Wan Zuki Azman Wan Muhamad

Optimization of noise and vibration in passenger car cabin

Vibration and sound in the comfort of the interior of the car are all factors which are normally considered by the customer to buy a vehicle. Basically a vehicle with a lot of vibration and noise has to be comfortable not adversely affect the comfort optimum for the vehicle acoustical comfort index (VACI) in the interior of the car. With reference to the relationship between the VACI the VDV is dependent while driving. Normally, the sound produced by the motor system can cause damage to human hearing system, stress, harassment and sometimes can reduce the concentration of driving that will result in a road accident. Since the vibration is a major contributor to noise levels, a study was conducted to obtain the vibration dose value (VDV) is on engine speed, a mathematical model was constructed to find the optimum value for the VDV to gain VACI better value by satisfying all the equations and constraints are identified based on the characteristics of the noise and vibration. By using optimization model is generated, automotive researchers will be able to make a benchmark for the vibration dose value for the index the better acoustic comfort in the interior of the vehicle.

Linear programming model for optimizing of noise and vibration in passenger car cabin

Car cabin acoustical comfort is one of the factors which attract the buyer prospective buyer on purchasing a new vehicle. Basically the amount of discomfort depends to magnitude, frequency, direction and also the duration of exposed vibration in the cabin. The comfort of the driving influences driving performance. Generally the vibration is caused by two main sources: engine transmission and interaction between tyre and road surface. In this study the effects of vibration to noise in passenger car cabin were investigated. Vehicle acoustical comfort index (VACI) was used to evaluate the noise annoyance level and vibration dose value (VDV) was used to evaluate the vibration level. By using the changes trend of noise and vibration level depending to engine speeds, LP model was used to optimise the vibration level in the passenger car cabin.

Multi Objective Optimization of Noise and Vibration in Passenger Car Cabin by Using Goal Programming Approach

Car cabin acoustical comfort is one of the main points which may attract the customers to purchase a new vehicle. Basically the noise in passenger car cabin is closely related to generated vibration in the vehicle system. The amount of discomfort depends to magnitude, frequency, direction and also the duration of exposed vibration in the cabin. The comfort of the driving influences driving performance. Generally the vibration is caused by two main sources: engine transmission and interaction between tyre and road surface. In this study the effects of vibration to noise in passenger car cabin were studied. Vehicle acoustical comfort index (VACI) was used to evaluate the noise annoyance level and vibration dose value (VDV) was used to evaluate the vibration level. According to the changes trend of noise and vibration level depending to engine speeds, goal programming model was used to optimise the noise annoyance level in passenger car cabin. At the end of study, a multi objective model successfully built to optimize the noise annoyance levels by looking the required vibration dose value at certain engine speeds. The results of the study might be useful for automotive researchers to provide technical method to optimize acoustical comfort in manufacturing of vehicles.

Genetic Algorithm Method in Examination Timetabling Problem: A Survey

Solving a real-world university examination timetabling is an intractable problem. Examination timetabling problems (ETP) can be defined as a problem of assigning a set of final examinations schedules to a given number of timeslots and rooms subject to a set of constraints. These constraints are usually divided into hard and soft constraints. Hard constraints must be satisfied under any circumstances. Whilst, soft constraints are not only essential but also should be satisfied as much as possible. Due to the complexity of the problem, it is usually impossible to have solutions that satisfy all soft constraints. The quality of the timetable is measured based on soft constraints violation. A weighted penalty/value is attached to each violation of the soft constraint and the objective is to minimize the total penalty value of these violations. Although there have been a numerous approaches in solving the ETP, but this paper only focus on Genetic Algorithm (GA) method based on the fact that it is robust and can fits into complex problem space. This paper gives a summary of the GA method on ETP and it aims in helping other researcher to shorten their time in understanding the application of GA in the selected problem.

Random binary search algorithm based feature selection in Mahalanobis Taguchi system for breast cancer diagnosis

Breast cancer is becoming the major factor of death amongst women in the world. However, it is found that longer lifespan of this disease’s patients can be guaranteed through early detection and accurate diagnosis of this disease. When it comes to the treatment given to patients, a doctor needs to put his/her knowledge and experience in practice by specifying the source of the suspected disease (out of a list of the possible causes with similar symptoms). This is followed by confirming the diagnosis through a number of tests. Therefore, identifying the disease without receiving assistance from intelligence systems is time consuming. The objective of this study is to introduce the intelligence system which develops Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS). It is also with the purpose to validate the techniques of feature selection problems which are computationally efficient, and to apply Random Binary Search algorithm in solving medical classification problems. In this study, in order to improve the step of choosing the most useful variables, Random Binary Search (RBS) algorithm is proposed, which is incorporated between MTS. Besides being a relatively new statistical methodology where various mathematical concepts are combined, MTS is used in the field of diagnosis and classification in multidimensional systems. It is also a highly efficient method, and it has been utilized in a wide range of disciplines such as engineering, medical, financial, and more. Datasets of medical fields, which were concerning cancer, diabetes and hepatitis, were used in this study. Besides, binary class classification problems were also represented by these data sets.Breast cancer is becoming the major factor of death amongst women in the world. However, it is found that longer lifespan of this disease’s patients can be guaranteed through early detection and accurate diagnosis of this disease. When it comes to the treatment given to patients, a doctor needs to put his/her knowledge and experience in practice by specifying the source of the suspected disease (out of a list of the possible causes with similar symptoms). This is followed by confirming the diagnosis through a number of tests. Therefore, identifying the disease without receiving assistance from intelligence systems is time consuming. The objective of this study is to introduce the intelligence system which develops Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS). It is also with the purpose to validate the techniques of feature selection problems which are computationally efficient, and to apply Random Binary Search algorithm in solving medical classification prob...

Reconstruction of Medical Images Using Artificial Bee Colony Algorithm

The goal of this study is to assess the efficiency of Artificial Bee Colony (ABC) algorithm in finding the optimal solution of curve fitting problem specifically for medical images. Data of Computed Tomography (CT) images from two different patients were collected. The procedure of curve fitting for medical images include conversion of Digital and Communications in Medicine (DICOM) images to binary images, boundary and corner point detection, parameterization, and curve reconstruction by using ABC algorithm. Then, Sum Square Error (SSE) was used to calculate the distance of the fitted Cubic Bezier curve with the boundary of the original images. Based on the calculation and parameter tuning that had been done, the smallest error of both skulls is and , respectively. The finding of this study illustrated that the proposed method had efficiently produced fitted Bezier curve that resemble the original medical images. In addition, the used of Douglas Peucker algorithm helps to improve the performance of the proposed method since computational time can be minimized. This study had shown that the proposed method can be used as an alternative method in order to reconstruct or redesigned the medical images since it produces a small error. For future work, we are planning to explore and applied the ABC algorithm to reconstruct the missing part of the skull since it can reduce the time taken to produce the skull implant as well as reducing the cost of producing it.

Combination of feature selection approaches with random binary search and Mahalanobis Taguchi System in credit scoring

The wide usage of credit scoring is to determine credit, credit analysis cost reduction, and faster decision making. However, traditional credit scoring models are not the factor for the influence of noises. There are several objectives of this study: 1) to develop Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS), 2) to validate the techniques of feature selection problems which are computationally efficient, and 3) to apply Random Binary Search algorithm in solving credit problems. In this study, Random Binary Search (RBS) algorithm was proposed, which was incorporated between MTS. The purpose of this was to optimize the procedures for determining the most useful variables. Besides being a relatively new statistical methodology where various mathematical concepts are integrated, there is an involvement of MTS in the field of diagnosis and classification in multidimensional systems. Besides, it is a highly effective method and has been implemented to a range of di...

A robust correlation method to detect heterogeneous heart valve symptoms

Heart valve disease affects a large number of patients. During the past decade, major advances have occurred in diagnostic techniques of heart valve disease. In this paper, we present an alternative method in classifying heart valve disease using correlation analysis and neural network classifier based on heart sound signal. The heart sound signals used in this study were taken from heart sound manipulator software. First, the signal was converted into frequency domain. Then, power spectrum of the sample is determined and cross-correlated with a reference sample (also in power spectrum form) to get different pattern of correlation plot. Seven different heart sounds of normal and other abnormal sounds from heart valve disease were classified into their classes. The result shows that 98.70% of the samples had been correctly classified by the system.

Effect of single step pretreatment on cobalt contents and surface roughness of tungsten carbide substrate prior to diamond coating

Adhesive strength is a critical issue for super hard diamond coatings on tungsten carbide (WC-Co). The presence of Co in the cemented tungsten carbide substrate has a negative influence on the diamond deposition process. High Co content in the substrates has proved to be very deteriorating for the adhesion of diamond coatings. The surface treatments (single step pretreatment) leading to both Co removal and the roughening of the substrate surface can ensure adequate adhesion levels of the diamond coatings deposited onto WC-Co substrates. The factors investigated were acid temperature, etching time, concentration of H2O2 and concentration H2SO4. The Co content and surface roughness were the response variables investigated. The experimental plan was based on the face central composite design (CCD). The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The acid temperature is the most significant factor that influences the surface roughness and the Co content. The etching time provided secondary contribution to the responses investigated. Additionally, the square of the acid temperature also provided secondary contribution to the surface roughness. The reduced quadratic model developed using RSM were reasonably accurate and can be used for prediction within the limits of the factor investigated.

An Optimization Model of Noise and Vibration in Passenger Car Cabin

Car cabin acoustical comfort is one of the factors which attract the buyer prospective buyer on purchasing a new vehicle. Basically the amount of discomfort depends to magnitude, frequency, direction and also the duration of exposed vibration in the cabin. The comfort of the driving influences driving performance. Generally the vibration is caused by two main sources: engine transmission and interaction between tyre and road surface. In this study the effects of vibration to noise in passenger car cabin were investigated. Vehicle acoustical comfort index (VACI) was used to evaluate the noise annoyance level and vibration dose value (VDV) was used to evaluate the vibration level. By referring to the noise and vibration trend to engine speeds, an optimization model was built to find the optimal VDV value against the engine speed in order to obtain required maximum noise (VACI) in the passenger car cabin. Thus, a VACI-VDV reference table formed to assist automotive researchers to reduce the vibration and at the same time to reduce noise annoyance level in passenger car cabin.