Determination of flexibility of workers working time through Taguchi method approach

Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.

An overview of multi-filters for eliminating impulse noise for digital images

An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far

Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques

Edge detection involves identifying and tracing the sudden sharp discontinuities to extract meaningful information from an image. The purpose of this paper is to improve detecting the leukaemia edges in the blood cell image. Toward this end, two distinctive procedures are developed which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localisation of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared to determine the best edge detection method. The results revealed that the Prewitt edge detection method produced an optimal performance for detecting edges of leukaemia cells with a value of 107%. Meanwhile, the ACO, Sobel and Robert yielded performance results of 76%, 102% and 93% respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method

A Comparative Study on Whole Body Vibration (WBV) Comfort towards Compact Car Model through Data Mining Approach

Nowadays people of Malaysian spend a significant amount of time traveling by the vehicle to travel from one location to another location, and this could be the main reason to decrease minimal vibration for the comfort level in transportation. The vibration that generated while driving can influence pressure and eliminate the focus to the driver and passenger, and this is one of the main causes that can lead accidents on the roads. In this study, we investigate the effect of the vibration caused by the tire interaction with the road surface. The methodology focuses on the trends which occur on the vibration exposure that has been generated throughout the engine operating rpm range in both stationary and nonstationary conditions. An equation will be approached through the analysis to find the significant data that can be used in the process which is K-Means algorithm. Based on the trends of the experienced and exposed vibration, the model is able to differentiate the level of comfort between the clusters by grouping the level of vibration into five categories. To review the accuracy of classification data cluster, the K-Nearest Neighbor method and Analysis Linear Discriminant is used for shows the percentage accuracy of classification data have been a cluster. Later, the vibration for the three cars in this study which has analyzed, compared using the approach of analysis of variations (ANOVA)

Progression approach for image denoising

Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. Therefore, this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. This technique permits the narrowing of the gap between the original and the restored images, visually and quantitatively by adopting the mathematical concept ''arithmetic progression''. Through this paper, this concept is integrated into the image denoising, due to its ability in modelling the variation of pixels’ intensity in the image. The principle of the proposed denoising technique relies on the precision, where it keeps the uncorrupted pixels by using effective noise detection and converts the corrupted pixels by replacing them with other closest pixels from the original image at lower cost and with more simplicity

An overview of the fundamental approaches that yield several image denoising techniques

Digital image is considered as a powerful tool to carry and transmit information between people. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge for the researchers in this field. A lot of image denoising techniques have been introduced in order to remove the noise by taking care of the image features; in other words, getting the best similarity to the original image from the noisy one. However, the findings are still inconclusive. Beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), there is also the scarcity of review papers which carry an important role in the development and progress of research. Thus, this review paper intorduce an overview of the different fundamental approaches that yield the several image-denoising techniques, presented with a new classification. Furthermore, the paper presents the different evaluation tools needed on the comparison between these techniques in order to facilitate the processing of this noise problem, among a great diversity of techniques and concepts

An Experimental Framework for Assessing Emotions of Stroke Patients using Electroencephalogram (EEG)

Abstract: This research aims to assess the emotional experiences of stroke patients using Electroencephalogram (EEG) signals. Since emotion and health are interrelated, thus it is important to analyse the emotional states of stroke patients for neurofeedback treatment. Moreover, the conventional methods for emotional assessment in stroke patients are based on observational approaches where the results can be fraud easily. The observational-based approaches are conducted by filling up the international standard questionnaires or face to face interview for symptom recognition from psychological reactions of patients and do not involve experimental study. This paper introduces an experimental framework for assessing emotions of the stroke patient. The experimental protocol is designed to induce six emotional states of the stroke patient in the form of video-audio clips. In the experiments, EEG data are collected from 3 groups of subjects, namely the stroke patients with left brain damage (LBD), the stroke patients with right brain damage (RBD), and the normal control (NC). The EEG signals exhibit nonlinear properties, hence the non-linear methods such as the Higher Order Spectra (HOS) could give more information on EEG in the signal’s analysis. Furthermore, the EEG classification works with a large amount of complex data, a simple mathematical concept is almost impossible to classify the EEG signal. From the investigation, the proposed experimental framework able to induce the emotions of stroke patient and could be acquired through EEG

A computational approach for optimizing vehicles' interior noise and vibration

This paper proposes a Genetic Algorithm (GA) to optimise vehicles’ interior noise and vibration caused by powertrain, tire-road surface interaction and type of car. Toward this end, an experimental design was carried out to obtain the noise and vibration data of three local compact-sized cars at stationary and running conditions and varying engine speeds. The acquired data were analysed to obtain sound quality parameters such as loudness and sharpness, sound pressure level and vibration exposures in the interior cabin. Besides that, a K-means clustering algorithm was utilised to cluster the noise and vibration to determine the comfort level in the vehicle’s interior cabin. The overall findings indicate that the comfort level is influenced by the types of road surface, powertrain and vehicle design. The results also indicate that the proposed GA approach is reliable and can be utilised by automotive researchers to identify the optimal Noise, Vibration and Harshness (NVH) values for vehicle refinement and noise control

The study of particle filter for satellite angular rate estimation without rate sensor measurement

This paper studies particle filter algorithm to estimate the angular rate of a satellite without the rate sensor measurements. In this work, the performance of the algorithm is studied in terms of capability to estimate the angular rate by using the Euler angles attitude information only. The effects of the number of particles on the algorithm performance are also investigated in terms of accuracy and computational aspects. The performance of the particle filter algorithm is verified using real flight data of Malaysian satellite

The study of particle filter for satellite angular rate estimation without rate sensor measurement

This paper studies particle filter algorithm to estimate the angular rate of a satellite without the rate sensor measurements. In this work, the performance of the algorithm is studied in terms of capability to estimate the angular rate by using the Euler angles attitude information only. The effects of the number of particles on the algorithm performance are also investigated in terms of accuracy and computational aspects. The performance of the particle filter algorithm is verified using real flight data of Malaysian satellite