Yoichi Shiraishi

Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H_MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H_MCOP

Multi constrained Route Optimization for Electric Vehicles using SimE

Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5N (N is the population size and N ≥ 2 and generally N = 20) times lesser memory than the GA.

A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem

Multi-objective shortest path (MOSP) problem aims to find the shortest path between a pair of source and a destination nodes in a network. This paper presents a stochastic evolution (StocE) algorithm for solving the MOSP problem. The proposed algorithm is a single-solution-based evolutionary algorithm (EA) with an archive for storing several non-dominant solutions. The solution quality of the proposed algorithm is comparable to the established population-based EAs. In StocE, the solution replaces its bad characteristics as the generations evolve. In the proposed algorithm, different sub-paths are the characteristics of the solution. Using the proposed perturb operation, it eliminates the bad sub-paths from generation to generation. The experiments were conducted on huge real road networks. The proposed algorithm is comparable to well-known single-solution and population-based EAs. The single-solution-based EAs are memory efficient, whereas, the population-based EAs are known for their good solution quality. The performance measures were the solution quality, speed and memory consumption, assessed by the hypervolume (HV) metric, total number of evaluations and memory requirements in megabytes. The HV metric of the proposed algorithm is superior to that of the existing single-solution and population-based EAs. The memory requirements of the proposed algorithm is at least half than the EAs delivering similar solution quality. The proposed algorithms also executes more rapidly than the existing single-solution-based algorithms. The experimental results show that the proposed algorithm is suitable for solving MOSP problems in embedded systems.

Simulations of pattern dynamics for reaction-diffusion systems via SIMULINK

BackgroundInvestigation of the nonlinear pattern dynamics of a reaction-diffusion system almost always requires numerical solution of the system’s set of defining differential equations. Traditionally, this would be done by selecting an appropriate differential equation solver from a library of such solvers, then writing computer codes (in a programming language such as C or Matlab) to access the selected solver and display the integrated results as a function of space and time. This “code-based” approach is flexible and powerful, but requires a certain level of programming sophistication. A modern alternative is to use a graphical programming interface such as Simulink to construct a data-flow diagram by assembling and linking appropriate code blocks drawn from a library. The result is a visual representation of the inter-relationships between the state variables whose output can be made completely equivalent to the code-based solution.ResultsAs a tutorial introduction, we first demonstrate application of the Simulink data-flow technique to the classical van der Pol nonlinear oscillator, and compare Matlab and Simulink coding approaches to solving the van der Pol ordinary differential equations. We then show how to introduce space (in one and two dimensions) by solving numerically the partial differential equations for two different reaction-diffusion systems: the well-known Brusselator chemical reactor, and a continuum model for a two-dimensional sheet of human cortex whose neurons are linked by both chemical and electrical (diffusive) synapses. We compare the relative performances of the Matlab and Simulink implementations.ConclusionsThe pattern simulations by Simulink are in good agreement with theoretical predictions. Compared with traditional coding approaches, the Simulink block-diagram paradigm reduces the time and programming burden required to implement a solution for reaction-diffusion systems of equations. Construction of the block-diagram does not require high-level programming skills, and the graphical interface lends itself to easy modification and use by non-experts.

Crack width prediction of RC structures by Artificial Neural Networks

This paper proposes the use of Artificial Neural Networks (ANN) for the prediction of the maximum surface crack width of precast reinforced concrete beams joined by steel coupler connectors and anchor bars (jointed beams). Two different training algorithms are used in this study and their performances are compared. The first approach used Back propagation (BPANN) and the second one includes Genetic Algorithms (GANN) during the training process. Input and output vectors are designed on the basis of empirical equations available in the literature to estimate crack widths in common reinforced concrete (RC) structures and parameters that characterize the mechanical behavior of RC beams with overlapped reinforcement. Two well-defined points of loading are considered in this study to demonstrate the suitability of this approach in both, a linear and a highly nonlinear stage of the mechanical response of this type of structures. Remarkable results were obtained, however, in all cases the combined Genetic Artificial Neural Network approach resulted in improved prediction performance over networks trained by error back propagation.

Finding Multi-Objective Shortest Paths Using Memory-Efficient Stochastic Evolution Based Algorithm

Multi-objective shortest path (MOSP) computation is a critical operation in many applications. MOSP problem aims to find optimal paths between source and destination nodes in a network. This paper presents a stochastic evolution (StocE) based algorithm for solving the MOSP problem. The proposed algorithm works on a single solution and is memory efficient than the evolutionary algorithms (EAs) that work on a population of solutions. In the proposed algorithm, different sub-paths in the solution are considered as its characteristics and bad sub paths are replaced by good sub-paths from generation to generation. The proposed algorithm is compared with non-dominated sorting genetic algorithm-II (NSGA-II), micro genetic algorithm (MicroGA), multi-objective simulated annealing (MOSA), and a straight-forward StocE. The comparison results show that the proposed algorithm generally performs better than the other algorithms that works on a single solution (i.e. MOSA and straight-forward StocE) and also infrequently performs better than the algorithms that work on a population of solutions (i.e. NSGA-II and MicroGA). Therefore, our proposed algorithm is suitable to solve MOSP in embedded systems that have a limited amount of memory.

On-line measurement system for internal resistance in lead acid battery

An on-line measurement system for internal resistance of battery is developed as an IoT (Internet of Things) device in order to evaluate the effect of pulse generation which is expected to prolong the life of lead acid battery. This system consists of a pulse control sub-system and a monitoring and recording sub-system. The pulse control sub-system generates a controlling pulse with its specified width and cycle. The monitoring and recording sub-system has an SD card drive recording the measurement data for the backup as well as an Ethernet interface transmitting them when measured. The proposed on-line measurement system is implemented by the combination of one-board microcomputer and some peripheral circuits. In the experiments, the internal resistances and the voltages of the battery, temperature and humidity of the air and measured-times are actually measured once every hour for three weeks. The measurement data are successfully recorded in the SD card and are transmitted to the server at the same time. Moreover, the operating status can be checked at any time by way of web browser.

Solution Space Reduction of Simulated Evolution Algorithm for Solving Standard Cell Placement Problem

Simulated Evolution algorithm is versatile, efficient but very much time consuming. This paper shows that the reduction of trials in the allocation phase leads to the improvement of the performances of Simulated Evolution algorithm. In its application to the cell placement problem of VLSI chip, 90% reduction of the solution space in the allocation phase accelerates the total processing time, 4.6 ~ 7.7 times and improves the solution quality, 1.6 ~ 6.4% when compared with an exhaustive search of the solution space. This result is also useful for implementing Simulated Evolution algorithm on an FPGA.

Pressure transfer function for aorta model in cardiovascular simulator: Feasibility study of wearable central blood-pressure gauge

A noninvasive estimation of central blood-pressure is required by the researchers or the medical practitioners in the cardiovascular field. Up to now, a simulation based approach has been pursued by many researchers and the practical results are obtained. As the progresses in the electrical engineering, the possibility that a wearable central blood-pressure gauge can be realized is increased. This study shows that the aorta model in the cardiovascular simulator can be equivalently transformed to the pressure transfer function and gives the feasibility of wearable central blood-pressure gauge.

Simulation based defect estimation of metal pole by analyzing hammering sounds

This paper proposes a new measurement metric for estimating the defects in a metal pole by analyzing its hammering sounds. The validity of this measurement metric is supported by the simulation which is based on the Stress Harmonic Analysis. Moreover, the simulator is constructed by applying Model Based Approach and this makes it possible to extend the simulator to be applicable to some variations of metal poles. The configuration of the defect estimation system, the proposed measurement metric and the details of Stress Harmonic Analysis are described. As a result, the possibility of applying the proposed new measurement metric is increased in the actual defect estimation of metal poles.