Fully convolutional neural network for Malaysian road lane detection

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy

Optimised combinatorial control strategy for active anti-roll bar system for ground vehicle

The objective of this paper is to optimise the proposed control strategy for an active anti-roll bar system using non-dominated sorting genetic algorithm (NSGA-II) tuning method. By using an active anti-roll control strategy, the controller can adapt to current road conditions and manoeuvres unlike a passive anti-roll bar. The optimisation solution offers a rather noticeable improvement results compared to the manually-tuned method. From the application point of view, both tuning process can be used. However, using optimisation method gives a multiple choice of solutions and provides the optimal parameters compared to manual tuning method

Dual-stage gain-clamped erbium-doped fiber amplifier with fiber Bragg grating

We demonstrate a dual-stage gain-clamped erbiumdoped fiber amplifier. The first-stage amplifier consists of a short length of erbium-doped fiber to produce low noise figures. The second-stage is constructed from a counter-propagating ringlaser, in which the signals and the lasing wavelength propagate in the opposite direction. The lasing wavelength is selected via a reflective-type of fiber Bragg grating. The gain-clamping mechanism can be adjusted by either changing the fiber Bragg grating reflectivity or center wavelength. The noise figure penalty is about 1.5 dB for the gain-clamping value from 11 dB to 20.5 dB. (© 2008 by Astro Ltd., Published exclusively by WILEY-VCH Verlag GmbH & Co. KGaA

The fusion of HRV and EMG signals for automatic gender recognition during stepping exercise

In this paper, a new gender recognition framework based on fusion of features extracted from healthy people electromyogram (EMG) and heart rate variability (HRV) during stepping activity using a stepper machine is proposed. An approach is investigated for the fusion of EMG and HRV which is feature fusion. The feature fusion is carried out by concatenating the feature vector extracted from the EMG and HRV signals. A proposed framework consists of a sequence of processing steps which are preprocessing, feature extraction, feature selection and lastly the fusion. The results shown that the fusion approach had improved the performance of gender recognition compared to solely on EMG or HRV based gender identifier

Radial basis function neural network for head roll prediction modelling in a motion sickness study

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses

The Usability of E-learning Platforms in Higher Education: A Systematic Mapping Study

The use of e-learning in higher education has increased significantly in recent years, which has led to several studies being conducted to investigate the usability of the platforms that support it. A variety of different usability evaluation methods and attributes have been used, and it has therefore become important to start reviewing this work in a systematic way to determine how the field has developed in the last 15 years. This paper describes a systematic mapping study that performed searches on five electronic libraries to identify usability issues and methods that have been used to evaluate e-learning platforms. Sixty-one papers were selected and analysed, with the majority of studies using a simple research design reliant on questionnaires. The usability attributes measured were mostly related to effectiveness, satisfaction, efficiency, and perceived ease of use. Furthermore, several research gaps have been identified and recommendations have been made for further work in the area of the usability of online learning

In-Situ Measurement Of Electrode Wear During Edm Drilling Using Vision System

Machine vision system is an image-based technology use to perform automatic inspection and analysis such as process control and robot guidance. The aim for this project is to develop a fully automated electrode wear detection system in EDM by using machine vision system and apply this system in detecting electrode wear in EDM. This project was conducted using DSLR camera as monitoring device. The electrode undergo hole making process with a depth of 10 mm,20 mm,30 mm,40 mm, and 50 mm to observe the electrode condition. The image of the electrode will be remotely captured from the laptop and then will undergo image processing process using Matlab software to calculate and determine the electrode wear. The output of this project will show the images of the electrode wear and its wear value. Findings from the project show that this system is suitable and applicable in EDM super drill machine to monitor the tool condition

Innovative method of measuring electrode wear during EDM drilling process using vision system

The aim of this project is to develop a fully automated electrode wear detection system in EDM by using machine vision system and apply this system in detecting electrode wear in EDM drilling. Machine vision system was imagebased technology used to perform automatic inspection and analysis of the electrode wear for EDM drilling. The high resolution’s DSLR camera as monitoring device to capture the image. The brass electrode with the diameter of 3 mm undergo hole making process with a depth of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm to obtain the level of wear. The images of the electrodes were remotely captured using DSLR camera then read from the laptop and undergo image processing process using Matlab software to calculate and determine the electrode wear. The result showed the wear percentage of the electrode is 4.235% for 10 mm to 30 mm in depth and 3.59% for depth of 30 mm to 50 mm. This project showed that the developed system was suitable and applicable in monitor an EDM electrode super drill machine

An investigation on the mitigation of end-stop impacts in a magnetorheological damper operated by the mixed mode

This paper presents mitigation behaviour of magnetorheological (MR) damper operated with a mixed working modes. A combination of the shear and squeeze modes is employed in the structure of MR damper to obtain the field-dependent normal yield stress as well as strengthen the squeeze effect. The experimental evaluation shows that when the piston is squeezing the bottom gap from the stroke of 25 to 26 mm, the sudden increase of squeeze force is observed confirming the existence of the mitigation effect. It is also observed that the magnitude of mitigation force is positively correlated with the magnitude of current given to the electromagnet. The measured peak mitigation forces are ranged from 722 N to 1032 N when the electromagnet currents are varied from 0.2 A to 0.8 A, respectively. The variable mitigation effect indicates that the concept can be further discussed as a potential impact protection feature in an MR damper