• 제목/요약/키워드: Pattern template

검색결과 163건 처리시간 0.026초

EEG Current Source Imaging using VEP Data Recorded inside a 3.0T MRI Magnet

  • Han Jae Y.;Choi Young H.;Im Chang H.;Kim Tae-S.;Lee Soo Y.
    • 대한의용생체공학회:의공학회지
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    • 제26권2호
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    • pp.95-99
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    • 2005
  • We have performed EEG current source imaging on the cortical surface using visual evoked potentials (VEPs) recorded inside a 3.0 T MRI magnet. In order to remove ballistocardiogram (BCG) artifacts in the VEPs, an improved BCG template subtraction technique is devised. Using the cortically constrained current source imaging technique and pattern-reversal visual stimulations, we have obtained current source maps from 10 subjects. To validate the EEG current source imaging inside the magnet, we have compared the current source maps to the ones obtained outside the magnet. The experimental results demonstrate that there is a strong correspondence between the current source maps, proving that current source imaging is feasible with the evoked potentials recorded inside a 3.0 T MRI magnet.

신 재생 에너지 저장용 초전도 세라믹 합성 (Fabrication Technology of High Tc Superconducting Thick Films for Renewed Electric Power Energy)

  • 이상헌
    • 전기학회논문지
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    • 제56권1호
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    • pp.128-131
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    • 2007
  • YBaCuO superconducting ceramic thick films were fabricated by chemical process. YBaCuO films have been successfully grown on $SrTiO_3$ substrates without a template layer. The films show poor or non superconductivity although they have excellent crystalline properties. ion channeling measurement made it clear that the strain in the films due to strong chemical bonding between the substrate and epilayer remains, resulting in the poor superconductivity. The X ray diffraction pattern of the YBaCuO thick films contained 90K phase. The self template method have resolved this problem. We obtained high-Jc as-grown YBaCuO on $SrTiO_3$ (100).

뇌파의 감성 분류기로서 다층 퍼셉트론의 활용에 관한 연구 (A Study on Application of the Multi-layor Perceptron to the Human Sensibility Classifier with Eletroencephalogram)

  • 김동준
    • 전기학회논문지
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    • 제67권11호
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    • pp.1506-1511
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    • 2018
  • This study presents a human sensibility evaluation method using neural network and multiple-template method on electroencephalogram(EEG). We used a multi-layer perceptron type neural network as the sensibility classifier using EEG signal. For our research objective, 10-channel EEG signals are collected from the healthy subjects. After the necessary preprocessing is performed on the acquired signals, the various EEG parameters are estimated and their discriminating performance is evaluated in terms of pattern classification capability. In our study, Linear Prediction(LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the degree of human sensibility. Also, the estimation for human comfortableness is performed by varying temperature and humidity environment factors and our results showed that the proposed scheme achieved good performances for evaluation of human sensibility.

Chemical Solution Deposition of PZT/Oxide Electrode Thin Film Capacitors and Their Micro-patterning by using SAM

  • Suzuki, Hisao
    • 한국정보디스플레이학회:학술대회논문집
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    • 한국정보디스플레이학회 2005년도 International Meeting on Information Displayvol.II
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    • pp.907-912
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    • 2005
  • Micro-patterns of $Pb(Zr_{0.53}Ti_{0.47})O_3$, PZT, thin films with a MPB composition were deposited on $Pt/Ti/SiO_2/Si$ substrate from molecular-designed PZT precursor solution by using self-assembledmonolayer(SAM) as a template. This method includes deposition of SAM followed by the optical etching by exposing the SAM to the UV-light, leading to the patterned SAM as a selective deposition template. The pattern of SAM was formed by irradiating UV-light to the SAM on a substrate and/or patterned PZT thin film through a metal mask for the selective deposition of patterned PZT or lanthanum nickel oxide (LNO) precursor films from alkoxide-based precursor solutions. As a result, patterned ferroelectric PZT and PZT/LNO thin film capacitors with good electrical properties in micrometer size could be successfully deposited.

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평판 디스플레이 비전 정렬 시스템의 기구학 및 제어 (Kinematics and Control of a Visual Alignment System for Flat Panel Displays)

  • 권상주;박찬식;이상무
    • 제어로봇시스템학회논문지
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    • 제14권4호
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    • pp.369-375
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    • 2008
  • The kinematics and control problem of a visual alignment system is investigated, which plays a crucial role in the fabrication process of flat panel displays. The first solution is the inverse kinematics of a 4PPR parallel alignment mechanism. It determines the driving distance of each joint to compensate the misalignment between mask and panel. Second, an efficient vision algorithm for fast alignment mark recognition is suggested, where by extracting essential feature points to represent the geometry of a mark, the geometric template matching enables much faster object recognition comparing with the general template matching. Finally, the overall visual alignment process including the kinematic solution, vision algorithm, and joint control is implemented and experimental results are given.

SIFT-Grid를 사용한 향상된 얼굴 인식 방법 (An Improved Face Recognition Method Using SIFT-Grid)

  • 김성훈;김형호;이현수
    • 디지털융복합연구
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    • 제11권2호
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    • pp.299-307
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    • 2013
  • 본 논문은 SIFT-Grid 기반의 얼굴 인식 시스템에서 식별 능력의 향상과 계산량 감소를 목적으로 한다. 첫번째는 한 얼굴 클래스의 다양한 훈련 이미지로부터 비슷한 SIFT 특징점들은 제거하고, 상이한 특징점들은 병합하는 통합템플릿의 구성 방법을 제안한다. 통합템플릿은 SIFT-Grid를 통해 나누어진 훈련 이미지들의 동일 부분영역 내의 특징점들에 대한 유사도 행렬의 계산과 임계치 기반의 히스토그램의 계산을 통해 구성하였다. 두 번째는 구성된 통합템플릿들로부터 테스트 이미지의 효과적인 식별을 위한 유사도 계산 방법을 제안한다. 유사도의 계산은 테스트 이미지와 각 클래스의 통합템플릿간의 일대일 비교로 수행된다. 이때 동일 부분영역 별로 유사도 점수와 임계치 기반의 보팅 점수가 계산된다. 얼굴 인식 작업에 대한 실험 결과 제안된 방법이 SIFT-Grid 기반의 다른 두 방법보다 정확한 것으로 확인 되었고, 또한 계산량도 감소하였다.

Visual Model of Pattern Design Based on Deep Convolutional Neural Network

  • Jingjing Ye;Jun Wang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권2호
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    • pp.311-326
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    • 2024
  • The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

A Neuro-Fuzzy Based Circular Pattern Recognition Circuit Using Current-mode Techniques

  • Eguchi, Kei;Ueno, Fumio;Tabata, Toru;Zhu, Hongbing;Tatae, Yoshiaki
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 ITC-CSCC -2
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    • pp.1029-1032
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    • 2000
  • A neuro-fuzzy based circuit to recognize circuit pat-terns is proposed in this paper. The simple algorithm and exemption from the use of template patterns as well as multipliers enable the proposed circuit to implement on the hardware of an economical scale. Furthermore, thanks to the circuit design by using current-mode techniques, the proposed circuit call achieve easy extendability of tile circuit and efficient pattern recognition with high-speed. The validity of the proposed algorithm and tile circuit design is confirmed by computer simulations. The proposed pattern recognition circuit is integrable by a standard CMOS technology.

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PEDOT:PSS Thin Films with Different Pattern Structures Prepared Using Colloidal Template

  • Yu, Jung-Hoon;Lee, Jin-Su;Nam, Sang-Hun;Boo, Jin-Hyo
    • Applied Science and Convergence Technology
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    • 제23권5호
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    • pp.254-260
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    • 2014
  • Organic solar cells have attracted extensive attention as a promising approach for cost-effective photovoltaic devices. However, organic solar cell has disadvantage of low power conversion efficiency in comparison with other type of solar cell, due to the recombination ratio of hole and electron is too large in the active layer. Thus we have change the surface structure of PEDOT:PSS layers to improve the current density by colloidal lithography method using various-size of polystyrene sphere. The two types of coating method were applied to fabricate the different pattern shape and height, such as spin coating and drop casting. Using the organic solvent, we easily eliminate the PS sphere and could make the varied pattern shapes by controlling the wet etching time. Also we have measured the electrical properties of patterned PEDOT:PSS film to check whether it is suitable for organic photovoltaics.

An Improved 2-D Moment Algorithm for Pattern Classification

  • Yoon, myoung-Young
    • 한국산업정보학회논문지
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    • 제4권2호
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    • pp.1-6
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    • 1999
  • 화상 데이터의 특성을 표현하는데 적합한 깁스분포를 바탕으로 특징벡터를 추출하여 패턴을 분류하는 새로운 알고리즘을 제안하였다. 특징벡터는 화상의 크기, 위치, 회전에 대해서 불변이며 접영에 대해서도 덜 민감한 특징을 갖는 2차원 모멘트들의 원소로 만들어진다. 알고리즘은 공간정보를 갖는 2차원 모멘트를 이용하여 특징벡터를 추출하는 과정과 거리함수를 이용하여 패턴을 분류하는 과정으로 구축하였다. 특징벡터는 깁스분포의 묘수를 추정하여 2차원 조건부 모멘트를 추출하여 구성한다. 패턴 분류 과정은 추출된 특징벡터로부터 제안된 판별거리함수를 계산하여 여러 원형 패턴 가운데 최소거리를 산출한 미지의 패턴을 원형패턴으로 분류한다. 제안된 방법의 성능을 검증하기 위하여 대문자와 소문자 52자로 구성된 훈련 데이터를 만들어 SUN ULTRA 10 워크스테이션에서 실험을 한 결과 98%이상의 분류성능이 있음을 밝혔다.

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