Entropy-based Concept Shift Detection

2006 ◽  
Author(s):  
Peter Vorburger ◽  
Abraham Bernstein
Keyword(s):  
Shift Detection ◽  
Concept Shift

scholarly journals Streamflow input to Lake Athabasca, Canada

2013 ◽  
Vol 17 (5) ◽  
pp. 1681-1691 ◽  
Author(s):  
K. Rasouli ◽  
M. A. Hernández-Henríquez ◽  
S. J. Déry
Keyword(s):  
Oil Sands ◽  
Multiple Stressors ◽  
Sediment Cores ◽  
Lake Basin ◽  
Ramsar Convention ◽  
Athabasca River ◽  
Oil Reserves ◽  
Mk Test ◽  
Streamflow Variability ◽  
Shift Detection

Abstract. The Lake Athabasca drainage area in northern Canada encompasses ecologically rich and sensitive ecosystems, vast forests, glacier-clad mountains, and abundant oil reserves in the form of oil sands. The basin includes the Peace–Athabasca Delta, recognized internationally by UNESCO and the Ramsar Convention as a biologically rich inland delta and wetland that are now under increasing pressure from multiple stressors. In this study, streamflow variability and trends for rivers feeding Lake Athabasca are investigated over the last half century. Hydrological regimes and trends are established using a robust regime shift detection method and the Mann–Kendall (MK) test, respectively. Results show that the Athabasca River, which is the main contributor to the total lake inflow, experienced marked declines in recent decades impacting lake levels and its ecosystem. From 1960 to 2010 there was a significant reduction in lake inflow and a significant recession in the Lake Athabasca level. Our trend analysis corroborates a previous study using proxy data obtained from nearby sediment cores suggesting that the lake level may drop 2 to 3 m by 2100. The lake recession may threaten the flora and fauna of the Athabasca Lake basin and negatively impact the ecological cycle of an inland freshwater delta and wetland of global importance.

scholarly journals Tm-Shift Detection of Dog-Derived Ancylostoma ceylanicum and A. caninum

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yeqi Fu ◽  
Mingwei Wang ◽  
Xinxin Yan ◽  
Auwalu Yusuf Abdullahi ◽  
Jianxiong Hang ◽  
...  
Keyword(s):  
Ancylostoma Ceylanicum ◽  
Shift Method ◽  
Accuracy Test ◽  
Zoonotic Risk ◽  
Detectable Concentration ◽  
Positive Rate ◽  
Clinical Detection ◽  
Shift Detection ◽  
The Stability ◽  
Detection Effect

To develop a Tm-shift method for detection of dog-derived Ancylostoma ceylanicum and A. caninum, three sets of primers were designed based on three SNPs (ITS71, ITS197, and ITS296) of their internal transcribed spacer 1 (ITS1) sequences. The detection effect of the Tm-shift was assessed through the stability, sensitivity, accuracy test, and clinical detection. The results showed that these three sets of primers could distinguish accurately between A. ceylanicum and A. caninum. The coefficient of variation in their Tm values on the three SNPs was 0.09% and 0.15% (ITS71), 0.18% and 0.14% (ITS197), and 0.13% and 0.07% (ITS296), respectively. The lowest detectable concentration of standard plasmids for A. ceylanicum and A. caninum was 5.33 × 10−6 ng/μL and 5.03 × 10−6 ng/μL. The Tm-shift results of ten DNA samples from the dog-derived hookworms were consistent with their known species. In the clinical detection of 50 fecal samples from stray dogs, the positive rate of hookworm detected by Tm-shift (42%) was significantly higher than that by microscopic examination (34%), and the former can identify the Ancylostoma species. It is concluded that the Tm-shift method is rapid, specific, sensitive, and suitable for the clinical detection and zoonotic risk assessment of the dog-derived hookworm.

Process Mean Shift Detection Using Prediction Error Analysis

1998 ◽  
Vol 120 (3) ◽  
pp. 489-495 ◽  
Author(s):  
S. J. Hu ◽  
Y. G. Liu
Keyword(s):  
Error Analysis ◽  
Prediction Error ◽  
Control Charts ◽  
Mean Shift ◽  
Measurement Data ◽  
Step Function ◽  
False Alarms ◽  
Prediction Errors ◽  
Function Type ◽  
Shift Detection

Autocorrelation in 100 percent measurement data results in false alarms when the traditional control charts, such as X and R charts, are applied in process monitoring. A popular approach proposed in the literature is based on prediction error analysis (PEA), i.e., using time series models to remove the autocorrelation, and then applying the control charts to the residuals, or prediction errors. This paper uses a step function type mean shift as an example to investigate the effect of prediction error analysis on the speed of mean shift detection. The use of PEA results in two changes in the 100 percent measurement data: (1) change in the variance, and (2) change in the magnitude of the mean shift. Both changes affect the speed of mean shift detection. These effects are model parameter dependent and are obtained quantitatively for AR(1) and ARMA(2,1) models. Simulations and examples from automobile body assembly processes are used to demonstrate these effects. It is shown that depending on the parameters of the AMRA models, the speed of detection could be increased or decreased significantly.

scholarly journals Quantum limits on phase-shift detection using multimode interferometers

2003 ◽  
Vol 67 (5) ◽  
Author(s):  
Jonas Söderholm ◽  
Gunnar Björk ◽  
Björn Hessmo ◽  
Shuichiro Inoue
Keyword(s):  
Phase Shift ◽  
Shift Detection ◽  
Quantum Limits

scholarly journals Data-driven Adaptive Thresholding Model for Real-time Valve Delay Estimation in Digital Pump/Motors

2020 ◽  
Author(s):  
Abdallah Chehade ◽  
Farid Breidi ◽  
Keith Scott Pate ◽  
John Lumkes
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Domain Knowledge ◽  
Operating Conditions ◽  
Delay Model ◽  
Hydraulic Systems ◽  
Pressure Transducers ◽  
Statistical Behavior ◽  
Shift Detection ◽  
And Performance

Valve characteristics are an essential part of digital hydraulics. The on/off solenoid valves utilized on many of these systems can significantly affect the performance. Various factors can affect the speed of the valves causing them to experience various delays, which impact the overall performance of hydraulic systems. This work presents the development of an adaptive statistical based thresholding real-time valve delay model for digital Pump/Motors. The proposed method actively measures the valve delays in real-time and adapts the threshold of the system with the goal of improving the overall efficiency and performance of the system. This work builds on previous work by evaluating an alternative method used to detect valve delays in real-time. The method used here is a shift detection method for the pressure signals that utilizes domain knowledge and the system’s historical statistical behavior. This allows the model to be used over a large range of operating conditions, since the model can learn patterns and adapt to various operating conditions using domain knowledge and statistical behavior. A hydraulic circuit was built to measure the delay time experienced from the time the signal is sent to the valve to the time that the valve opens. Experiments were conducted on a three piston in-line digital pump/motor with 2 valves per cylinder, at low and high pressure ports, for a total of six valves. Two high frequency pressure transducers were used in this circuit to measure and analyze the differential pressure on the low and high pressure side of the on/off valves, as well as three in-cylinder pressure transducers. Data over 60 cycles was acquired to analyze the model against real time valve delays. The results show that the algorithm was successful in adapting the threshold for real time valve delays and accurately measuring the valve delays. 

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