• Title/Summary/Keyword: 특징점 검출

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Development of a Visual Simulation Tool for Object Behavior Chart based on LOTOS Formalism (객체행위챠트를 위한 LOTOS 정형기법 기반 시각적 시뮬레이션 도구의 개발)

  • Lee, Gwang-Yong;O, Yeong-Bae
    • Journal of KIISE:Computing Practices and Letters
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    • v.5 no.5
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    • pp.595-610
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    • 1999
  • This paper presents a visual simulation tool for verification and validation(V&V) of design implications of the Object Behavior Chart developed in accordance with the existing real-time object's behavior design method. This tool can simulates the dynamic interactions using the executable simulation machine, that is EFSM(Extended Finite State Machine) and can detect various logical and temporal errors in the visual object behavior charts before a concrete implementation is made. For this, a LOTOS prototype specification is automatically generated from the visual Object Behavior Chart, and is translated into an EFSM. This system is implemented in Visual C++ version 4.2 and currently runs on PC Windows 95 environment. For simulation purpose, LOTOS was chosen because of it's excellence in specifying communication protocols. Our research contributes to the support tools for seamlessly integrating methodology-based graphical models and formal-based simulation techniques, and also contributes to the automated V&V of the Visual Models.

Light-Ontology Classification for Efficient Object Detection using a Hierarchical Tree Structure (효과적인 객체 검출을 위한 계층적 트리 구조를 이용한 조명 온톨로지 분류)

  • Kang, Sung-Kwan;Lee, Jung-Hyun
    • Journal of Digital Convergence
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    • v.10 no.10
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    • pp.215-220
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    • 2012
  • This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

Study on Structure Visual Inspection Technology using Drones and Image Analysis Techniques (드론과 이미지 분석기법을 활용한 구조물 외관점검 기술 연구)

  • Kim, Jong-Woo;Jung, Young-Woo;Rhim, Hong-Chul
    • Journal of the Korea Institute of Building Construction
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    • v.17 no.6
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    • pp.545-557
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    • 2017
  • The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.

Design and Implementation of Optimal Smart Home Control System (최적의 스마트 홈 제어 시스템 설계 및 구현)

  • Lee, Hyoung-Ro;Lin, Chi-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.1
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    • pp.135-141
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    • 2018
  • In this paper, we describe design and implementation of optimal smart home control system. Recent developments in technologies such as sensors and communication have enabled the Internet of Things to control a wide range of objects, such as light bulbs, socket-outlet, or clothing. Many businesses rely on the launch of collaborative services between them. However, traditional IoT systems often support a single protocol, although data is transmitted across multiple protocols for end-to-end devices. In addition, depending on the manufacturer of the Internet of things, there is a dedicated application and it has a high degree of complexity in registering and controlling different IoT devices for the internet of things. ARIoT system, special marking points and edge extraction techniques are used to detect objects, but there are relatively low deviations depending on the sampling data. The proposed system implements an IoT gateway of object based on OneM2M to compensate for existing problems. It supports diverse protocols of end to end devices and supported them with a single application. In addition, devices were learned by using deep learning in the artificial intelligence field and improved object recognition of existing systems by inference and detection, reducing the deviation of recognition rates.

The Development of Image Processing System Using Area Camera for Feeding Lumber (영역카메라를 이용한 이송중인 제재목의 화상처리시스템 개발)

  • Kim, Byung Nam;Lee, Hyoung Woo;Kim, Kwang Mo
    • Journal of the Korean Wood Science and Technology
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    • v.37 no.1
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    • pp.37-47
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    • 2009
  • For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.

1,3-bisdicyanovinylindane 색소를 이용한 선택적 $Hg^{2+}$ 감지 특성

  • Kim, Su-Ho;Kim, Young-Sung;Kim, Sung-Hoon;Son, Young-A
    • Proceedings of the Korean Society of Dyers and Finishers Conference
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    • 2009.11a
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    • pp.19-20
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    • 2009
  • 최근 화학, 물리, 생명과학, 전기, 전자등의 다양한 분야에 활발하게 연구가 이루어지고 있는 초분자 화학은 선택적 분자인지를 위한 효율적인 골격구조와 나아가 다양한 계에 응용할 수 있다. 초분자 화학의 분자인지 과정의 특징은 일반적으로 수용체 (receptor 혹은 host)가 목표가 되는 기질 (substrate, analyte, 혹은 guest)에 대하여 선택적으로 식별하고 반응하는 것이다. 비공유 결합성 상호작용에 의하여 이루어지는 초분자 화학의 분자 인지 과정의 특징은 일반적으로 수용체 (receptor 혹은 host)가 목표가 되는 기질 (substrate, analyte, 혹은 guest)에 대하여 선택적으로 식별하고 반응하는 것이다. 이는 공유결합을 이용하는 분자화학과는 차별화 된 것이다. 수용체는 간단한 구조의 화합물 및 금속 이온들과 같은 기질과 가역적으로 상호 작용할 수 있는 착물을 형성한다. 최근들어 급격한 산업화가 진행되어 환경문제가 심각하게 대두 되어져 왔고, 그 중에서 특히 수은이나 카드뮴에 의한 질병, 납에 의한 중독 등 중금속에 의한 오염이 크게 나타남에도 불구하고, 현재 그러한 중금속을 검출함에 있어 많은 비용과 시간이 드는 문제점이 있다. 또한 우리에게 이로운 금속은 효율적 분석을 통해 환경계와 의료계에 많은 도움을 줄 것으로 사례되므로 화학센서 기술의 개발은 절실히 요구되어지고 있다. 이에 새로운 1,3-bisdicyanovinylindane 을 통해 $Hg^{2+}$ 금속의 감지 여부 알아보고, 그 특성을 파악하고자 한다. 1,3-indandion (2.18g, 14.9mmol), malononitrile (2.95g, 44.7mmol), ethanol 50ml를 20분간 상온에서 용해시킨다. 후에 sodium trihydrate acetate(3.05g)을 첨가한 후 5시간 동안 환류반응 시킨다. 이 과정에서 얻어진 용액을 필터과정을 통하여 에서 합성 반응 중에 생성된 불순물(1,3-dicyanovinylindane-1-one, monocondensation)을 제거한다. 필터과정을 통해 걸러진 미 반응 물질을 제�G 용반응욕액을 증류수(100ml)를 이용하여 희석시키고 난 후 염산을 이용, 산성화 시켜 고체 생성물을 얻어낸다. 이렇게 생성된 고체 생성물은 다시 필터 및 건조를 통하여 회색의 고체 화합물을 얻어낸다. 1,3-bisdicyanovinylindane과 금속이온에 대한 감응도를 확인하기 위하여 metanol/water(1:2)을 용매로 하여 금속이온의 농도를 변화시켜 발색특성을 살펴보았다. 본 색소화합물과 Hg2+에 대한 UV 흡광도 변화 적정결과와 그 화합물의 상태 살펴본 결과 금속이온이 0.2ml씩 더 참가되면서 색의 변화를 뚜렷하게 나타내었다. 반면 그 밖에 이온($Fe^{3+}$, $Ag^{2+}$, $Pd^{2+}$, $Zn^2$, $Fe^{2+}$, $Cu^{2+}$, $Pb^{2+}$)은 UV 흡광도 변화가 적거나 변화 자체가 없었다. 하지만 과량의 $Fe^{3+}$, $Ag^{2+}$, $Pd^{2+}$는 색상 변화를 나타내었으며,이와 같은 흡광도 변화는 금속에 따라 약간의 차이가 있지만, 420nm를 등흡수점으로 하여, 580nm의 파장 영역에 있는 흡수 밴드의 세기는 감소하는 반면 400nm 파장 영역에 있는 흡수 밴드의 세기가 증가하였다. 1,3-bisdicyanovinylindane 화합물은 다양한 생물계 및 환경계에서 요구되는 micro mol에서 milli mol 농도 영역의 $Hg^{2+}$ 이온의 선택적이고 민감한 검출과 정량적인 분석에 유용하게 사용될 수 있을 것이다.

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Design of a Holter Monitoring System with Flash Memory Card (플레쉬 메모리 카드를 이용한 홀터 심전계의 설계)

  • 송근국;이경중
    • Journal of Biomedical Engineering Research
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    • v.19 no.3
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    • pp.251-260
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    • 1998
  • The Holter monitoring system is a widely used noninvasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we design a high performance intelligent holter monitoring system which is characterized by the small-sized and the low-power consumption. The system hardware consists of one-chip microcontroller(68HC11E9), ECG preprocessing circuit, and flash memory card. ECG preprocessing circuit is made of ECG preamplifier with gain of 250, 500 and 1000, the bandpass filter with bandwidth of 0.05-100Hz, the auto-balancing circuit and the saturation-calibrating circuit to eliminate baseline wandering, ECG signal sampled at 240 samples/sec is converted to the digital signal. We use a linear recursive filter and preprocessing algorithm to detect the ECG parameters which are QRS complex, and Q-R-T points, ST-level, HR, QT interval. The long-term acquired ECG signals and diagnostic parameters are compressed by the MFan(Modified Fan) and the delta modulation method. To easily interface with the PC based analyzer program which is operated in DOS and Windows, the compressed data, that are compatible to FFS(flash file system) format, are stored at the flash memory card with SBF(symmetric block format).

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DNN based Robust Speech Feature Extraction and Signal Noise Removal Method Using Improved Average Prediction LMS Filter for Speech Recognition (음성 인식을 위한 개선된 평균 예측 LMS 필터를 이용한 DNN 기반의 강인한 음성 특징 추출 및 신호 잡음 제거 기법)

  • Oh, SangYeob
    • Journal of Convergence for Information Technology
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    • v.11 no.6
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    • pp.1-6
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    • 2021
  • In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

A Comparative Study of Standard Methods for Assessing Ecotoxicity of Endocrine Disrupting Chemicals (내분비계장애물질의 생태독성평가를 위한 표준시험법 비교연구)

  • Kwak, Jin Il;Cui, Rongxue;Moon, Jongmin;Kim, Dokyung;An, Youn-Joo
    • Journal of Korean Society of Environmental Engineers
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    • v.39 no.3
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    • pp.132-139
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    • 2017
  • Endocrine disrupting chemicals (EDCs) interfere with endocrine system in organisms, and have different mode of actions compared to conventional chemicals. Therefore, EDCs specific ecotoxicity tests and assessments have been globally developed by some organizations such as OECD, ASTM, and USEPA. In Korea, researches on EDCs and monitoring of EDCs in domestic environments were also continued. However, Korean ecotoxicity test guidelines for EDCs are not suggested till date. The purpose of this study is to review and analyze international ecotoxicity test guidelines for EDCs and the compare ecotoxicity methods and toxicity endpoints among standard test guidelines. We found that there are very limited EDCs specific soil ecotoxicity test guidelines (only in ASTM) compared to aquatic ecotoxicity test guidelines. Currently, fish, amphibian, waterflea, copepoda, earthworm, white worm, springtail, nematode, mite, and midge are suggested as standard ecotoxicity test species for EDCs. Reproduction, hormones, growth, vitellogenin, sex retio and development were proposed as endpoints for EDCs ecotoxicity. This study provided the comparison of EDCs specific ecotoxicity methods and endpoints between standard test guidelines, and suggested the further research to develop the method for assesseing ecotoxicity of EDCs.

Virtual core point detection and ROI extraction for finger vein recognition (지정맥 인식을 위한 가상 코어점 검출 및 ROI 추출)

  • Lee, Ju-Won;Lee, Byeong-Ro
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.3
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    • pp.249-255
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    • 2017
  • The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.