scholarly journals COINTEGRATION ANALYSIS METHOD FOR FAULT DETECTION BASED ON SENSOR DATA

2020 ◽  
pp. 49-64
Author(s):  
Rinat Faizullin ◽  
◽  
Stefan Hering ◽  
Keyword(s):  
Cointegration Analysis ◽  
Sensor Data ◽  
Automatic Process ◽  
Normal System ◽  
Time Interval ◽  
Random Component ◽  
Data Set ◽  
Tennessee Eastman Process ◽  
Multidimensional Time Series

Sensors are a popular source of information about the operation of complex dynamic technical systems. Considering data from sensors as a multidimensional time series is also used to describe cyber-physical systems. The article proposes a method for detecting system malfunctions based on the method of analyzing cointegration dependencies. It is determined that in the data for analysis it is possible to reveal cointegration dependences as facts of interdependence of data from different sensors. Calculations are given on the example of a system with 52 parameters. Out of 1,326 data pairs, 75 are cointegrated. The conducted analysis shows that the proposed method enables one to clearly illustrate situations with changes in behavior. Having identified cointegrated pairs, we can follow them, and if cointegration has ‘disappeared’, that is, at some new time interval we can no longer talk about the presence of a cointegration ratio, then something has changed in the process itself. In practice, this means either a change in technology (which the operator knows about), or a breakdown/accident/failure, due to equipment errors, changes in some parameters of the resources used. In the latter case, such information (that the process has changed) can be used to attract attention in general, which may ultimately lead to the need for equipment repair or maintenance or readjustment, etc. The analysis shows that the proposed method enables one to clearly illustrate situations with changes in behavior. As an example of using the method, we used the ready-made Tennessee Eastman Process (TEP) data set. Different pairs of data may have the ability to identify different errors. All errors cause a change in the behavior of one or several pairs of data, thus tracking the behavior of the value of random component enables identifying cases of deviation of the process from long-term equilibrium (in terms of cointegration), that is, cases of failure from the normal system operation. The results obtained are clear and objective and can be used by process operators or by a source for automatic process control.

scholarly journals A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
Author(s):  
Hanbo Yang ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Zheng Sun ◽  
Xuesong Mei
Keyword(s):  
Time Windows ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Time Interval ◽  
Kernel Module ◽  
Initial Stage ◽  
Research Task ◽  
Consecutive Time ◽  
Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

scholarly journals A 2-decade (1988–2009) record of diatom fluxes in the Mauritanian coastal upwelling: impact of low-frequency forcing and a two-step shift in the species composition

2021 ◽  
Vol 18 (5) ◽  
pp. 1873-1891
Author(s):  
Oscar E. Romero ◽  
Simon Ramondenc ◽  
Gerhard Fischer
Keyword(s):  
Coastal Waters ◽  
Temporal Dynamics ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Benthic Diatoms ◽  
Time Interval ◽  
Data Set ◽  
The Impact

Abstract. Eastern boundary upwelling ecosystems (EBUEs) are among the most productive marine regions in the world's oceans. Understanding the degree of interannual to decadal variability in the Mauritania upwelling system is crucial for the prediction of future changes of primary productivity and carbon sequestration in the Canary Current EBUE as well as in similar environments. A multiyear sediment trap experiment was conducted at the mooring site CBmeso (“Cape Blanc mesotrophic”, ca. 20∘ N, ca. 20∘40′ W) in the highly productive coastal waters off Mauritania. Here, we present results on fluxes of diatoms and the species-specific composition of the assemblage for the time interval between March 1988 and June 2009. The temporal dynamics of diatom populations allows the proposal of three main intervals: (i) early 1988–late 1996, (ii) 1997–1999, and (iii) early 2002–mid 2009. The Atlantic Multidecadal Oscillation (AMO) appears to be an important driver of the long-term dynamics of diatom population. The long-term AMO-driven trend is interrupted by the occurrence of the strong 1997 El Niño–Southern Oscillation (ENSO). The extraordinary shift in the relative abundance of benthic diatoms in May 2002 suggests the strengthening of offshore advective transport within the uppermost layer of filament waters and in the subsurface and in deeper and bottom-near layers. It is hypothesized that the dominance of benthic diatoms was the response of the diatom community to the intensification of the slope and shelf poleward undercurrents. This dominance followed the intensification of the warm phase of AMO and the associated changes of the Atlantic Meridional Overturning Circulation. Transported valves (siliceous remains) from shallow Mauritanian coastal waters into the bathypelagic should be considered for the calculation and model experiments of bathy- and pelagic nutrients budgets (especially Si), the burial of diatoms, and the paleoenvironmental signal preserved in downcore sediments. Additionally, our 1988–2009 data set contributes to the characterization of the impact of low-frequency climate forcings in the northeastern Atlantic and will be especially helpful for establishing the scientific basis for forecasting and modeling future states of the Canary Current EBUE and its decadal changes.

scholarly journals Open Education Resource for School Children with Down Syndrome

2019 ◽  
Vol 8 (4) ◽  
pp. 11945-11948
Keyword(s):  
Down Syndrome ◽  
Action Recognition ◽  
Time Interval ◽  
Camera Motion ◽  
Data Set ◽  
Benchmark Data ◽  
Lstm Network ◽  
Education Resource ◽  
Unified Method

Action recognition in video sequences is a challenging problem of computer vision due to the similarity of visual contents, changes in the viewpoint for the same actions, camera motion with action performer, scale and pose of an actor, and different illumination conditions. Also, there is no designated action recognition model for hazy videos. This model proposes a novel unified and unique model for action recognition in haze built with Convolutional Neural Network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, every frame of the hazy video is feed into the AOD-Net (All-in-One Dehazing Network). Next, deep features are extracted from every sampled dehazed frame by using VGG-16, which helps reduce the redundancy and complexity. Later, the sequential and temporal information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both the forward and backward passes of DB-LSTM to increase its depth. The proposed unified method is capable of learning long term sequences and can process lengthy videos (even hazy videos) in real time by analyzing features for a certain time interval. Experimental results on both synthesized and natural video datasets show decent results on par with other state of the art methods in action recognition using the proposed method on the benchmark data set UCF-101. This helps the Down Syndrome Students to recognize an action faster.

Pollution load modelling in sewer systems: an approach of combining long term online sensor data with multi-objective auto-calibration schemes

2009 ◽  
Vol 59 (1) ◽  
pp. 73-79 ◽  
Author(s):  
V. Gamerith ◽  
D. Muschalla ◽  
P. Könemann ◽  
G. Gruber
Keyword(s):  
Management Strategies ◽  
Calibration Method ◽  
Sensor Data ◽  
Calibration Data ◽  
Validation Data ◽  
Data Set ◽  
Pollutant Load ◽  
Multi Objective ◽  
Sewer Systems

Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two surface accumulation–wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. Results from multi-objective calibration show better robustness in validation events than single-objective calibration. The build-up wash-off approach using the basic wash-off equation gives the best correlations between measured data and simulation results.

scholarly journals A two-decades (1988–2009) record of diatom fluxes in the Mauritanian coastal upwelling: Impact of low-frequency forcing and a two-step shift in the species composition

2020 ◽  
Author(s):  
Oscar E. Romero ◽  
Simon Ramondenc ◽  
Gerhard Fischer
Keyword(s):  
Coastal Waters ◽  
Temporal Dynamics ◽  
Meridional Overturning Circulation ◽  
Benthic Diatoms ◽  
Time Interval ◽  
Data Set ◽  
Specific Composition ◽  
Species Specific ◽  
The Impact

Abstract. Eastern Boundary Upwelling Ecosystems (EBUEs) are among the most productive marine regions in the world's oceans. Understanding the degree of interannual to decadal variability in the Mauritania upwelling system is key for the prediction of future changes of primary productivity and carbon sequestration in the Canary Current EBUE as well as in similar environments. A multiannual sediment trap experiment was conducted at the mooring site CBmeso (= 'Cape Blanc mesotrophic', ca. 20° N, ca. 20°40' W) in the high-productive Mauritanian coastal waters. Here, we present results on fluxes and the species-specific composition of the diatom assemblage for the time interval between March 1988 and May 2009. The temporal dynamics of diatom populations allows to propose three main diatom productivity/flux intervals: (i) early 1988–late 1996; (ii) 1997–1999, and (iii) early 2002–mid 2009. The impact of the Atlantic Multidecadal Oscillation appears to be an important forcing of the long-term dynamics of diatom population. The impact of cold (1988–1996) and warm AMO phases (2001–2009) is reflected by the outstanding shifts in species-specific composition. This AMO-impacted, long-term trend is interrupted by the occurrence of the strong 1997 ENSO. The extraordinary shift in the relative abundance of benthic diatoms in May 2002 suggests the strengthening of offshore advective transport within the uppermost layer of filament waters, and in the subsurface and in deeper and bottom‐near layers. It is hypothesized that the dominance of benthic diatoms was the response of the diatom community to the intensification of the slope and shelf poleward undercurrents. This dominance followed the intensification of the warm phase of AMO and the associated changes of the Atlantic Meridional Overturning Circulation. Transported valves (siliceous remains) from shallow coastal waters into the deeper bathypelagial should be considered for the calculation and model experiments of bathy- and pelagial nutrients budgets (especially Si), the burial of diatoms and the paleoenvironmental signal preserved in downcore sediments. Our 1988–2009 data set contributes to the distinction between climate-forced and intrinsic variability of populations of diatoms and will be especially helpful for establishing the scientific basis for forecasting and modelling future states of this ecosystem and its decadal changes.

scholarly journals Noctilucent clouds observed from the UK and Denmark – trends and variations over 43 years

2008 ◽  
Vol 26 (5) ◽  
pp. 1243-1254 ◽  
Author(s):  
S. Kirkwood ◽  
P. Dalin ◽  
A. Réchou
Keyword(s):  
Time Interval ◽  
Noctilucent Cloud ◽  
Data Set ◽  
Statistical Probability ◽  
Zonal Winds ◽  
Increasing Trend ◽  
The Uk ◽  
Long Term Trend ◽  
10.7 Cm Solar Flux

Abstract. The combined UK/Denmark record of noctilucent cloud (NLC) observations over the period 1964–2006 is analysed. This data set is based on visual observations by professional and voluntary observers, with around 40 observers each year contributing reports. Evidence is found for a significantly longer NLC season, a greater frequency of bright NLC, and a decreased sensitivity to 5-day planetary waves, from 1973–1982, compared to the rest of the time interval. This coincides with a period when the length of the summer season in the stratosphere was also longer (defined by zonal winds at 60° N, 30 hPa). At NLC heights, lower mean temperatures, and/or higher water vapour and/or smaller planetary wave amplitudes could explain these results. The time series of number of NLC nights each year shows a quasi-decadal variation with good anti-correlation with the 10.7 cm solar flux, with a lag of 13–17 months. Using multi-parameter linear fitting, it is found that the solar-cycle and the length of summer in the stratosphere together can explain ~40% of the year-to-year variation in NLC numbers. However, no statistically confidant long-term trend in moderate or bright NLC is found. For NLC displays of moderate or greater intensity, the multi-parameter fit gives a trend of ~0.08 nights (0.35%) per year with a statistical probability of 28% that it is zero, or as high as 0.16 nights (0.7%) per year. There is a significant increasing trend in the number of reports of faint or very faint NLC which is inconsistent with other observations and may be due changes in observing practices.

scholarly journals An investigation of demand and exchange rate shocks in the tourism sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kurtulus Bozkurt ◽  
Hatice Armutçuoğlu Tekin ◽  
Zeliha Can Ergün
Keyword(s):  
Exchange Rate ◽  
Panel Cointegration ◽  
Cointegration Analysis ◽  
Causality Test ◽  
Cross Sectional ◽  
Data Set ◽  
Demand Shocks ◽  
Content Type ◽  
Exchange Rate Shocks

Purpose This study aims to measure the relationship between demand and exchange rate shocks in the tourism industry. Design/methodology/approach A panel data set is constructed covering the period between 1995 and 2017, and the data set includes the top 26 countries that host 10 million tourists and above in the world as of 2017. The standard errors of the series are used as an indicator of shocks. First, the cross-sectional dependency, stationarity and the homogeneity of the series are examined; second, a panel cointegration analysis is implemented; third, long-term panel cointegration coefficients are analyzed with Dynamic Common Correlated Effects (DCCE) approach; and, finally, Dumitrescu and Hurlin’s (2012) Granger non-causality test is used to detect the causality. Findings The preliminary analyses show that the variables are cross-sectional dependent and heterogeneous and are stationary in their first difference; hence, the effects of the shocks are temporary. On the other hand, as a result of the panel cointegration analysis, it is found that both series are cointegrated over the long-term. However, the long-term coefficients estimated with the DCCE approach are found not to be statistically significant. Finally, as a result of the Dumitrescu and Hurlin’s (2012) Granger non-causality test, it is concluded that there is a causality running from exchange rate shocks to demand shocks. Originality/value To the best of the authors’ knowledge, the cointegration between the tourism demand shocks and exchange rates shocks has not been investigated before, and therefore, this study is considered to be a pioneering study that will contribute to the literature.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

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