Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem. © 2013 Published by Elsevier Ltd.

A world full of surprises:Bayesian theory of surprise to quantify degrees of uncertainty

In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a "surprising" event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks. Copyright © 2014 ACM.

Practically oriented design projects in mechatronics engineering:a case study of design experiences and outcomes

This paper discusses and presents a case study of a practically oriented design project together with a few examples of implemented design projects recently incorporated into an undergraduate system course at the mechatronics engineering department in Ah-Balqa’ Applied University. These projects have had a positive impact on both the department and its graduates. The focus of these projects is the design and implementation of processor-based system. This helps graduate students cross the border between hardware design and software design. Our case study discusses the research methodology adopted for the physical development of the project, the technology used in the project, and the design experiences and outcomes.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

An intelligent broker agent for energy trading:an MDP approach

This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Processes (MDP) to purchase energy at low prices in a day-ahead power wholesale market, and keeps energy supply and demand balanced. Moreover, we explain how the agent uses Non-Homogeneous Hidden Markov Model (NHHMM) to forecast energy demand and price. An evaluation and analysis of the 2012 Power TAC finals show that AstonTAC is the only agent that can buy energy at low price in the wholesale market and keep energy imbalance low.

Getting round pegs into round holes:getting students onto the right engineering programme

Aston University offers a Foundation year in Engineering and Applied Science. The purpose of this programme is to prepare people with the necessary skills and knowledge required to enrol on an undergraduate programme in Engineering and Applied Science. It is acknowledged there are many misconceptions as to what engineering is. This is further compounded by the lack of knowledge of the different engineering disciplines both by pre-university students and careers teachers [1]. In order to ameliorate this lack of knowledge, Aston University offers a unique programme where students are given the opportunity to have a ?taste? of four Engineering Disciplines: Mechanical Engineering, Electrical Engineering, Chemical Engineering and Computer Science. Alongside these ?taster? sessions, the students study a Professional Skills module where they are expected to keep a portfolio of skills. In their portfolios they comment on their strengths and weakness in relation to six skill areas: independent enquirer, self-manager, effective participator, creative thinker, reflective learner and team worker. The portfolio gives them the opportunity to perform a self-skills audit and identify areas where they have strengths and areas which require work to improve to become a competent professional engineer. They also have talks from engineers who discuss with them their careers and the different aspects of engineering. The purpose of the ?taster? sessions, portfolio and the talks are to encourage the students to critically examine their career aspirations and choose an engineering undergraduate programme which best suits their ambitions and potential skills. The feedback from students has been very positive. The ?taster? sessions have enabled them to make an informed choice as to the undergraduate programme they would like to study. The programme has given them the technical skills and knowledge to enrol on an undergraduate programme and also the skills and knowledge to be a successful learner.