scholarly journals DATA ANALYSIS OF GRAVITATIONAL WAVES USING A NETWORK OF DETECTORS

2008 ◽  
Vol 17 (07) ◽  
pp. 1095-1104 ◽  
Author(s):  
LINQING WEN
Keyword(s):  
Source Localization ◽  
Large Scale ◽  
Design Sensitivity ◽  
Singular Value ◽  
Consistency Check ◽  
Term Operation ◽  
Stable Solutions ◽  
Value Decomposition ◽  
Svd Method

Several large-scale gravitational wave (GW) interferometers have achieved long term operation at design sensitivity. Questions arise on how to best combine all available data from detectors of different sensitivities for detection, consistency check or veto, localization and waveform extraction. We show that these problems can be formulated using the singular value decomposition (SVD)1 method. We present techniques based on the SVD method for (1) detection statistic, (2) stable solutions to waveforms, (3) null-stream construction for an arbitrary number of detectors, and (4) source localization for GWs of unknown waveforms.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

scholarly journals Introduction to Matrix Factorization for Recommender Systems

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Singular Value Decomposition ◽  
Online Education ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Large Scale ◽  
Singular Value ◽  
Factorization Algorithms ◽  
Value Decomposition ◽  
Interaction History

Recommender systems aim to personalize the experience of user by suggesting items to the user based on the preferences of a user. The preferences are learned from the user’s interaction history or through explicit ratings that the user has given to the items. The system could be part of a retail website, an online bookstore, a movie rental service or an online education portal and so on. In this paper, I will focus on matrix factorization algorithms as applied to recommender systems and discuss the singular value decomposition, gradient descent-based matrix factorization and parallelizing matrix factorization for large scale applications.

scholarly journals Satellite image classification using proposed singular value decomposition method

2019 ◽  
Vol 13 (28) ◽  
pp. 52-67
Author(s):  
Noor Zubair Kouder
Keyword(s):  
Singular Value Decomposition ◽  
Classification Accuracy ◽  
Satellite Image ◽  
Field Work ◽  
Mean Value ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Erdas Imagine ◽  
Value Decomposition ◽  
Svd Method

In this work, satellite images for Razaza Lake and the surrounding areadistrict in Karbala province are classified for years 1990,1999 and2014 using two software programming (MATLAB 7.12 and ERDASimagine 2014). Proposed unsupervised and supervised method ofclassification using MATLAB software have been used; these aremean value and Singular Value Decomposition respectively. Whileunsupervised (K-Means) and supervised (Maximum likelihoodClassifier) method are utilized using ERDAS imagine, in order to getmost accurate results and then compare these results of each methodand calculate the changes that taken place in years 1999 and 2014;comparing with 1990. The results from classification indicated thatwater and hills are decreased, while vegetation, wet land and barrenland are increased for years 1999 and 2014; comparable with 1990.The classification accuracy was done by number of random pointschosen on the study area in the field work and geographical data thencompared with the classification results, the classification accuracy forthe proposed SVD method are 92.5%, 84.5% and 90% for years1990,1999,2014, respectivety, while the classification accuracies forunsupervised classification method based mean value are 92%, 87%and 91% for years 1990,1999,2014 respectivety.

Numerical Modelling of a District Scale Groundwater Heat Pump Operation: Case Study from Colchester, UK

2021 ◽  
Author(s):  
Taha Sezer ◽  
Abubakar Kawuwa Sani ◽  
Rao Martand Singh ◽  
David P. Boon
Keyword(s):  
Large Scale ◽  
District Level ◽  
Thermal Plume ◽  
Injection Wells ◽  
Term Operation ◽  
Heating And Cooling ◽  
Groundwater Heat Pump ◽  
The Impact

<p>Groundwater heat pumps (GWHP) are an environmentally friendly and highly efficient low carbon heating technology that can benefit from low-temperature groundwater sources lying in the shallow depths to provide heating and cooling to buildings. However, the utilisation of groundwater for heating and cooling, especially in large scale (district level), can create a thermal plume around injection wells. If a plume reaches the production well this may result in a decrease in the system performance or even failure in the long-term operation. This research aims to investigate the impact of GWHP usage in district-level heating by using a numerical approach and considering a GWHP system being constructed in Colchester, UK as a case study, which will be the largest GWHP system in the UK. Transient 3D simulations have been performed pre-construction to investigate the long-term effect of injecting water at 5°C, into a chalk bedrock aquifer. Modelling suggests a thermal plume develops but does not reach the production wells after 10 years of operation. The model result can be attributed to the low hydraulic gradient, assumed lack of interconnecting fractures, and large (>500m) spacing between the production and injection wells. Model validation may be possible after a period operational monitoring.</p>

Recommender System in the Context of Big Data

2016 ◽  
pp. 231-246
Author(s):  
Khadija Ateya Almohsen ◽  
Huda Kadhim Al-Jobori
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Large Scale ◽  
Singular Value ◽  
Web Content ◽  
Large Scale Systems ◽  
Existing Problems ◽  
Current State ◽  
The World ◽  
Value Decomposition

The increasing usage of e-commerce website has led to the emergence of Recommender System (RS) with the aim of personalizing the web content for each user. One of the successful techniques of RSs is Collaborative Filtering (CF) which makes recommendations for users based on what other like-mind users had preferred. However, as the world enter Big Data era, CF has faced some challenges such as: scalability, sparsity and cold start. Thus, new approaches that overcome the existing problems have been studied such as Singular Value Decomposition (SVD). This chapter surveys the literature of RSs, reviews the current state of RSs with the main concerns surrounding them due to Big Data, investigates thoroughly SVD and provides an implementation to it using Apache Hadoop and Spark. This is intended to validate the applicability of, existing contributions to the field of, SVD-based RSs as well as validated the effectiveness of Hadoop and spark in developing large-scale systems. The results proved the scalability of SVD-based RS and its applicability to Big Data.

Advanced Structural Integrity Assessment Tools for Safe Long Term Operation: ATLAS+ Project — Status of the Activities of the WP3 on Modelling

2019 ◽  
Author(s):  
Stéphane Marie ◽  
Arnaud Blouin ◽  
Tomas Nicak ◽  
Dominique Moinereau ◽  
Anna Dahl ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Integrity ◽  
Assessment Tools ◽  
Local Approach ◽  
Coupled Models ◽  
Large Defect ◽  
Ductile Tearing ◽  
Term Operation ◽  
Integrity Assessment

Abstract The main objective and mission of the ATLAS+ project is to develop advanced structural assessment tools to address the remaining technology gaps for the safe and long term operation of nuclear reactor pressure coolant boundary systems. ATLAS+ WP3 focuses mainly on ductile tearing prediction for large defect in components: Several approaches have been developed to accurately model the ductile tearing process and to take into account phenomena such as the triaxiality effect, or the ability to predict large tearing in industrial components. These advanced models include local approach coupled models or advanced energetic approaches. Unfortunately, the application of these tools is today rather limited to R&D expertise. However, because of the continuous progress in the performance of the calculation tools and accumulated knowledge, in particular by members of ATLAS+, these models can now be considered as relevant for application in the context of engineering assessments. WP3 will therefore: • Illustrate the implementation of these models for industrial applications through the interpretation of large scale mock-ups (with cracks in weld joints for some of them), • Make recommendations for the implementation of the advanced models in engineering assessments, • Correct data from the conventional engineering approach by developing a methodology to produce J-Δa curve suitable case by case, based on local approach models, • Improve the tools, guidance and procedures for undertaking leak-before-break (LBB) assessments of piping components, particularly in relation to representing structural representative fracture toughness J-Resistance curves and the influence of weld residual stresses. To achieve these goals, WP3 is divided into 4 sub-WPs and this paper presents the progress of the work performed in each sub-WP after 24 months of activities.

scholarly journals Non-Negative K-SVD as an element of the forecasting electricity demand system

2019 ◽  
Vol 84 ◽  
pp. 01003
Author(s):  
Marcin Drechny
Keyword(s):  
Singular Value Decomposition ◽  
Sparse Coding ◽  
Demand Forecasting ◽  
Singular Value ◽  
Demand System ◽  
Power Demand ◽  
Power Load ◽  
Fast Power ◽  
Value Decomposition ◽  
Svd Method

The article describes the NN-K-SVD method based on the use of sparse coding and the singular value decomposition to specific values. An example of using the method is the compression of load profiles. The experiment of compression of 125022 power load profiles has been carried out with the use of registered profiles in households and small offices. Two matrices: patterns (atoms) and scaling factors are the result of the discussed algorithm. Features of the created matrices, which can be used in the creation of fast power demand forecasting systems, have been characterized.

scholarly journals Split-and-Combine Singular Value Decomposition for Large-Scale Matrix

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jengnan Tzeng
Keyword(s):  
Singular Value Decomposition ◽  
Large Scale ◽  
Semantic Analysis ◽  
Computational Cost ◽  
Matrix Decomposition ◽  
Singular Value ◽  
Fast Method ◽  
The Matrix ◽  
Scale Matrix ◽  
Value Decomposition

The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order three computational cost makes many modern applications infeasible, especially when the scale of the data is huge and growing. Therefore, it is imperative to develop a fast SVD method in modern era. If the rank of matrix is much smaller than the matrix size, there are already some fast SVD approaches. In this paper, we focus on this case but with the additional condition that the data is considerably huge to be stored as a matrix form. We will demonstrate that this fast SVD result is sufficiently accurate, and most importantly it can be derived immediately. Using this fast method, many infeasible modern techniques based on the SVD will become viable.

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