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Your search keyword '"Pg Emeroylariffion Abas"' showing total 8 results
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8 results on '"Pg Emeroylariffion Abas"'

1. In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

Author

Muhammad Mu'az Imran, Young Kim, Gisun Jung, Liyanage Chandratilak De Silva, Jeong-Hun Suh, Pg Emeroylariffion Abas, and Yun Bae Kim

Subjects
Big data visualization, directed energy deposition, in-situ quality monitoring, performance evaluation, surface defects detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel Defects-Finder algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm’s ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

Published
2023
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5. Highly Sensitive Photonic Crystal Fiber Sensor for Liquid Detection

Author

Feroza Begum, Abdul Mu’iz Maidi, and Pg Emeroylariffion Abas

Subjects
Core (optical fiber), Materials science, business.industry, Antenna aperture, Single-mode optical fiber, Optoelectronics, Sensitivity (control systems), Cladding (fiber optics), business, Finite element method, Highly sensitive, Photonic-crystal fiber
Abstract

A highly sensitive simple photonic crystal fiber sensor for liquid detection of water, ethanol and benzene, has been proposed and numerically analysis using a full vector finite element method. The proposed structure consists of a hexagonal core hole and 3 cladding air hole rings, operating in the lower optical region of 0.6 to 2.0 µm. It has been shown that the proposed structure have a high relative sensitivity, low confinement loss and low effective area; with the proposed structure operating in single mode for all liquid analytes. The results have demonstrated the applicability of the proposed structure for liquid detection sensing application.

Published
2021

6. Novel Terahertz Photonic Crystal Fiber for Liquid Cocaine Sensing

Author

Izaddeen Kabir Yakasai, Feroza Begum, Nurul Awadah Nadiah Binti Suhaimi, and Pg Emeroylariffion Abas

Subjects
Core (optical fiber), Materials science, Birefringence, business.industry, Terahertz radiation, Optoelectronics, Fiber, Low transmission, business, Sensitivity (electronics), Photonic-crystal fiber
Abstract

A novel photonic crystal fiber has been proposed for sensing liquid cocaine in terahertz frequencies. By varying the fiber's core ellipse height, simulation results have shown a high relative sensitivity above 85%, high birefringence of 0.018 and extremely low transmission effective material loss at 1 THz.

Published
2020

7. Proposal of Highly Birefringent Porous Core Photonic Crystal Fibre for Polarisation Maintaining Terahertz Wave Guidance

Author

Feroza Begum, Pg Emeroylariffion Abas, and Izaddeen Kabir Yakasai

Subjects
Materials science, Birefringence, Wave propagation, Terahertz radiation, business.industry, Physics::Optics, 02 engineering and technology, 01 natural sciences, Finite element method, 010309 optics, Core (optical fiber), 020210 optoelectronics & photonics, Perfectly matched layer, Optics, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Photonics, business, Photonic-crystal fiber
Abstract

A highly birefringent photonic crystal fibre with elliptical air holes in the core has been proposed and simulated for terahertz wave propagation. Using finite element method with anti-reflective perfectly matched layer, it is shown that the three elliptical air holes in the core are sufficient to produce extremely high birefringence, high power fraction and low transmission losses.

Published
2020

8. Bird Sounds Classification Using Linear Discriminant Analysis

Author

Murugiaya Ramashini, Liyanage C. De Silva, Ulmar Grafe, and Pg Emeroylariffion Abas

Subjects
Computer science, business.industry, Centroid, Pattern recognition, Linear discriminant analysis, computer.software_genre, Support vector machine, Classification methods, Artificial intelligence, South east asia, Audio signal processing, business, Classifier (UML), computer
Abstract

Visually identifying birds is a challenging task due to its mobile airborne nature especially in the dense jungle of the Borneo rainforest. Instead, bird sounds may be used for bird identification purpose. The goal of this study is to classify and identify Borneo regional birds from their sounds, by using simple classification method. Five (5) local bird species have been used to demonstrate the performance of the proposed method. Using audio signal processing techniques, thirty-five (35) features have been extracted from the bird sounds, which are then reduced using Linear Discriminant Analysis (LDA), before feeding the data to Nearest Centroid (NC) classifier. It has been demonstrated that the proposed method outperforms other complex methods such as support vector machine and K-nearest neighbor, with a prediction accuracy level of 96%.

Published
2019

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