Maxmin Data Range Heuristic-based Initial Centroid Method of Partitional Clustering for Big Data Mining

The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality with high iterations. For these reasons, this study proposed the initial centroid initialization based Maxmin Data Range Heuristic (MDRH) method for K-Means (KM) clustering that reduces the execution times, iterations, and improves quality for big data clustering. The proposed MDRH method has compared against the classical KM and KM++ algorithms with four real datasets. The MDRH method has achieved better effectiveness and efficiency over RS, DB, CH, SC, IS, and CT quantitative measurements.

The Classification of GIS Objects

This paper discusses the current issues of the application of classification and data processing in geoinformation systems. The problems of classification of various objects have been studied in the works of many authors. These include a fairly wide range of problems: decryption of satellite images, pattern recognition, mathematical modeling, etc. In this paper, we study the methods and techniques for classifying objects listed in the literature, as well as preliminary data processing: feature normalization, feature weighting, aggregation, dimensionality reduction, etc. The result of finding spatial features in an attribute space is often a representation of spatial features in the form of an object-feature matrix that reflects the measurement of M features on N spatial features and contains N rows and M columns. To classify spatial objects, you must have a geographical map of these objects and an object-attribute matrix, the rows of which correspond to the spatial objects. In order to properly classify, you need to perform pre-processing of the data, including normalization, weighting, dimensionality reduction, aggregation, and identification. After preliminary data processing, the objects are classified. The paper lists and describes such classification methods as nuclear classification methods, hierarchical divisive classification methods, hierarchical agglomerative classification methods, near neighbor method, far neighbor method, centroid method, group mean method (mean link method) and other issues related to the classification of geoinformation objects.

A Study On Reliability Using Pendant, Hexant, Octant Fuzzy Numbers

The weaving machine’s reliability is assessed using newly introduced fuzzy numbers. The fuzzy numbers introduced in this study give a better method to improve the reliability than other techniques. Pendant Fuzzy Number, Hexant Fuzzy Number, and Octant Fuzzy Number are all introduced in this present study. Pendant Fuzzy Number, Hexant Fuzzy Number, and Octant Fuzzy Number,α-cuts are defined, as well as their mathematical operations. The numerical examples are utilised to conduct a comparative research of reliability using various Fuzzy Numbers, and their defuzzification is accomplished using various ways such as Signed Distance method, Graded Mean Integration Method and Centroid Method. The purpose of this study is to discover the most reliable value for a weaving machine.

On the Trail of Spatial Patterns of Genetic Variation

The accurate determination of the spatial trends on the variability of a species’ gene pool is essential to elucidate the underlying demographic-evolutionary events, thus helping to unravel the microevolutionary history of the population under study. Herein we present a new software called GenoCline, mainly addressed to detect genetic clines from allele, haplotype, and genome-wide data. This program package allows identifying the geographic orientation of clinal genetic variation through a system of iterative rotation of a virtual coordinate axis. Besides, GenoCline can perform complementary analyses to explore the potential origin of the genetic clines observed, including spatial autocorrelation, isolation by distance, centroid method, multidimensional scaling and Sammon projection. Among the advantages of this software is the ease in data entry and potential interconnection with other programs. Genetic and geographic data can be entered in spreadsheet table formatting (.xls), whereas genome-wide data can be imported in Eigensoft format. Genetic frequencies can also be exported in a format compatible with other programs dealing with population genetic and evolutionary biology analyses. All illustrations of results are saved in.svg format so that there will be high quality and easily editable vectorial graphs available for the researcher. Being implemented in Java, GenoCline is highly portable, thus working in different operating systems.

Site Selection for Weihai Port International Logistics Center Based on Centroid Method and Fuzzy Evaluation Method

Centroid method is used to study siteselection forWeihai Port International Logistics Center, then relevant index system is built based on the factors influencing site selection.Fuzzy comprehensive evaluation method is used to score several locations in Weihai City so that the optimal location can be selected via comparison. The research conclusions have a certain reference value for the site selection of Weihai comprehensive international logistics distribution center, which carries positive significance for the development of international logistics industry in Weihai Port and the urban area.

Energy Cane x Sugarcane Microregion Interaction in the State of Pernambuco: Sugarcane for Production of Bioenergy and Renewable Fuels

Assessing the differential behavior of a group of genotypes in various environments is fundamentally important in any breeding program. As sugarcane is the most important crop in the state of Pernambuco, it is of great relevance to study its performance in different cultivation sites to assist in the recommendation of new cultivars that increase the productivity of the cane fields. In view of the new demand from the sugar-energy sector for cultivars with high energy potential, this work aimed to select and recommend new genotypes with high fiber and sucrose percentage in the sugarcane microregions of the state of Pernambuco. The methodologies used to classify genotypes for adaptability and stability were as follows: simple linear regression, the modified centroid method, additive main effects, multiplicative interaction analysis, and linear mixed models. Genotypes with higher productivity and specific adaptability to the tested microregions were identified. The methodologies applied were efficient and complementary in recommending genotypes with favorable prospects for increasing sugar productivity, cogeneration of electric energy and the production of renewable fuels. Genotypes 6, 7, 9, 14, 16, and 18 stand out in terms of the productivity of sugar and fiber, with high potential to be released as commercial cultivars.

A Framework in Calibration Process for Line Structured Light System Using Image Analysis

Line structured light systems have been widely applied in the measurement of various fields. Calibration has been a hot research topic as a vitally important process of the line structured light system. The accurate calibration directly affects the measurement result of the line structured light system. However, the external environment factors, such as uneven illumination and uncertain light stripe width, can easily lead to an inaccurate extraction of light stripe center, which will affect the accuracy of the calibration. An image analysis-based framework in the calibration process was proposed for the line structure light system in this paper. A three-dimensional (3D) vision model of line structure light system was constructed. An image filtering model was established to equalize the uneven illumination of light stripe image. After segmenting the stripe image, an adaptive window was developed, and the width of the light stripe was estimated by sliding the window over the light stripe image. The light stripe center was calculated using the gray centroid method. The light plane was fitted based on the calibration points coordinates acquired by the camera system. In the measurement experiment of standard gauge block width, the maximum and minimum average deviations of 0.021 pixels and 0.008 pixels and the maximum and minimum absolute deviations of 0.023 pixels and 0.009 pixels could be obtained by using the proposed method, which implies the accuracy of the proposed method.

Enhanced Performance of Indirect Vector Controlled Induction Motor Drive with a Modified Type 2 Neuro-Fuzzy Torque Controller in Interfacing with dSPACE DS-2812

Due to the nonlinearities of the PI controller, the performance of the PI controller is not satisfactory. The gains must be properly selected after changes in control parameters is one of the issues of the PI controller. The modified type 2 Neuro-Fuzzy torque controller of indirect vector control-based induction motor drive is proposed in this paper by taking single input as an error i.e. speed and torque against two inputs error and change in error of conventional T2NFC.The superiority of fuzzy and neural networks has been utilized by T2NFC as type 2 MF’s consist of fuzzy and FOU. The intersection point of the membership function is smaller so that the value of the centroid method is more precise than the T1NFC. The induction motor parameters, such as stator phase current, speed, and torque of the proposed T2NFC are simulated in MATLAB at different operating conditions and compared with PI, T1NFC controllers. The proposed T2NFC significantly minimizes the ripples in torque of the induction motor in comparison with PI and T1NF controllers. The practical implementation is also carried out with a 3.7 KW induction motor using DSP 2812 controller to analyse induction motor parameters in real-time.

Trilinear fuzzy seepage model of shale gas reservoirs with multiple fuzzy parameters

This paper focuses on establishing the trilinear fuzzy seepage model with multiple fuzzy parameters for shale gas reservoirs. Different from the conventional seepage models of shale gas reservoirs, the multiple fuzzy parameters seepage model uses fuzzy numbers to describe some parameters with uncertainty. Firstly, the multiple fuzzy parameters seepage model is constructed based on fuzzy concepts. The fuzzy structure element method and the centroid method are used to solve the fuzzy seepage model and defuzzify, respectively. Secondly, the advantages of the development fuzzy model over the conventional seepage model are discussed and illustrated through numerical examples and simulations. Finally, to further study the seepage laws inside shale gas reservoirs, this paper explores the sensitivity of relevant main control parameters to gas production based on the development model.

CubeSat Observation of the Radiation Field of the South Atlantic Anomaly

The movement of the South Atlantic Anomaly has been observed since the end of the last century by many spacecrafts equipped with various types of radiation detectors. All satellites that have observed the drift of the South Atlantic Anomaly have been exclusively large missions with heavy payload equipment. With the recent rapid progression of CubeSats, it can be expected that the routine monitoring of the South Atlantic Anomaly will be taken over by CubeSats in the future. We present one-and-a-half years of observations of the South Atlantic Anomaly radiation field measured by a CubeSat in polar orbit with an elevation of 540 km. The position is calculated by an improved centroid method that takes into account the area of the grid. The dataset consists of eight campaigns measured at different times, each with a length of 22 orbits (~2000 min). The radiation data were combined with GPS position data. We detected westward movement at 0.33°/year and southward movement at 0.25°/year. The position of the fluence maximum featured higher scatter than the centroid position.