• 제목/요약/키워드: Auto detection

검색결과 354건 처리시간 0.027초

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

  • 하호생;박대현;김윤
    • 정보화연구
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    • 제10권1호
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    • pp.113-122
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    • 2013
  • 본 논문에서는 영상을 3분할 기법에 맞춰 재구성함으로써 자동으로 구도를 안정적이고 세련되게 보정하는 기법을 제안한다. Saliency Map과 Image Segmentation기술을 이용하여 사진에서 피사체의 관심영역(Region Of Interest, ROI)을 구하고, 그 영역을 기준으로 3분할 기법에 맞도록 사진을 Cropping하여 구도를 보정한다. 또한, 얼굴 인식(Face Detection)기법을 활용하여 사람의 얼굴을 ROI에 추가하고 ROI에 따른 다양한 시나리오에 의하여 구도를 보정함으로써, 좀 더 자연스러운 사진을 얻는다. 실험결과를 통해 보정된 구도의 사진이 원본사진과 비교하여 자연스럽게 보정이 되었는다는 것을 알 수 있다.

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

  • 이찬주;김현홍;박종배;신중린;조란 라도예빅
    • 대한전기학회논문지:전력기술부문A
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    • 제54권11호
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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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    • 제11권3호
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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.

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

  • 김경영;이호령;김동주
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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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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    • 제65권3호
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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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    • 제6권4호
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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.

Output only structural modal identification using matrix pencil method

  • Nagarajaiah, Satish;Chen, Bilei
    • Structural Monitoring and Maintenance
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    • 제3권4호
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    • pp.395-406
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    • 2016
  • Modal parameter identification has received much attention recently for their usefulness in earthquake engineering, damage detection and structural health monitoring. The identification method based on Matrix Pencil technique is adopted in this paper to identify structural modal parameters, such as natural frequencies, damping ratios and modal shapes using impulse vibration responses. This method can also be applied to dynamic responses induced by stationary and white-noise inputs since the auto- and cross-correlation function of the two outputs has the same form as the impulse response dynamic functions. Matrix Pencil method is very robust to noise contained in the measurement data. It has a lower variance of estimates of the parameters of interest than the Polynomial Method, and is also computationally more efficient. The numerical simulation results show that this technique can identify modal parameters accurately even if the noise level is high.

Time Dependent Correlation Function과 그의 응용에 관한 연구 (Some Study on Time Dependent Correlation Function and Its Applications)

  • 안수길
    • 대한전자공학회논문지
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    • 제10권6호
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    • pp.25-44
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    • 1973
  • 원인과 결과간의 위상과 work사이의 관계를 correlation function의 견지에서 검토하였고 continuous signal에 포함되어 있는 redundancy를 지적하여 sampling취급의 근거를 auto correlation면에서 보였다. 두 신호사이의 Correlation의 시간변동을 보여줄 수 있는 Time Dependent Correlation Function을 정의하여 PLL회로에서 그 유편성을 보였다. 끝으로 다상포낙선검파법에 의한 특성의 개선을 T.D.Correlation Furction에 의한 Correlation Analysis를 통하려 입증하였다.

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공구파손검출을 위한 시스템인식에 관한 연구 (A Study on the System Identification for Detection of Tool Breakage)

  • 사승윤
    • 한국생산제조학회지
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    • 제9권5호
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    • pp.144-149
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    • 2000
  • The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

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드릴링 작업의 모델링과 진단법에 관한 연구 (A Study on the Modeling and Diagnostics in Drilling Operation)

  • 윤문철
    • 동력기계공학회지
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    • 제2권2호
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    • pp.73-80
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    • 1998
  • The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

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