Morphological computation of multi-gaited robot locomotion based on free vibration.

In recent years, there has been increasing interest in the study of gait patterns in both animals and robots, because it allows us to systematically investigate the underlying mechanisms of energetics, dexterity, and autonomy of adaptive systems. In particular, for morphological computation research, the control of dynamic legged robots and their gait transitions provides additional insights into the guiding principles from a synthetic viewpoint for the emergence of sensible self-organizing behaviors in more-degrees-of-freedom systems. This article presents a novel approach to the study of gait patterns, which makes use of the intrinsic mechanical dynamics of robotic systems. Each of the robots consists of a U-shaped elastic beam and exploits free vibration to generate different locomotion patterns. We developed a simplified physics model of these robots, and through experiments in simulation and real-world robotic platforms, we show three distinctive mechanisms for generating different gait patterns in these robots.

Generation of conjugate meshes for complex geometries for coupled multi-physics simulations

Over recent years we have developed and published research aimed at producing a meshing, geometry editing and simulation system capable of handling large scale, real world applications and implemented in an end-to-end parallel, scalable manner. The particular focus of this paper is the extension of this meshing system to include conjugate meshes for multi-physics simulations. Two contrasting applications are presented: export of a body-conformal mesh to drive a commercial, third-party simulation system; and direct use of the cut-Cartesian octree mesh with a single, integrated, close-coupled multi-physics simulation system. Copyright © 2010 by W.N.Dawes.

Implementation of multi-group cross-section methodology in BGCore MC-depletion code

BGCore is a software package for comprehensive computer simulation of nuclear reactor systems and their fuel cycles. The BGCore interfaces Monte Carlo particles transport code MCNP4C with a SARAF module - an independently developed code for calculating in-core fuel composition and spent fuel emissions following discharge. In BGCore system, depletion coupling methodology is based on the multi-group approach that significantly reduces computation time and allows tracking of large number of nuclides during calculations. In this study, burnup calculation capabilities of BGCore system were validated against well established and verified, computer codes for thermal and fast spectrum lattices. Very good agreement in k eigenvalue and nuclide densities prediction was observed for all cases under consideration. In addition, decay heat prediction capabilities of the BGCore system were benchmarked against the most recent edition of ANS Standard methodology for UO2 fuel decay power prediction in LWRs. It was found that the difference between ANS standard data and that predicted by the BGCore does not exceed 5%.

Interactive multi-objective particle swarm optimisation using decision space interaction

The most common approach to decision making in multi-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to multi-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO. © 2013 IEEE.

Training and assignment of multi-skilled workers for implementing seru production systems

Confronted with high variety and low volume market demands, many companies, especially the Japanese electronics manufacturing companies, have reconfigured their conveyor assembly lines and adopted seru production systems. Seru production system is a new type of work-cell-based manufacturing system. A lot of successful practices and experience show that seru production system can gain considerable flexibility of job shop and high efficiency of conveyor assembly line. In implementing seru production, the multi-skilled worker is the most important precondition, and some issues about multi-skilled workers are central and foremost. In this paper, we investigate the training and assignment problem of workers when a conveyor assembly line is entirely reconfigured into several serus. We formulate a mathematical model with double objectives which aim to minimize the total training cost and to balance the total processing times among multi-skilled workers in each seru. To obtain the satisfied task-to-worker training plan and worker-to-seru assignment plan, a three-stage heuristic algorithm with nine steps is developed to solve this mathematical model. Then, several computational cases are taken and computed by MATLAB programming. The computation and analysis results validate the performances of the proposed mathematical model and heuristic algorithm. © 2013 Springer-Verlag London.

Production planning of multi-stage multi-option seru production systems with sustainable measures

With the concerns over climate change and the escalation in worldwide population, sustainable development attracts more and more attention of academia, policy makers, and businesses in countries. Sustainable manufacturing is an inextricable measure to achieve sustainable development since manufacturing is one of the main energy consumers and greenhouse gas contributors. In the previous researches on production planning of manufacturing systems, environmental factor was rarely considered. This paper investigates the production planning problem under the performance measures of economy and environment with respect to seru production systems, a new manufacturing system praised as Double E (ecology and economy) in Japanese manufacturing industries. We propose a mathematical model with two objectives minimizing carbon dioxide emission and makespan for processing all product types by a seru production system. To solve this mathematical model, we develop an algorithm based on the non-dominated sorting genetic algorithm II. The computation results and analysis of three numeral examples confirm the effectiveness of our proposed algorithm. © 2014 Elsevier Ltd. All rights reserved.