Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on ε insensitive support vector regression (ε-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of ε-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

5th International Conference on Advanced Manufacturing Engineering and Technologies

This book presents the proceedings from the 5th NEWTECH conference (Belgrade, Serbia, 5–9 June 2017), the latest in a series of high-level conferences that bring together experts from academia and industry in order to exchange knowledge, ideas, experiences, research results, and information in the field of manufacturing. The range of topics addressed is wide, including, for example, machine tool research and in-machine measurements, progress in CAD/CAM technologies, rapid prototyping and reverse engineering, nanomanufacturing, advanced material processing, functional and protective surfaces, and cyber-physical and reconfigurable manufacturing systems. The book will benefit readers by providing updates on key issues and recent progress in manufacturing engineering and technologies and will aid the transfer of valuable knowledge to the next generation of academics and practitioners. It will appeal to all who work or conduct research in this rapidly evolving field

The Daily News

Daily newspaper from Shawnee, Oklahoma Territory that includes local, territorial, and national news along with advertising

Lipase catalyzed synthesis of flavor esters in non-aqueous media: Optimization of the yield of pentyl 2-methylpropanoate by statistical analysis

In this study, the synthesis of pentyl 2-methylpropanoate employing a commercial lipase from Candida rugosa was investigated, the emphasis being placed on analyzing the effects of various process conditions on the yield of ester. The response surface methodology (RSM) and five-level-five-factor central composite rotatable design (CCRD) were used to evaluate the effects of variables, namely the initial water content, 0.0–2.0 % (w/v), the reaction temperature, 35–75 °C, the enzyme concentration, 1.0–5.0 g dm-3, the acid/alcohol mole ratio, 1:2–5:2, and the reaction time, 4–48 h, on the yield (%) of ester. The production of pentyl 2-methylpropanoate was optimized and an ester yield response equation was obtained, enabling the prediction of ester yields from known values of the five main factors. It seems that the enzyme concentration, reaction time and acid/alcohol mole ratio predominantly determine the conversion process, while the amount of added water amount had no significant influence on the ester yield. Conversion of around 92 % of the substrate to ester could be realized using a concentration of lipase as low as 4.0 g dm-3 and in a relatively short time (26 h) at 35 °C, when a high substrate mole ratio of 2.5 was used

Eco-design of fixtures based on life cycle and cost assessment

© 2019 DAAAM International Vienna. All rights reserved. The paper presents the new model for eco-design of fixtures based on life cycle and cost assessment. Four fixture types with different mechanical and physical properties as well as different manufacturing costs have been evaluated. The life cycle results show that the environmental impact is closely related to the mass of steel needed for fixture manufacture. On the other hand, the fixture with the largest environmental impact had the smallest total fixture cost and vice versa. The results show that it is possible to implement environmental and cost analysis in fixture design process and to enable comparative analysis of fixture constructions by three standpoints, technical, environmental and economic

Intelligent sensing systems – Status of research at KaProm

Within Industrie 4.0 intelligent sensing systems represent an indispensable asset with significant role in enabling shifting from automated to intelligent manufacturing. Instead of being simple transducers, intelligent sensors are able to retrieve useful information from raw signal. They represent systems with integrated computation and communication capabilities, that run sophisticated and real time applicable algorithms and communicate the necessary information to the other elements of the manufacturing facility. In this paper we present the recent research results in the field of intelligent sensing systems that were accomplished at Laboratory for Manufacturing Automation and Laboratory for Robotics and Artificial Intelligence at Department for Production Engineering (KaProm) at Faculty of Mechanical Engineering in Belgrade. Presented systems are intended for application in various manufacturing processes, such as machining, assembly, manipulation, material transport, rubber processing lines. They are based on application of different non-stationary signal processing (Discrete Wavelet Transform, Huang-Hilbert transform) and machine learning and artificial intelligence techniques (Support Vector Machines, Artificial Neural Networks, bio-inspired algorithms, clustering methods, fuzzy inference mechanisms). The most of developed systems are implemented in embedded devices and their real-world applicability is demonstrated