierarchical agglomerative clustering), 스펙트럼 군집화(spectral clustering)에 적용하고, 그 성능을 비교해 본다.

Popular Object detection algorithms in deep learning (딥러닝을 이용한 객체 검출 알고리즘)

  • Kang, Dongyeon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2019.05a
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    • pp.427-430
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    • 2019
  • Object detection is applied in various field. Autonomous driving, surveillance, OCR(optical character recognition) and aerial image etc. We will look at the algorithms that are using to object detect. These algorithms are divided into two methods. The one is R-CNN algorithms [2], [5], [6] which based on region proposal. The other is YOLO [7] and SSD [8] which are one stage object detector based on regression/classification.

Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection

  • Jae-Yong Baek;Dae-Hyeon Park;Hyuk-Jin Shin;Yong-Sang Yoo;Deok-Woong Kim;Du-Hwan Hur;SeungHwan Bae;Jun-Ho Cheon;Seung-Hwan Bae
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.7
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    • pp.41-51
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    • 2024
  • In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

Effects of Various Thawing Methods on the Quality Characteristics of Frozen Beef

  • Kim, Young Boong;Jeong, Ji Yun;Ku, Su Kyung;Kim, Eun Mi;Park, Kee Jae;Jang, Aera
    • Food Science of Animal Resources
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    • v.33 no.6
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    • pp.723-729
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    • 2013
  • In this study, the quality characteristics due to the influence of various thawing methods on electro-magnetic and air blast frozen beef were examined. The loin and round of second grade Hanwoo were sliced into 5-7 cm thickness and packed with aerobic packaging. The packaged beef samples, which were frozen by air blast freezing at $-45^{\circ}C$ and electro-magnetic freezing at $-55^{\circ}C$, were thawed by 4 thawing methods with refrigeration ($4{\pm}1^{\circ}C$), room temperature (RT, $25^{\circ}C$), cold water ($15^{\circ}C$), and microwave (2450 MHz). These samples were thawed to the point, which were core temperature reached $0^{\circ}C$. Analyses were carried out to determine drip and cooking loss, water holding capacity (WHC), moisture contents and sensory evaluation. Frozen beef thawed by microwave indicated a lower drip loss (0.66-2.01%) than the other thawing methods (0.80-2.50%). Cooking loss after electro-magnetic freezing indicated 52.0% by microwave thawing for round compared with 41.8% by refrigeration, 50.1% by RT, and 50.8% by cold water. WHC thawing by microwave with electro-magnetic freezing didn't showed any difference depending on the thawing methods, while moisture contents was higher thawing by microwave with electro-magnetic freezing than refrigeration (71.9%), RT (75.0%), and cold water (74.9%) for round. The texture of sensory evaluation for round thawed by microwave result was the highest than refrigeration (4.7 point), RT (6.4 point) and cold water (6.6 point), while sensory evaluation was no significant difference. Therefore, it was shown that microwave thawing is an appropriate way to reduce the deterioration of meat quality due to freezing.