Modern methods in engine knock signal detection

In this paper, a review is given of some of the modern methods in the detection of knock in internal-combustion engines and some comparisons are made between these methods and the effectiveness of each one of them is indicated through a statement of the advantages and disadvantages of each method. In this way it will be possible to clarify how to deal with the original signal and the associated signal noise through some of the modern algorithms in the field of soft computing such as an Artificial Neural Network (ANN), Genetic Algorithms (GA), Wavelet Transform (WT), Fuzzy logic, Supported Vector Machine (SVM) and some statistical methods

Product assembly and disassembly sequence optimization based on genetic algorithm and design for assembly methodologies

Product assembly and disassembly sequences are the most crucial operations in product lifecycle. In this paper, an Artificial Intelligence (AI) technique, namely Genetic Algorithm (GA) is proposed to optimize product components assembly and disassembly sequences.The proposed methodology is developed and tested on an industrial product made of plastics with no integrated assembly and permanent joint parts.GA method is applied to determine the accuracy and optimum results based on 20 assembly and disassembly sequence solutions that was generated by the Design for Assembly methodology.The results indicated that GA based approach is able to obtain a near optimal solution for assembly and disassembly sequences

Rule based ETL (RETL) approach for GEO spatial data warehouse

This paper presents the use of Service Oriented Architecture (SOA) for integrating multi source heterogeneous geospatial data in order to facilitate geospatial data warehouse. In this study, Real Based ETL (RETL) concept is adapted in order to extract, transform and load data from a variety of heterogeneous data sources. ETL will transform data to schematic format and loading data into the Geo spatial data warehouse.By using a rule-based technique, the distribution of parallel ETL pipeline will enhance and perform more efficient in large scale of data and overcome data bottleneck and performance overhead. This can ease the disaster management and enables planners to monitor disaster emergency response in an efficient manner

Penggunaan algorithma genetik untuk menyelesaikan masalah penjadualan

Penjadualan waktu sekolah adalah masalah yang berhubung dengan jadual mingguan bagi kesemua kelas disesebuah sekolah. Masalah ini merangkumi cara bagaimana menentukan satu-satu matapelajaran diajar mengikut period yang akan ditetapkan dengan mengambilkira tiada guru atau kelas bertembung dalam satu-satu masa. Kehendak asas ini biasanya akan melibatkan banyak lagi perkara lain seperti masa pengajaran bagi sesebuah matapelajaran mestilah tersebar dalam masa seminggu.Tujuan utama kertaskerja ini adalah untuk memahami had-had keupayaan Algorithma Genetik dan potensinya dalam menanggani masalah yang mempunyai kekangan yang tinggi, di mana dalam masalah pengoptimuman, sebarang perubahan kecil kepada penyelesaian yang feasible akan menjana kepada satu penyelesaian yang tidak feasible. Sebagai ujian, masalah jadual waktu telah dipilih, di mana innya diketahui adalah NP-hard, tetapi ianya amat praktikal kerana telah banyak penyelidikan telah dibuat yang berkaitan dengannya

The evolution of Geography Markup Language (GML) compression model

This paper will discuss about the evolution of Geography Markup Language (compression model.GML is a type of XML files normally used to store spatial data from database.However due to the huge size processing and transferring this type of file will cost performance and storage issue. Throughout the years several GML file compression model has been developed to help in addressing this problem.Four GML file compression model which are GPress, Delta Sp Compression and Extrapolation Model, GMill, and GPress++ has been selected to be discussed in this paper.In addition a comparison and the enhancement done in each model will be discussed in here.From the assessment GPress++ compression model h significant file compression rate on synthetic dataset with 77.10% improvement on gzip compressor

The Impact of Supply Chain Social Capital on Market Orientation: The Buyer Perspective

Developing the strength of social ties between buying and supplying organizations is increasingly cited as a critical differentiator of high and low performers in global supply chains. While the creation of relational capital within an organization is a relatively well-identified concept in organizational research, comparatively little research exists on the bonding and bridging social ties that create relational value in supply chains. In our research, we extend theoretical models of group social conduits into this context and develop a model that posits the impact of social capital on the reactive and proactive market orientation between buyers and suppliers. Results from our study of 114 technology-based companies in Malaysia suggest that formal social capital are important in the creation of relational capital, which in turn can lead to improving market orientation eventually supplier relationship outcomes. Formal bridging social capital conduits appear to play a lesser role in deriving these benefits

Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control

Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, two attractive tools have emerged recently.These two attractive tools are: the artificial neural networks and the fuzzy logic system. One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes.To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here.Air- fuel ratio represents complex, nonlinear and stochastic behavior.To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air- fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time.This paper describes a fuzzy clustering method to initialize the ANFIS

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Group formation using genetic algorithm

Due to the increasing of complexity in software projects, group work is becoming more important in order to ensure quality software products can be delivered on time.Thus, in universities, group work is seen as a good preparation for students to industry because by working in group, it can reduce the individual workload,improve the ability to manage a project and enhance the problem solving skills. However, due to lack of programming skills especially in Java programming language and the inability to have meetings frequently among the group members,most of the students’ software project cannot be delivered successfully.To solve this problem, systematic group formation is one of the initial factors that should be considered to ensure that every group consists of quality individuals who are good in Java programming and also to ensure that every group member in a group are staying closer to each other.In this research, we propose a method for group formation using Genetic Algorithms, where the members for each group will be generated based on the students’ programming skill and location of residential colleges

Teknik untuk membangunkan jadual waktu sekolah dengan menggunakan komputer

Penjadualan waktu sekolah adalah masalah yang berhubung dengan jadual mingguan bagi kesemua kelas disesebuah sekolah. Masalah ini merangkumi cara bagaimana menentukan semua mata pelajaran diajar mengikut waktu yang akan ditetapkan dengan mengambil kira tiada guru atau kelas bertembung dalam satu-satu masa.Kehendak asas ini biasanya akan melibatkan banyak lagi perkara lain seperti masa pengajaran bagi sesebuah mata pelajaran mestilah tersebar dalam masa seminggu.Tujuan utama kertas kerja ini adalah untuk memahami had-had keupayaan Algoritma Genetik dan potensinya dalam menangani masalah yang mempunyai kekangan yang tinggi.Dalam masalah pengoptimuman, sebarang perubahan kecil kepada penyelesaian yang feasible akan menjana kepada satu penyelesaian yang tidak feasible.Dalam kertas kerja ini, masalah jadual waktu sekolah telah dipilih sebagai ujian kerana ianya diketahui sebagai NP-hard.Ianya juga amat praktikal kerana banyak penyelidikan telah dibuat berkaitan dengan masalah ini