Functional Analysis of the Differences in the Dimensions of Two Types of Boiled and Steamed Rice Grains

The study tested how the cooking process can change the dimensions of rice grains. The impact of set times of cooking or steaming process on the characteristics such as length, width, and height of two varieties of rice, namely, long-grain white and parboiled, was investigated. The measurements of the dimension characteristics obtained at different times of the cooking process were converted to functional data. Different methods of multivariate functional data analysis, namely, functional multivariate analysis of variance, functional discriminant coordinates, and cluster analysis, were applied to discover the differences between the two varieties and the two heat treatment methods.

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Evaluating the impact of climate change on stream flow: integrating GCM, hydraulic modelling and functional data analysis

Arabian Journal of Geosciences ◽

10.1007/s12517-020-05881-y ◽

2020 ◽

Vol 13 (17) ◽

Author(s):

Abdul Razzaq Ghumman ◽

Ateeq-ur-Rauf ◽

Abdullah Alodah ◽

Husnain Haider ◽

Md. Shafiquzzaman

Keyword(s):

Climate Change ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Stream Flow ◽

Hydraulic Modelling ◽

Impact Of Climate Change ◽

The Impact

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A New Functional Clustering Method with Combined Dissimilarity Sources and Graphical Interpretation

10.5772/intechopen.100124 ◽

2021 ◽

Author(s):

Wenlin Dai ◽

Stavros Athanasiadis ◽

Tomáš Mrkvička

Keyword(s):

Functional Data Analysis ◽

Functional Data ◽

Visualization Tool ◽

Functional Clustering ◽

Graphical Interpretation ◽

Model Free ◽

Heavy Tailed ◽

Cluster Variation ◽

And Performance ◽

Multivariate Functional Data

Clustering is an essential task in functional data analysis. In this study, we propose a framework for a clustering procedure based on functional rankings or depth. Our methods naturally combine various types of between-cluster variation equally, which caters to various discriminative sources of functional data; for example, they combine raw data with transformed data or various components of multivariate functional data with their covariance. Our methods also enhance the clustering results with a visualization tool that allows intrinsic graphical interpretation. Finally, our methods are model-free and nonparametric and hence are robust to heavy-tailed distribution or potential outliers. The implementation and performance of the proposed methods are illustrated with a simulation study and applied to three real-world applications.

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Outlier Detection for Pandemic-Related Data Using Compositional Functional Data Analysis

10.1007/978-3-030-78334-1_12 ◽

2021 ◽

pp. 251-266

Author(s):

Christopher Rieser ◽

Peter Filzmoser

Keyword(s):

Outlier Detection ◽

Functional Data Analysis ◽

Functional Data ◽

Subject Area ◽

Smooth Functions ◽

Related Data ◽

Relative Information ◽

The Subject ◽

Multivariate Functional Data ◽

Events Data

AbstractWith accurate data, governments can make the most informed decisions to keep people safer through pandemics such as the COVID-19 coronavirus. In such events, data reliability is crucial and therefore outlier detection is an important and even unavoidable issue. Outliers are often considered as the most interesting observations, because the fact that they differ from the data majority may lead to relevant findings in the subject area. Outlier detection has also been addressed in the context of multivariate functional data, thus smooth functions of several characteristics, often derived from measurements at different time points (Hubert et al. in Stat Methods Appl 24(2):177–202, 2015b). Here the underlying data are regarded as compositions, with the compositional parts forming the multivariate information, and thus only relative information in terms of log-ratios between these parts is considered as relevant for the analysis. The multivariate functional data thus have to be derived as smooth functions by utilising this relative information. Subsequently, already established multivariate functional outlier detection procedures can be used, but for interpretation purposes, the functional data need to be presented in an appropriate space. The methodology is illustrated with publicly available data around the COVID-19 pandemic to find countries displaying outlying trends.

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Functional data analysis of models for predicting temperature and precipitation under climate change scenarios

Journal of Water and Climate Change ◽

10.2166/wcc.2019.172 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1748-1765 ◽

Cited By ~ 2

Author(s):

Abdul Razzaq Ghumman ◽

Ateeq-ur-Rauf ◽

Husnain Haider ◽

Md. Shafiquzamman

Keyword(s):

Climate Change ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Climate Change Scenarios ◽

Emission Scenarios ◽

Temperature And Precipitation ◽

Station Data ◽

The Impact ◽

Precipitation And Temperature

Abstract Evaluating the impact of climatic change on hydrologic variables is highly important for sustainability of water resources. Precipitation and temperature are the two basic parameters which need to be included in climate change impact studies. Thirty years (1985–2015) climatic data of Astore, a sub-catchment of the Upper Indus River Basin (UIRB), were analyzed for predicting the temperature and precipitation under different climate change scenarios. The station data were compared with the results of two global climate models (GCMs) each with two emission scenarios, including Representative Concentration Pathway (RCP) 2.6 and 8.5. The Mann–Kendall test and Sen's slope were applied to explore various properties of precipitation and temperature data series for a trend analysis. The commonalities and dissimilarities between the results of various GCMs and the trend of the station data were investigated using the functional data analysis. Two cross distances were estimated on the basis of Euclidean distances between the predicted time series; subsequently, the differences in their first derivatives were used to evaluate their mutual dissimilarities. The long-term predictions by GCMs show a decreasing trend in precipitation and a slight increase in temperature in some seasons. The result of GCMs under both the emission scenarios showed almost the same pattern of changes in the two hydrologic variables throughout the century with their values reporting slightly higher for the RCP8.5 scenario as compared to those for RCP2.6. Validation of the GCM results using GCM-CSIRO-Mk3.6 revealed an overall agreement between the different models. The dissimilarity analysis manifested the difference between the results of temperature predicted by various GCMs.

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Microstructural and Mechanical Behaviour Evaluation of Mg-Al-Zn Alloy Friction Stir Welded Joint

International Journal of Automotive and Mechanical Engineering ◽

10.15282/ijame.17.3.2020.08.0612 ◽

2020 ◽

Vol 17 (3) ◽

pp. 8150-8159

Author(s):

Kulwant Singh ◽

Gurbhinder Singh ◽

Harmeet Singh

Keyword(s):

Heat Treatment ◽

Friction Stir Welding ◽

Magnesium Alloys ◽

Mechanical Performance ◽

Fuel Efficiency ◽

Research Work ◽

Grain Structure ◽

Tensile Fracture ◽

Friction Stir ◽

The Impact

The weight reduction concept is most effective to reduce the emissions of greenhouse gases from vehicles, which also improves fuel efficiency. Amongst lightweight materials, magnesium alloys are attractive to the automotive sector as a structural material. Welding feasibility of magnesium alloys acts as an influential role in its usage for lightweight prospects. Friction stir welding (FSW) is an appropriate technique as compared to other welding techniques to join magnesium alloys. Field of friction stir welding is emerging in the current scenario. The friction stir welding technique has been selected to weld AZ91 magnesium alloys in the current research work. The microstructure and mechanical characteristics of the produced FSW butt joints have been investigated. Further, the influence of post welding heat treatment (at 260 °C for 1 h) on these properties has also been examined. Post welding heat treatment (PWHT) resulted in the improvement of the grain structure of weld zones which affected the mechanical performance of the joints. After heat treatment, the tensile strength and elongation of the joint increased by 12.6 % and 31.9 % respectively. It is proven that after PWHT, the microhardness of the stir zone reduced and a comparatively smoothened microhardness profile of the FSW joint obtained. No considerable variation in the location of the tensile fracture was witnessed after PWHT. The results show that the impact toughness of the weld joints further decreases after post welding heat treatment.

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