A dynamic threshold method of clustering update messages into routing events in BGP measurements

2007 ◽  
Author(s):  
Xia Yin ◽  
Min Tang ◽  
Zhiliang Wang
Keyword(s):  
Dynamic Threshold ◽  
Threshold Method

scholarly journals Improved Dynamic Threshold Method for Skin Colour Detection Using Multi-Colour Space

2016 ◽  
Vol 13 (2) ◽  
pp. 135-144 ◽  
Author(s):  
Mohd Zamri Osman ◽  
Mohd Aizaini Maarof ◽  
Mohd Foad Rohani
Keyword(s):  
Skin Colour ◽  
Dynamic Threshold ◽  
Threshold Method ◽  
Colour Space

Dynamic Threshold Method Based Aircraft Engine Sensor Fault Diagnosis

2008 ◽  
Author(s):  
Wenfei Li ◽  
Rama K. Yedavalli
Keyword(s):  
Fault Diagnosis ◽  
Robustness Analysis ◽  
Aircraft Engine ◽  
Upper And Lower Bounds ◽  
Dynamic Threshold ◽  
False Alarms ◽  
Parametric Uncertainties ◽  
State Variables ◽  
Sensor Fault ◽  
Threshold Method

It is challenging to have a good fault diagnostic scheme that can distinguish between model uncertainties and occurrence of faults, which helps in reducing false alarms and missed detections. In this paper, a dynamic threshold algorithm is developed for aircraft engine sensor fault diagnosis that accommodates parametric uncertainties. Using the robustness analysis of parametric uncertain systems, we generate upper-and-lower bound trajectories for the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the perturbed eigenvalues reside in a set of distinct circular regions. Dedicated observer scheme is used for engine sensor fault diagnosis design. The residuals are errors between estimated state variables from a bank of Kalman filters. With this design approach, the residual crossing the upper-and-lower bounds of the dynamic threshold indicates the occurrence of fault. Application to an aircraft gas turbine engine Component Level Model clearly illustrates the improved performance of the proposed method.

The Research on Hand Gestures Segmentation Based on Skin Color Detection Combination with Dynamic Threshold

2013 ◽  
Vol 756-759 ◽  
pp. 1938-1942 ◽  
Author(s):  
Yi Ting Wang ◽  
Feng Jing Shao
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Color Segmentation ◽  
Dynamic Threshold ◽  
Background Color ◽  
Threshold Method ◽  
Hand Gestures ◽  
Skin Color Detection ◽  
Color Detection ◽  
Rgb Color Space

Aiming at the shortcomings of hand gestures segmentation based on fixed threshold and the problem of the interference of background color, this paper proposes a research on hand gestures segmentation based on skin color detection. The captured image is translated to YCgCr color space from RGB color space, and then skin color segmentation is done by using dynamic threshold method, so the hand gestures segmentation is completed. Finally segmentation results are verified by experiments and the method is summarized.

scholarly journals Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2262
Author(s):  
Donghyun Jin ◽  
Sung-Rae Chung ◽  
Kyeong-Sang Lee ◽  
Minji Seo ◽  
Sungwon Choi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Brightness Temperature ◽  
Detection Algorithm ◽  
Japan Meteorological Agency ◽  
Dynamic Threshold ◽  
Threshold Method ◽  
Ground Truth Data ◽  
Geostationary Satellites ◽  
Observation Area ◽  
Ice Detection

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.

scholarly journals The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method

2019 ◽  
Vol 11 (23) ◽  
pp. 2725 ◽  
Author(s):  
Huang ◽  
Liu ◽  
Zhu ◽  
Atzberger ◽  
Liu
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Original Method ◽  
Dynamic Threshold ◽  
Threshold Method ◽  
Summer Maize ◽  
Crop Phenology ◽  
Optimal Thresholds ◽  
Index Time Series ◽  
Late Rice

Crop phenology is an important parameter for crop growth monitoring, yield prediction, and growth simulation. The dynamic threshold method is widely used to retrieve vegetation phenology from remotely sensed vegetation index time series. However, crop growth is not only driven by natural conditions, but also modified through field management activities. Complicated planting patterns, such as multiple cropping, makes the vegetation index dynamics less symmetrical. These impacts are not considered in current approaches for crop phenology retrieval based on the dynamic threshold method. Thus, this paper aimed to (1) investigate the optimal thresholds for retrieving the start of the season (SOS) and the end of the season (EOS) of different crops, and (2) compare the performances of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in retrieving crop phenology with a modified version of the dynamic threshold method. The reference data included SOS and EOS ground observations of three major crop types in 2015 and 2016, which includes rice, wheat, and maize. Results show that (1) the modification of the original method ensures a 100% retrieval rate, which was not guaranteed using the original method. The modified dynamic threshold method is more suitable to retrieve crop SOS/EOS because it considers the asymmetry of crop vegetation index time series. (2) It is inappropriate to retrieve SOS and EOS with the same threshold for all crops, and the commonly used 20% or 50% thresholds are not the optimal thresholds for all crops. (3) For single and late rice, the accuracies of the SOS estimations based on EVI are generally higher compared to those based on NDVI. However, for spring maize and summer maize, results based on NDVI give higher accuracies. In terms of EOS, for early rice and summer maize, estimates based on EVI result in higher accuracies, but, for late rice and winter wheat, results based on NDVI are closer to the ground records.

scholarly journals A new method of electrodes placement to improve QRS detection in real-time stress ECG acquisition

2015 ◽  
Vol 18 (2) ◽  
pp. 159-163
Author(s):  
Dang Cao Le ◽  
Tan Hoang Nguyen ◽  
Nam Hoai Phan ◽  
Quoc Minh Thai
Keyword(s):  
Real Time ◽  
New Method ◽  
Dynamic Threshold ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Threshold Method ◽  
Baseline Drift ◽  
Different Types ◽  
Detecting Method ◽  
Real Time Application

In dynamic threshold method to detect QRS complex from ECG signal, especially in real-time application, there are two main issues: baseline drift and noise. This paper introduces an improved QRS complex detecting method using dynamic threshold algorithm combined with a new method of electrodes placement to minimize baseline drift and different types of noise in real-time ECG acquisition with moving patients. Our method proved to be more effective in detecting QRS complex with less error due to minimized baseline drift and noise in original ECG signal.

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