• Title/Summary/Keyword: Auto detection

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Novel Gain Control Scheme of Efficient Signal Detection Technique for Multiple Access VLC Systems (다중 접속 가시광 통신을 위한 신호 검출 기법의 게인 조절)

  • Lee, Sun Yui;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.11 no.1
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    • pp.32-36
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    • 2016
  • In this paper, a transceiver of VLC (Visible Light Communication) using LED white lighting has been implemented. We analyzed differences between conventional wireless communication and VLC with high-speed data transmission in a variety of environments, and confirmed symbols which passed through a channel using QAM. In order to get a high data rate, we found some variables that should be considered essentially though experiments. In addition, we set a bandwidth and found an optimal gain according to the distance between transmitter and receiver. We analyzed proposed system model through Labview and finally showed a system performance and to efficient data transmission and detection for VLC, we apply the orthogonal sequences to VLC system.

Performance Improvement of STDR Scheme Employing Sign Correlator (부호 상관기를 활용한 STDR 기법의 탐지 성능 개선)

  • Han, Jeong Jae;Noh, Sanguk;Park, So Ryoung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.6
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    • pp.990-996
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    • 2015
  • This paper proposes an enhanced scheme adding a sign detector at the front of the correlator in STDR (sequence time domain reflectometry) system. We have executed simulations to show the improvement of detection performance in two fault types and various fault locations. Consequently, it can be shown that the proposed scheme improves the detection performance of the location of far-fault without increasing the computational complexity.

Single-pixel Autofocus with Plasmonic Nanostructures

  • Seok, Godeun;Choi, Seunghwan;Kim, Yunkyung
    • Current Optics and Photonics
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    • v.4 no.5
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    • pp.428-433
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    • 2020
  • Recently, the on-chip autofocus (AF) function has become essential to the CMOS image sensor. An auto-focus usually operates using phase detection of the photocurrent difference from a pair of AF pixels that have focused or defocused. However, the phase-detection method requires a pair of AF pixels for comparison of readout. Therefore, the pixel variation may reduce AF performance. In this paper, we propose a color-selective AF pixel with a plasmonic nanostructure in a 0.9 μ㎡ pixel. The suggested AF pixel requires one pixel for AF function. The plasmonic nanostructure uses metal-insulator-metal (MIM) stack arrays instead of a color filter (CF). The color filters are formed at the subwavelength, and they transmit the specific wavelength of light according to the stack period and incident angles. For the optical analysis of the pixel, a finite-difference time-domain (FDTD) simulation was conducted. The analysis showed that the MIM stack arrays in the pixels perform as an AF pixel. As the primary metric of AF performance, the resulting AF contrasts are 1.8 for the red pixels, 1.6 for green, and 1.5 blue. Based on the simulation results, we confirmed the autofocusing performance of the MIM stack arrays.

Algorithm of an automated auditory brainstem response neonatal hearing screening method (신생아를 대상으로한 청성뇌간유발반응의 자동 판독 알고리즘)

  • Jung, Won-Hyuk;Hong, Hyun-Ki;Kim, Sung-Woo;Kim, Jin-Tae;Park, Joong-Hoon;Kim, Deok-Won
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.825-826
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    • 2006
  • In this paper, we propose an algorithm that applies Rolle's theorem to automatically detect and label peak III and V of the normal, suprathreshold auditory brainstem response (ABR). ABR waveform were recorded from 55 normal-hearing ears at screening levels varying from 30 to 60 dBnHL. For each ABR waveform, the peak-finding algorithm proceeded in fourth steps: (1) Select maximum and minimum values of the target ABR waveform, (2) divide this range into n equal parts, (3) effective candidate peaks in the ABR waveform are identified using Rolle's theorem (4) peak III and V are identified from these candidate peaks based on their latency and morphology. As a result, proposed auto dectection method showed high correlation and accuracy with manual detection method performed by clinician. By using proposed algorithm, clinician can detect and label peak III and V faster and more efficient than manual detection method.

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Auto Correction Technique of Photography Composition Using ROI Extraction Method (ROI 추출을 통한 사진 구도 자동 보정 기법)

  • Ha, Ho-Saeng;Park, Dae-Hyun;Kim, Yoon
    • Journal of Information Technology and Architecture
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    • v.10 no.1
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    • pp.113-122
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    • 2013
  • In this paper, we propose the method that automatically corrects the composition of a picture stylishly as well as reliably by cropping pictures based on the Rule of Thirds. The region of interest (ROI) is extracted from a picture by applying the Saliency Map and the Image Segmentation technology, the composition of the photo is amended based on this area to satisfy the Rule of Thirds. In addition, since the face region of the person is added to ROI by the Face Detection technique and the composition is amended by the various scenario according to ROI, the little more natural picture is acquired. The experimental result shows that the photo of the corrected composition was naturally amended compared with the original photo.

An Improved Two-Terminal Numerical Algorithm of Fault Location Estimation and Arcing Fault Detection for Adaptive AutoReclosure (고속 적응자동재폐로를 위한 사고거리추정 및 사고판별에 관한 개선된 양단자 수치해석 알고리즘)

  • Lee, Chan-Joo;Kim, Hyun-Houng;Park, Jong-Bae;Shin, Joong-Rin;Radoievic, Zoran
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.54 no.11
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    • pp.525-532
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    • 2005
  • This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phaser in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the assumed PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) is used.

IPv6 Autoconfiguration for Hierarchical MANETs with Efficient Leader Election Algorithm

  • Bouk, Safdar Hussain;Sasase, Iwao
    • Journal of Communications and Networks
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    • v.11 no.3
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    • pp.248-260
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    • 2009
  • To connect a mobile ad hoc network (MANET) with an IP network and to carryout communication, ad hoc network node needs to be configured with unique IP adress. Dynamic host configuration protocol (DHCP) server autoconfigure nodes in wired networks. However, this cannot be applied to ad hoc network without introducing some changes in auto configuration mechanism, due to intrinsic properties (i.e., multi-hop, dynamic, and distributed nature) of the network. In this paper, we propose a scalable autoconfiguration scheme for MANETs with hierarchical topology consisting of leader and member nodes, by considering the global Internet connectivity with minimum overhead. In our proposed scheme, a joining node selects one of the pre-configured nodes for its duplicate address detection (DAD) operation. We reduce overhead and make our scheme scalable by eliminating the broadcast of DAD messages in the network. We also propose the group leader election algorithm, which takes into account the resources, density, and position information of a node to select a new leader. Our simulation results show that our proposed scheme is effective to reduce the overhead and is scalable. Also, it is shown that the proposed scheme provides an efficient method to heal the network after partitioning and merging by enhancing the role of bordering nodes in the group.

Robust Detection Deep Learning Model in the Various Exterior Wall Cracks (다양한 외벽 균열에 강인한 딥러닝 검출 모델 개발)

  • Kim, Gyeong-Yeong;Lee, Ho-Ryeong;Kim, Dong-Ju
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.53-56
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    • 2021
  • 국내 산업화가 들어선 후 산업화 당시 지었던 낙후된 건물의 증가에 따라 구조물의 손상 조사 및 검사 방법의 수요가 늘어나고 있다. 일반적으로 구조물의 손상은 전문 검사원이 현장에서 직접 측량도구와 시각적인 방식으로 검사한다. 그러나 전문 검사원들이 직접 조사하는 수고에 비해 균열을 검사하는 방식 자체가 단순하고, 일반 사람이 검사하기에는 객관성이 떨어지는 한계가 있어 균열을 자동적으로 검출함으로써 객관성과 편의성을 보장할 기술이 필요하다. 본 연구에서는 이미지 기반으로 다양한 환경에서의 외벽 균열을 검출할 수 있는 딥러닝 모델 개발을 소개한다. 균열 검출을 위해 다양한 외벽 균열 관련 데이터셋을 확보 및 구축하고 각 데이터셋의 검출 정보를 보완할 반자동(semi-auto) 라벨링 작업을 수행하였다. 두 번째로 기존 높은 검출 성능을 보였던 모델들을 선정 및 비교하여 YOLO v5 모델을 최종적으로 선정하였고, 도메인이 각각 다른 데이터셋에 대한 교차 학습을 통해 각 데이터셋의 mAP의 편차가 31%에서 11%로 좁히는 작업을 수행하였다. 이를 통해 실제 상황에서의 균열 영상에서 균열을 검출할 수 있는 측량 시스템을 개발함으로써 실질적인 검사의 도구로 활용될 수 있길 기대한다.

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Deep-learning-based gestational sac detection in ultrasound images using modified YOLOv7-E6E model

  • Tae-kyeong Kim;Jin Soo Kim;Hyun-chong Cho
    • Journal of Animal Science and Technology
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    • v.65 no.3
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    • pp.627-637
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    • 2023
  • As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.

Power Disturbance Classifier Using Wavelet-Based Neural Network

  • Choi Jae-Ho;Kim Hong-Kyun;Lee Jin-Mok;Chung Gyo-Bum
    • Journal of Power Electronics
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    • v.6 no.4
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    • pp.307-314
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    • 2006
  • This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.