• Title/Summary/Keyword: Curve detection

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Intensive Monitoring Survey of Nearby Galaxies (IMSNG) : Constraints on the progenitor system of a normal Type Ia SN 2019ein from its light curve at the early phase

  • Lim, Gu;Im, Myungshin;Kim, Dohyeong;Paek, Gregory S.H;Choi, Changsu;Kim, Sophia;Hwang, Sungyong
    • The Bulletin of The Korean Astronomical Society
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    • v.46 no.1
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    • pp.55.2-56
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    • 2021
  • The progenitor of Type Ia supernovae (SNe Ia) is mainly believed to be a close binary system of acarbon-oxygen white dwarf (CO WD) and non-degenerate companion (single degenerate) or another WD (double degenerate). However, it is unclear which system is more prevalent. Here, we present a high cadence optical/Near-IR light curve of normal but slightly faint type Ia SN 2019ein from IMSNG project. We fit the early light curve (t <+8.3 days from the first detection) with various models to find the shock-heated cooling emission from SN ejecta-companion interaction. No significant shock-heated cooling emission is found, from which we constrain the progenitor star size as the following. The upper limit (Rupper,*) of the companion size in R-band is ~0.2R when forcing the first light time (tfl) to have one value and ~0.9R when using the mean value of tfl from the fitting in each band. Assuming the source of the I-band curve is almost powered from the radioactive decay, we obtained Rupper,*~1.2R. The early B-V color curve is in agreement with the model color curve of the 2M main sequence companion. These results allow us to at least rule out large stars like red giants as a companion star of the binary progenitor system of this supernova. B-R and V-R color do not show any significant signs of a red bump, which shows a thin helium shell (MHe<0.1M) for the sub-Mch WD (double detonation model). In addition, we estimated the distance to NGC 5353 as 37.098±0.028Mpc.

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Characterization of Signal Measuring System Using ion Selective Microelectrode and Electrometer (이온 선택성 미소전극과 전위계를 이용한 신호 계측 시스템의 특성 평가)

  • Jun, Hyo-Yong;Seon, Kyeong-Suk;Park, Jeung-Jin;Byun, Im-Gyu;Park, Tae-Joo
    • Journal of Korean Society of Environmental Engineers
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    • v.28 no.11
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    • pp.1148-1153
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    • 2006
  • Signal measuring system to analyze ion concentrations in biofilm was constructed with ion selective microeleclrode and electrometer. In order to evaluate the performance and applicability of signal measuring system, the following characteristics, such as slope of calibration curve, detection limit, variation of response according to the time, and potentiometric selectivity coefficient, were investigated. The slope of calibration curve showed high degree of association for primary ion concentration. The response of the system was log-linear in standard solution down to $10{\mu}M$ and signal measuring system was not sensitive for interfering ions. In comparison with commercial electrometer, the fabricated electrometer system had similar tendencies for the slope of calibration curve, detection limit, and response time. Therefore the signal measuring system could be used to investigate ion profiles in biofilm as a cost effective and reliable measuring system.

A Study of Automatic Recognition on Target and Flame Based Gradient Vector Field Using Infrared Image (적외선 영상을 이용한 Gradient Vector Field 기반의 표적 및 화염 자동인식 연구)

  • Kim, Chun-Ho;Lee, Ju-Young
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.49 no.1
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    • pp.63-73
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    • 2021
  • This paper presents a algorithm for automatic target recognition robust to the influence of the flame in order to track the target by EOTS(Electro-Optical Targeting System) equipped on UAV(Unmanned Aerial Vehicle) when there is aerial target or marine target with flame at the same time. The proposed method converts infrared images of targets and flames into a gradient vector field, and applies each gradient magnitude to a polynomial curve fitting technique to extract polynomial coefficients, and learns them in a shallow neural network model to automatically recognize targets and flames. The performance of the proposed technique was confirmed by utilizing the various infrared image database of the target and flame. Using this algorithm, it can be applied to areas where collision avoidance, forest fire detection, automatic detection and recognition of targets in the air and sea during automatic flight of unmanned aircraft.

Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction

  • Kyungsoo Bae;Dong Yul Oh;Il Dong Yun;Kyung Nyeo Jeon
    • Korean Journal of Radiology
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    • v.23 no.1
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    • pp.139-149
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    • 2022
  • Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

GEOLOGICAL LINEAMENTS ANALYSIS BY IFSAR IMAGES

  • Wu Tzong-Dar;Chang Li Chi
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.169-172
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    • 2005
  • Modem SAR interferometry (IFSAR) sensors delivering intensity images and corresponding digital terrain model (DTM) allow for a thorough surface lineament interpretation with the all-weather day-night applicability. In this paper, an automatic linear-feature detection algorithm for high-resolution SAR images acquired in Taiwan is proposed. Methodologies to extract linear features consist of several stages. First, the image denoising techniques are used to remove the speckle noise on the raw image. In this stage, the Lee filter has been chosen because of its superior performance. After denoising, the Coefficient of Variation Detector is performed on the result images for edge enhancements and detection. Dilation and erosion techniques are used to reconnect the fragmented lines. The Hough transform, which is a special case of a more general transform known as Radon transform, is a suitable method for line detection in our analysis. Finally, linear features are extracted from the binary edge image. The last stage contains many substeps such as edge thinning and curve pruning.

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Reverse Engineering of Compound Surfaces Using Boundary Detection Method

  • Cho, Myeong-Woo;Seo, Tae-Il;Kim, Jae-Doc;Kwon, Oh-Yang
    • Journal of Mechanical Science and Technology
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    • v.14 no.10
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    • pp.1104-1113
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    • 2000
  • This paper proposes an efficient reverse engineering technique for compound surfaces using a boundary detection method. This approach consists in extracting geometric edge information using a vision system, which can be used in order to drastically reduce geometric errors in the vicinity of compound surface boundaries. Through the image-processing technique and the interpolation process, boundaries are reconstructed by either analytic curves (e. g. circle, ellipse, line) or parametric curves (B-spline curve). In other regions, except boundaries, geometric data are acquired on CMM as points inspected using a touch type probe, and then they are interpolated on several surfaces using a B-spline skinning method. Finally, the boundary edge and the skinned surfaces are combined to reconstruct the final compound surface. Through simulations and experimental works, the effectiveness of the proposed method is confirmed.

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Experimental Study on Development of ELISA Method for the Detection of Sulfamethazine Residues (잔류 Sulfamethazine 검출용 ELISA 개발에 관한 실험적 연구)

  • 임윤규;김성희
    • Journal of Food Hygiene and Safety
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    • v.10 no.4
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    • pp.213-217
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    • 1995
  • A screening method has been developed for detecting sulfamethazine(SMZ) contamination of meat or feeds by using horseradish peroxidase (HRP) labeled protein A (Prot AHRP)and an indirect competitve enzyme-linked immunosorbent assay(ELISA). The assay is based on competitve binding of guinea pig anti-SMZ with SMZ in smaple and SMZ-gelatin conjugate(SMZ.GEL). Percent binding (B.Bo$\times$100) was calculated from the absorbance in the absence (B0) and presence (B) of SMZ. By the sandard curve prepared by plotting log(SMZ) vs percent binding of each known reference solution, the detection limit was 1.0ppb or less. Cross reacton with sulfadimethoxine, sulfaguaniding, sulfamerazine, sulfamthoxpyridazine, sulfanilamide, sulfisomidine and sufisoxazole were not observed. But sulfamerazine crossreacted in the test. The EC-50 value (concentration causing 50% inhibition of color development compared with blank) of sulfamerazine was 2.0 ppm. Further quality control will make the ELISA system ideal for the detection of SMZ in meat or feeds.

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Construction of Through Transmission Scanning System for Weld Defects Detection of Rail Weld Zone (레일용접부의 용접결함검출을 위한 투과주사시스템의 구축)

  • Yun, In-Sik
    • Journal of the Korean Society of Safety
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    • v.20 no.1 s.69
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    • pp.30-35
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    • 2005
  • This study proposes construction of through transmission for weld defects detection of rail weld zone from ultrasonic signals. For these purposes, the ultrasonic signals for defects(porosity and crack) of weld zone in rails are acquired in the type of time series data and echo strength. 6 lines in the distance amplitude characteristics curve(DACC) indicated damage evaluation standard of weld zone in rails. The acquired ultrasonic signals agree flirty well with the mesured results of reference block and sensitivity block(defect location beam propagation distance, echo strength, etc). The proposed construction of through transmission in this study can be used for weld defects detection of rail weld zone.

Detection of Circular Arcs within Tolerant Error (허용 오차를 만족하는 호의 추출)

  • Lyu Sung-Pil
    • Journal of KIISE:Software and Applications
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    • v.32 no.9
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    • pp.868-877
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    • 2005
  • Arcs are usually treated as significant features in the field of pattern recognition. This paper presents a method to detect arcs from digital planar curves and estimate their arc centers and radii by using geometric analysis. The deviation of distance between the original curve and the detected arc by the proposed method does not exceed a tolerant error. The experimental results show that the proposed method is available for the detection of arcs iron not only smooth but also heavily noisy curves.

A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network

  • Tama, Bayu Adhi;Rhee, Kyung-Hyune
    • Journal of Information Processing Systems
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    • v.13 no.5
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    • pp.1203-1212
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    • 2017
  • Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.