Invited Speaker

Prof. Muhammad Abbas

A novel generalization of trigonometric Bezier curve and surface with shape parameters and its applications

Abstract

Generalized Trigonometric basis functions with two shape parameter have been formulated in this paper by using a recursive approach. These basis functions carry a lot of geometric features of the classical Bernstein basis functions and maintain the shape of curve and surface as well. Generalized trigonometric Bezier curves and surfaces have been defined on the basis of these functions and also analyze their properties. This analysis shows that the presence of shape parameters provides an opportunity to adjust the shape of the curve and surface by simply altering their values. These Generalized trigonometric Bezier curves meet the conditions required for parametric continuity as well as geometric continuity . After that some curve and surface design applications have been then discussed. The modeling examples illustrate that the new curves and surfaces provide better approximation and mathematical description of Bezier curves and surfaces and make them a valuable method for the design of curves and surfaces.

Biography

Muhammad Abbas is Associate Professor at Department of Mathematics, University of Sargodha, Pakistan. He completed his Ph.D. degree in Computer Aided Geometric Design at School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia, where he also completed his post-Doctorate fellowship in 2013. His research interest is shape-preserving scientific data visualization, computer graphics, numerical B spline methods for PDEs and ODEs. Dr. Abbas has published 60 papers at international conferences and peer-reviewed journals in his area of research. He has supervised one PhD and more than 25 projects of Master of Philosophy. He is member of the Editorial Board of international and national journals.

Dr. Sachio Kobayashi

Digital Transformation of Facility Monitoring by Predictive Anomaly Detection Based on Optical Fiber Temperature Measurement

Abstract

In recent years, the advancement of digital technology has transformed the way many traditional industries perform their daily functions. Operators, at power stations, plants, and manufacturing factories, give first priority to safety and have to guarantee stable operations. Therefore, they work hard on facility monitoring and maintenance to prevent problems that can lead to significant losses, such as serious accidents and production stop. In typical plants, technicians still detect anomalies of facilities from information about a few equipped sensors based on their own experience and intuition.

We have made digital facility monitoring possible with our predictive anomaly detection based on optical fiber temperature measurement. Our measurement technology with Raman scattering light can continually obtain multipoint temperature data, such as more than 10,000 points, with one optical fiber at the same time. Moreover, the shape feature of optical fiber allows temperature measurements in confined spaces, something difficult with conventional measuring instruments. These features are valuable to carefully monitor heat generation conditions of various machines and devices. Our predictive anomaly detection automatically and predictively captures anomaly signs, that potentially lead to errors and failures, based on analysis and simulation of high-resolution temperature data. The feature of anomaly detection in its very early stage enables to enhance safety and production quality in facility operation, e.g., preventing fuel pipes in a power station from clogging in response to a sudden drop in their external temperature by changing control conditions in advance.

Biography

Sachio Kobayashi is a senior researcher in the field of modeling and physics simulation in FUJITSU LABORATORIES LTD., in Japan. He has received his Ph. D degree in Precision Engineering from the University of Tokyo, Japan in 2010. He was Visiting Scholar of Computer Science at Stanford University, the US for one year since Oct. 2014. His current research interests focus on exploring practical uses and applications of digital technologies, such as machine learning, simulation, optimization, and IoT, in industrial fields. He received some awards, including ASME CIE Best Papers Award (2013), related to product design and production system.

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