In order to develop an efficient and safe road there are many methods have been implemented to measure the volume of traffic, to evaluate the road safety level and the others. However based on current practices these methods are very costly as well as complicated. In this paper we present the outcomes of the evaluation on several geosocial networks and transportation networks such as Twitter, Google Map and Waze. The evaluations have been done on the architecture, data inputs and outputs. These findings may give an overview on how all these methods work and how the outputs might be used to improve future road planning.
Abstract-Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space, inspired by the social behaviour of some animal species. However, it has its limitations such as being trapped into a local optima and having a slow rate of convergence. In this paper, a new method of creating a combination of a developed Accelerated PSO and a new modulated inertia coefficient for the velocity update has been proposed. Random term based on particle neighbourhood has been added in the position update formula, inspired by the Artificial Bee Colony (ABC) algorithm. To verify the proposed modified PSO, experiments were conducted on several benchmark optimization problems. The results show that the proposed algorithm is superior in comparison with standard PSO and accelerated PSO algorithms.Keyword-Velocity Update, Global Best, Modulated Inertia, Particle Swarm Optimization I. INTRODUCTION Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space. It is an optimization method inspired by social behaviour of fish schooling and birds flocking and was defined by Kennedy and Eberhart in 1995 [1]. PSO is inspired by general artificial life and random search methods applied in evolutionary algorithm [2]. When travelling in a group, individual birds and fishes have the ability to move without colliding with each other. This is achieved by having each member follow its own group and adjust its position and velocity using the group information, thereby reducing the burden of individual's effort in searching the target (food, shelter). Particle swarm optimization is quite similar to genetic algorithm because both are population-based and are equally effective [2]. The advantage of the PSO method lies in its lower complexity while having comparable performance as there are only a few parameters to be adjusted and manipulated. It also has better computational efficiency, need less memory space, and is less dependent on the CPU speed. Another advantage of PSO over derivative-based local search methods is that when solving a complicated optimization problem, the gradient information is not needed to perform the iterative search.
<p>This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece. </p>
This paper presents a comprehensive review of the development of magnetic flux leakage (MFL) applied by the researcher to improve existing methodology and evaluation techniques in MFL sensor development for corrosion detection in Above Storage Tanks (ASTs). MFL plays an important role in Non-Destructive (NDT) testing to detect crack and corrosion in ferromagnetic material. The demand for more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to major catastrophic upon questionable signal analysis. The accuracy of the MFL signal is crucial in validating the proposed method used in MFL sensor development. This is because the size, cost, efficiency, and reliability of the overall MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a reliable analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the overall performance of the system. This paper also discusses the advantages and disadvantages of major types of MFL sensors used in NDT based on the working principle and sensitivity on the abrupt signal acquisition. The application of the Artificial Neural Network (ANN) and Finite Element Method (FEM) also discussed to identify the impact on the credibility of the MFL signal.
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