Predictive Usage Mining for Sustainability of Complex Systems Design

2014 ◽  
Author(s):  
Jungmok Ma ◽  
Harrison M. Kim
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Systems Design ◽  
Sensor Data ◽  
Period Analysis ◽  
Dynamic Lca ◽  
Segmentation Analysis ◽  
Usage Patterns ◽  
New Perspective

A new perspective of dynamic LCA (life cycle assessment) is proposed with the predictive usage mining for sustainability (PUMS) algorithm. By defining usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon. Large-scale sensor data of product operation is analyzed in order to mine usage patterns and build a usage model for LCA. The PUMS algorithm consists of handling missing and abnormal values, seasonal period analysis, segmentation analysis, time series analysis, and predictive LCA. A newly developed segmentation algorithm can distinguish low activity periods and help to capture patterns more clearly. Furthermore, a predictive LCA method is formulated using a time series usage model. Finally, generated data is used to do predictive LCA of agricultural machinery as a case study.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

scholarly journals Simple IoT simulation

2019 ◽  
Vol 9 (1) ◽  
pp. 3328-3330
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Random Number Generator ◽  
Power Supplies ◽  
Sensor Data ◽  
Series Data ◽  
Scaled Model ◽  
True Time ◽  
Server Architecture

While analyzing iot projects it is very expensive to buy a lot of sensors , corresponding processor boards, power supplies etc. Moreover the entire process is to be replicated to cater to large topologies. The whole experiment is to be planned at a large scale before we can actually start to see analytics working. At a smaller scale this can be implemented as a simulation program in linux where the sensor data is created using a random number generator and scaled appropriately for each type of sensor to mimic representative data. This is them encrypted before sending it over the network to the edge nodes. At the server a socket stream now continuously awaits sensor data Here the required sensor data is retrieved and decrypted to give true time series data. This time series is now given to an analytics engine which calculates the trends and cyclicity and is used to train a neural network. The anomalies so found are properly deciphered. The multiplicity of the nodes can be characterized by having several client programs running in separate terminals. A simple client server architecture is thus able to simulate a large iot infrastructure and is able to perform analytics on a scaled model

scholarly journals Investigating the Time Series OLI multi Spectral Sensor Data Capability with the Step by Step Method for Producing the Major Cultivation Map (A Case Study: Chadegan City)

2018 ◽  
Vol 22 (3) ◽  
pp. 71-80
Author(s):  
V. Rahdari ◽  
A. R. Soffianian ◽  
S. Pourmanafi ◽  
H. Ghaiumi Mohammadi ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Sensor Data ◽  
Step Method

scholarly journals Animated Interval Scatter-Plot Views for the Exploratory Analysis of Large-Scale Microarray Time-Course Data

2005 ◽  
Vol 4 (3) ◽  
pp. 149-163 ◽  
Author(s):  
Paul Craig ◽  
Jessie Kennedy ◽  
Andrew Cumming
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Course ◽  
Time Frame ◽  
Scatter Plot ◽  
Treatment And Prevention ◽  
Microarray Time Course ◽  
Significant Patterns ◽  
New Perspective ◽  
Effective Analysis

Microarray technologies are a relatively new development that allow biologists to monitor the activity of thousands of genes (normally around 8,000) in parallel across multiple stages of a biological process. While this new perspective on biological functioning is recognised as having the potential to have a significant impact on the diagnosis, treatment, and prevention of diseases, it is only through effective analysis of the data produced that biologists can begin to unlock this potential. A significant obstacle to achieving effective analysis of microarray time-course is the combined scale and complexity of the data. This inevitably makes it difficult to reveal certain significant patterns in the data. In particular, it is less dominant patterns and, specifically, patterns that occur over smaller intervals of an experiment's overall time-frame that are more difficult to find. While existing techniques are capable of finding either unexpected patterns of activity over the majority of an experiment's time-frame or expected patterns of activity over smaller intervals of the time-frame, there are no techniques, or combination of techniques, that are suitable for finding unsuspected patterns of activity over smaller intervals. In order to overcome this limitation we have developed the Time-series Explorer, which specifically supports biologists in their attempts to reveal these types of pattern by allowing them to control an animated interval scatter-plot view of their data. This paper discusses aspects of the technique that make such an animated overview viable and describes the results of a user evaluation assessing the practical utility of the technique within the wider context of microarray time-series analysis as a whole.

scholarly journals INTEGRATING SOCIAL IMPACT MEASURES INTO THE AIRSHIP DESIGN PROCESS

2021 ◽  
Vol 1 ◽  
pp. 1877-1886
Author(s):  
D.C. Richards ◽  
Phillip D. Stevenson ◽  
Christopher A. Mattson ◽  
John L. Salmon
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Social Impact ◽  
Systems Design ◽  
Social Impacts ◽  
Design Parameters ◽  
Metric Analysis ◽  
The Social ◽  
Local Farmers

AbstractEngineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to determine the interaction between design parameters when the social impacts are incorporated into the design process. Social impact evaluation is increasing in importance similar to what has happened with environmental impact consideration in recent years in the design of engineered products. Concurrently, research into new airship design has increased, however airships have yet to be reintroduced at a large scale and for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon to help local farmers transport their produce to market. It explores the design space in terms of the airship's social impacts connected to the design parameters. The social impacts are found to be dependent not only on the social factors and airship design parameters, but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis.

scholarly journals A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

2020 ◽  
Vol 57 (8) ◽  
pp. 1102-1124
Author(s):  
M. Mahdianpari ◽  
H. Jafarzadeh ◽  
J. E. Granger ◽  
F. Mohammadimanesh ◽  
B. Brisco ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Scale Change ◽  
Large Scale Change ◽  
Google Earth Engine ◽  
Change Monitoring

scholarly journals Assessing local daily temperatures by means of novel analog approaches: a case study based on the city of Augsburg, Germany

2021 ◽  
Author(s):  
Christian Merkenschlager ◽  
Stephanie Koller ◽  
Christoph Beck ◽  
Elke Hertig
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Modeling ◽  
Selection Process ◽  
Urban Climate ◽  
Domain Size ◽  
Normal Vector ◽  
Circulation Patterns ◽  
The City

AbstractWithin the scope of urban climate modeling, weather analogs are used to downscale large-scale reanalysis-based information to station time series. Two novel approaches of weather analogs are introduced which allow a day-by-day comparison with observations within the validation period and which are easily adaptable to future periods for projections. Both methods affect the first level of analogy which is usually based on selection of circulation patterns. First, the time series were bias corrected and detrended before subsamples were determined for each specific day of interest. Subsequently, the normal vector of the standardized regression planes (NVEC) or the center of gravity (COG) of the normalized absolute circulation patterns was used to determine a point within an artificial coordinate system for each day. The day(s) which exhibit(s) the least absolute distance(s) between the artificial points of the day of interest and the days of the subsample is/are used as analog or subsample for the second level of analogy, respectively. Here, the second level of analogy is a second selection process based on the comparison of gridded temperature data between the analog subsample and the day of interest. After the analog selection process, the trends of the observation were added to the analog time series. With respect to air temperature and the exceedance of the 90th temperature quantile, the present study compares the performance of both analog methods with an already existing analog method and a multiple linear regression. Results show that both novel analog approaches can keep up with existing methods. One shortcoming of the methods presented here is that they are limited to local or small regional applications. In contrast, less pre-processing and the small domain size of the circulation patterns lead to low computational costs.

scholarly journals Data analysis toolkit for long-term, large-scale experiments

2012 ◽  
Vol 76 (8) ◽  
pp. 3355-3364 ◽  
Author(s):  
D. P. Bennett ◽  
R. J. Cuss ◽  
P. J. Vardon ◽  
J. F. Harrington ◽  
R. N. Philp ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Event Detection ◽  
Large Scale ◽  
Scientific Information ◽  
Frequency Component ◽  
Series Component

AbstractA new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/event detection and characterization/parameterization of local behaviours. An application is presented of a case study dataset arising from the 'Lasgit' experiment.

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