Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Investigating the effect of machining processes on the mechanical behavior of composite plates with circular holes

Abstract: The influence of the machining quality on the mechanical behavior of CFRP composites is yet not fully understood. There are only few works in the literature that have investigated the effect of the machining quality on CFRP. In fact, most of these works focus only on conventional machining such as axial or orbital drilling. The aim of this paper is to examine the influence of two machining processes namely conventional machining (CM) and abrasive water jet machining (AWJM) on the mechanical behavior of composite plates under cyclic loading. For this purpose, an experimental study using several composite plates drilled with a cutting tool and an abrasive water jet machining was carried out. In order to study the impact of the process of machining on the mechanical behavior, thermographic infrared testing and fatigue cyclic tests were performed to assess temperature evolutions, stiffness degradation, and the damage evolution in these plates. Fatigue testing results have shown that the damage accumulation in specimens drilled with CM process was higher than the AWJM specimens. Furthermore, the endurance limit for a composite plate drilled with CM was approximately 10% inferior compared to specimens drilled with AWJM. This difference can be related to the initial surface integrity after machining induced by the difference in the mechanism of material’s removal between the two processes used.

Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

Abstract: Airborne light detection and ranging (LiDAR) systems are used to measure the range (distance from the sensor to the target) and the intensity data (the backscattered energy from the target). LiDAR has been used extensively to model the topography of the Earth surface. Nowadays, LiDAR systems operating in the near-infrared spectral range are also gaining high interest for land cover classification and object recognition. LiDAR system requires geometric calibration (GC) and radiometric correction (RC) in order to maximize the benefit from the collected LiDAR data. This paper evaluates the impact of the GC and the RC of the LiDAR data on land cover classification. The procedure includes the use of a quasi-rigorous method for the GC and the radar (range) equation for the RC of the LiDAR data. The geometric calibration procedure is used to adjust the coordinates of the point cloud by removing the impact of biases in the system parameters as well as deriving corrected ranges and scan angles (in the absence of the system’s raw measurements) for the RC process. The geometrically calibrated ranges and scan angles are then used to correct the intensity data from the atmospheric attenuation and background backscattering based on the radar (range) equation. The atmospheric attenuation, which has not been fully addressed in the previous literature, is modeled by considering the parameters of absorption as well as scattering derived from existing empirical models and public (free) molecular absorption database. A LiDAR dataset covering an urban area is used to evaluate the effect of the GC and RC of the LiDAR data on land cover classification. The results are evaluated using a true ortho-rectified aerial image acquired during the same flight mission. The classification results show an accuracy improvement of about 9.4–12.8% for the LiDAR data used after the GC and RC. The study provides a practical approach for the LiDAR system GC and RC which can be implemented by most of the data end users. The outcome from this research work is a data processing tool that maximizes the benefits of using the intensity data for object recognition and land cover classification, which will be quite important for LiDAR data users.

A Space-Vector PWM-Based Voltage-Balancing Approach With Reduced Current Sensors for Modular Multilevel Converter

Abstract: Arm voltage and submodule (SM) capacitor voltage balancing is a key factor for the safe and reliable operation of modular multilevel converters (MMCs). The arm voltage balancing is achieved through a zero-sequence voltage controller in carrier pulse-width modulation (CPWM). In this study, a dual space-vector pulse-width modulation (SVPWM) technique is proposed for an MMC, which eliminates the external controller for arm voltage balancing. In this approach, the three-phase top and bottom arms are independently controlled using SVPWM. In addition, the capacitor voltage balancing can be achieved using redundant switching vectors. However, this will increase the computational load on the space-vector modulator. Therefore, an external capacitor voltage-balancing approach is proposed to minimize the computational complexity. The proposed approach uses the direction of load current instead of the arm current in SM selection process. As such, the required number of current sensors is reduced to 50% in a three-phase system. The proposed modulation and voltage-balancing approach are simulated and experimentally verified on the MMC system with three-level flying capacitor (3L-FC) SMs. Simulation and experimental results show the successful balancing of the arm voltage and SM capacitors voltage.