Contributions to statistical machine learning algorithm

This thesis's research focus is on computational statistics along with DEAR (abbreviation of differential equation associated regression) model direction, and that in mind, the journal papers are written as contributions to statistical machine learning algorithm literature

Variable modeling of fuzzy phenomena with industrial applications

Includes abstract. Includes bibliographical references (leaves 98-100)

Cumulative sum quality control charts design and applications

Includes bibliographical references (pages 165-169).Classical Statistical Process Control Charts are essential in Statistical Control exercises and thus constantly obtained attention for quality improvements. However, the establishment of control charts requires large-sample data (say, no less than I 000 data points). On the other hand, we notice that the small-sample based Grey System Theory Approach is well-established and applied in many areas: social, economic, industrial, military and scientific research fields. In this research, the short time trend curve in terms of GM( I, I) model will be merged into Shewhart and CU SUM two-sided version control charts and establish Grey Predictive Shewhart Control chart and Grey Predictive CUSUM control chart. On the other hand the GM(2, I) model is briefly checked its of how accurate it could be as compared to GM( I, 1) model in control charts. Industrial process data collected from TBF Packaging Machine Company in Taiwan was analyzed in terms of these new developments as an illustrative example for grey quality control charts

An Adaptive Tool-Based Telerobot Control System

Modern telerobotics concepts seek to improve the work efficiency and quality of remote operations. The unstructured nature of typical remote operational environments makes autonomous operation of telerobotic systems difficult to achieve. Thus, human operators must always remain in the control loop for safety reasons. Remote operations involve tooling interactions with task environment. These interactions can be strong enough to promote unstable operation sometimes leading to system failures. Interestingly, manipulator/tooling dynamic interactions have not been studied in detail. This dissertation introduces a human-machine cooperative telerobotic (HMCTR) system architecture that has the ability to incorporate tooling interaction control and other computer assistance functions into the overall control system. A universal tooling interaction force prediction model has been created and implemented using grey system theory. Finally, a grey prediction force/position parallel fuzzy controller has been developed that compensates for the tooling interaction forces. Detailed experiments using a full-scale telerobotics testbed indicate: (i) the feasibility of the developed methodologies, and (ii) dramatic improvements in the stability of manipulator – based on band saw cutting operations. These results are foundational toward the further enhancement and development of telerobot

Optimizing to Minimize Thrust Force in Drilling Carbon Fiber Reinforced Plastic Composites with HSS Drill Bit Using Taguchi-Pareto Particle Swarm Optimization Method

In this study, a robust method of Taguchi-Pareto (TP) coupled with particle swarm optimization (PSO) is proposed to minimize the thrust force in the drilling of carbon fiber reinforced plastic composites. Taguchi-Pareto is used against Taguchi (T) to emphasize the prioritization scheme essential for deploying the resources to parameters. Besides, and differently from earlier studies, particle swarm optimization is integrated with the Taguchi-Pareto to optimize the structure further. A further result is placed in the fitness function of the PSO to cultivate the velocity and position vectors. In the TP-PSO, the Pareto scheme is introduced to prioritize the factors based on the 80-20-rule. The Taguchi method yielded a feasible optimal parametric setting. The TPSO and TPPSO attained minimum thrust force in four and seven iterations, respectively. Furthermore, the PSO, TPPSO, and TPSO hold the first, second, and third positions, respectively. Results suggest that the proposed robust TPPSO offers an important indicator of optimization of the thrust force while drilling carbon fiber reinforced plastic composites using existing datasets. The usefulness of this effort is to help drilling operators and process engineers undertake energy-saving decisions

Modeling of Interstellar Scintillation Arcs from Pulsar B1133+16

The parabolic arc phenomenon visible in the Fourier analysis of the scintillation spectra of pulsars provides a new method of investigating the small scale structure in the ionized interstellar medium (ISM). We report archival observations of the pulsar B1133+16 showing both forward and reverse parabolic arcs sampled over 14 months. These features can be understood as the mutual interference between an assembly of discrete features in the scattered brightness distribution. By model-fitting to the observed arcs at one epoch we obtain a ``snap-shot'' estimate of the scattered brightness, which we show to be highly anisotropic (axial ratio >10:1), to be centered significantly off axis and to have a small number of discrete maxima, which are coarser the speckle expected from a Kolmogorov spectrum of interstellar plasma density. The results suggest the effects of highly localized discrete scattering regions which subtend 0.1-1 mas, but can scatter (or refract) the radiation by angles that are five or more times larger.Comment: 14 pages, 4 figures, submitted to Astrophysical Journa

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index (NDVI), landuse, rainfall, distance to road, distance to river, distance to fault, plan curvature, and profile curvature, were analyzed. Chi-square feature selection method was adopted to compare the significance of each factor with landslide occurence. The GWR model was compared with two well-known models, namely, logistic regression (LR) and support vcector machine (SVM). Results of chi-square feature selection indicated that lithology and slope are the most influencial factors, whereas SPI was found statistically insignificant. Four landslide susceptibility maps were generated by GWR, SGD-LR, SGD-SVM, and SVM models. The GWR model exhibited the highest performance in terms of success rate and prediction accuracy, with values of 0.789 and 0.819, respectively. The SVM model exhibited slightly lower AUC values than that of the GWR model. Validation result of the four models indicates that GWR is a better model than other widely used models