• 제목/요약/키워드: Threshold method

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Gamma spectrum denoising method based on improved wavelet threshold

  • Xie, Bo;Xiong, Zhangqiang;Wang, Zhijian;Zhang, Lijiao;Zhang, Dazhou;Li, Fusheng
    • Nuclear Engineering and Technology
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    • 제52권8호
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    • pp.1771-1776
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    • 2020
  • Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

A threshold decision of the object image by using the smart tag

  • Im, Chang-Jun;Kim, Jin-Young;Joung, Kwan-Young;Lee, Ho-Gil
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.2368-2372
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    • 2005
  • We proposed a novel method for object recognition using the Smart tag system in the previous research. We identified the object easily, but could not assure the object pose, because the threshold problem was not solved. So we propose a new method to solve this threshold problem. This method uses a smart tag to decide the threshold by recording color information of the image when the object feature is extracted. This method records the original of the object color information at the smart tag first. And then it records the object image information, the circumstance image information and the sensors information continuously when the object feature is extracted through the experiments. Finally, it estimates the current threshold by recorded information. This method can be applied the threshold to each objects. And it can solve the difficult threshold decision problem easily. To approve the possibility of our method, we implemented our approach by using easy and simple techniques as possible.

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적응 문턱치를 이용한 열영상 화염 검출 알고리즘 (Flame detection algorithm using adaptive threshold in thermal video)

  • 정수영;김원호
    • 한국위성정보통신학회논문지
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    • 제9권4호
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    • pp.91-96
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    • 2014
  • 본 논문은 적외선 열영상에서 영상의 밝기와 대비 변화에 따라 적응적으로 화염 후보 영역을 검출하기 위한 적응 문턱치를 제안한다. 현장에 사용 되고 있는 화재 검출 시스템은 카메라의 설치 장소에 따라 얻어지는 영상의 밝기나 대비의 변화가 발생 하여 고정된 문턱치를 적용하는 화재 검출 알고리즘의 성능이 변화하게 되므로 환경에 적응적인 문턱치가 필요하다. 제안하는 적응 문턱치를 이용한 화염 검출 알고리즘은 화염의 특성인 온도와 동적임 특성을 분석하여 화염을 검출 한다. 실험을 위해 고정 문턱치를 이용한 화염 검출 알고리즘과 비교 하였으며 제안된 적응 문턱치를 이용한 화염 검출 알고리즘은 화염 검출률 91.42%이며 고정 문턱치를 적용 하였을 때 보다 오검출률을 약 20%가 감소한다. 그리고 영상의 밝기와 대비 변화에 의한 검출 결과가 일정함을 보여 준다.

DWT를 이용한 형광 X-선 스펙트럼의 interval Threshold를 적용하기 위한 블록화 알고리즘 (X-ray fluorescence spectrum of the block algorithm to apply the interval threshold method using DWT)

  • 양상훈;이재환;박동선
    • 한국산학기술학회논문지
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    • 제13권5호
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    • pp.2291-2297
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    • 2012
  • X-선 스펙트럼 데이터는 물질의 성분과 관련이 없는 신호(백그라운드, 노이즈)들을 포함 하고 있다. XRF는 스펙트럼에서 가우시안 형태의 피크 위치와 크기를 이용하여 시료의 성분을 분석하며, 시료의 성분을 정확히 분석하기 위해서는 노이즈와 백그라운드를 제거 하여야 한다. 백그라운드를 제거하기 위한 방법으로는 SNIP, Threshold, Morphology 방법 등을 적용하고 있으며, Threshold 기법을 중에서 블록별로 각기 다른 임계값을 적용하는 Interval Threshold기법이 하나의 임계값을 적용하는 Level Threshold 방법보다 더 좋은 성능을 발휘한다. 본 논문에서는 Interval Threshold를 적용하기 위하여 웨이블릿을 이용하여 블록을 분리하는 알고리즘을 제안하였다.

무선 센서 네트워크 환경에서 적응형 임계값 설정 방법 (An Adaptive Threshold Method in Wireless Sensor Network Environments)

  • 김인태;김두용
    • 반도체디스플레이기술학회지
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    • 제7권1호
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    • pp.23-27
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    • 2008
  • Wireless sensor networks are emerging as a solution for a wide range of data gathering applications. The most difficult challenge for the design of sensor nodes is the need for significant reductions in energy consumption. The threshold methods which filter redundant and similar data can be used to save energy. In this paper, we propose the adaptive threshold method to effectively manage the energy in wireless sensor nodes. In the adaptive threshold method, wireless sensor nodes can change the thresholds dynamically as the sensing environments vary. The simulation results show that the adaptive threshold method works very effectively even when we experience the significant volatility in the data. This scheme can be used in order to monitor the malfunction in the equipment of semiconductor manufacturing line.

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레이저 다이오드의 코팅된 단면의 반사율 측정에 사용되는 문턱전류비에 관한 연구 (A study on the threshold current ratio method using the measurement of coated facet reflectivity of a laser diode)

  • 이상무;김부균
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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    • pp.541-543
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    • 1995
  • We propose the improved threshold current ratio method to determine the reflectivity of coated facets. The carrier recombination time used in the improved threshold current ratio method depends on the value of facet reflectivities. However, the carrier recombination time used in the conventional threshold current ratio method is constant regardless of facet reflectivities. The difference between the results of the two methods increases as the reflectivity of a coated facet decreases.

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A New Method of Simulation Output Analysis : Threshold Bootstrap

  • Kim, Yun-Bae-
    • 한국시뮬레이션학회:학술대회논문집
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    • 한국시뮬레이션학회 1993년도 제3회 정기총회 및 추계학술발표회
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    • pp.2-2
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    • 1993
  • Inference for discrete event simulations usually relies on either independent replications or, if each simulation run is expensive, the method of batch means applied to a single replications. We present a new method, threshold bootstrap, which equals or exceeds the performance of independent replications or batch means. The method works by resampling runs of data created when a stationary time series crosses a threshold level, such as the sample mean of series. Computational results show that the threshold bootstrap matches or exceeds the performance of these alternative methods in estimating the standard deviation of the sample mean and producing valid confidence intervals.

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다중 임계값을 이용한 임베디드 제로트리 웨이블렛(EZW) 영상압축 (Embedded Zero-tree Wavelet (EZW) Image Compression Using Multi-Threshold)

  • 방민기;조창호;이상효;박종우;이종용
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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    • pp.2311-2314
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    • 2003
  • In this paper, the embedded zero-tree wavelet image compression method using multi- threshold is proposed, which can reduce the scanning and symbol redundancy of the existing embedded zero-tree wavelet (EZW) method and enable more efficient coding. In the proposed scheme, a multi-threshold is constructed with the maximum absolute values from each subband decomposed by the wavelet transforms of the input image data. The multi-threshold values are compared with the threshold value T$_1$ in each pass in Successive Approximation Quantization (SAQ) to select the significant subbands, which are only used for the subsequent coding processes, therefore, can reduce the coding redundancy in the existing EZW. By the experimental results, it is verified that the proposed multi-threshold EZW method shows superior performances to the existing EZW method.

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Detecting Anomalous Trajectories of Workers using Density Method

  • Lan, Doi Thi;Yoon, Seokhoon
    • International Journal of Internet, Broadcasting and Communication
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    • 제14권2호
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    • pp.109-118
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    • 2022
  • Workers' anomalous trajectories allow us to detect emergency situations in the workplace, such as accidents of workers, security threats, and fire. In this work, we develop a scheme to detect abnormal trajectories of workers using the edit distance on real sequence (EDR) and density method. Our anomaly detection scheme consists of two phases: offline phase and online phase. In the offline phase, we design a method to determine the algorithm parameters: distance threshold and density threshold using accumulated trajectories. In the online phase, an input trajectory is detected as normal or abnormal. To achieve this objective, neighbor density of the input trajectory is calculated using the distance threshold. Then, the input trajectory is marked as an anomaly if its density is less than the density threshold. We also evaluate performance of the proposed scheme based on the MIT Badge dataset in this work. The experimental results show that over 80 % of anomalous trajectories are detected with a precision of about 70 %, and F1-score achieves 74.68 %.

Application of Multiple Threshold Values for Accuracy Improvement of an Automated Binary Change Detection Model

  • Yu, Byeong-Hyeok;Chi, Kwang-Hoon
    • 대한원격탐사학회지
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    • 제25권3호
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    • pp.271-285
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    • 2009
  • Multi-temporal satellite imagery can be changed into a transform image that emphasizes the changed area only through the application of various change detection techniques. From the transform image, an automated change detection model calculates the optimal threshold value for classifying the changed and unchanged areas. However, the model can cause undesirable results when the histogram of the transform image is unbalanced. This is because the model uses a single threshold value in which the sign is either positive or negative and its value is constant (e.g. -1, 1), regardless of the imbalance between changed pixels. This paper proposes an advanced method that can improve accuracy by applying separate threshold values according to the increased or decreased range of the changed pixels. It applies multiple threshold values based on the cumulative producer's and user's accuracies in the automated binary change detection model, and the analyst can automatically extract more accurate optimal threshold values. Multi-temporal IKONOS satellite imagery for the Daejeon area was used to test the proposed method. A total of 16 transformation results were applied to the two study sites, and optimal threshold values were determined using accuracy assessment curves. The experiment showed that the accuracy of most transform images is improved by applying multiple threshold values. The proposed method is expected to be used in various study fields, such as detection of illegal urban building, detection of the damaged area in a disaster, etc.