SDF classifier revisited

This paper is addressing problems related to the construction of classifiers based on the Similarity Discriminant Function (SDF), in which the traditional vector representation of a pattern is replaced with matrix data. We introduce potential modifications of the matrix data structure and propose new variants of the SDF. The algorithms that we present were tested on images of handwritten digits and on photographs of human faces, taken from the ORL and CMU‐PIE databases. The results of experiments show that our modifications significantly improved the performance of the original SDF classifier.     

Ultrasonic inspection of austenitic steel joints by coupled‐head scanning

This work covers short-circuit metal transfer in the MIG/MAG process, where the current is the direct regulation variable, in contrast with the conventional mode in which the direct regulation variable is the voltage. It sets out the general theory for this technique and describes a new system, not yet commercially offered, developed by LABSOLDA at UFSC. The control bases of the process are described, presenting oscillograph results and performance results in relation to root passes and production of splatter. In addition, some results from an existing commercial system are set out with the aim of making direct comparisons between the control strategies and the implications for the largest dependent factors, such as drop diameters, average current, arc efficiency and arc power. Questions are posed such as the criteria for monitoring the variables and the calculations for arc power, which are as important for these new technologies as they are in the case of conventional systems.     

Mechanics of Oil Sands Slurry Flow in a Flexible Pipeline System

Slurry transportation is an economic haulage system in oil sands and coal-mining operations characterized by long haulage distances and rugged terrain. In such conditions, the ton-km-hr limits are exceeded creating extreme tire wear and high maintenance costs. Steep haul grades and rugged terrain also cause mechanical wear and tear, which decrease haulage equipment economic life. Hydraulic transportation is a proven and viable technology for slurry transportation in such conditions. Currently, stationary pipeline transportation is being used in transporting minerals in many mines. There is an increasing demand to create slurrified minerals at the mining faces to be transported to the processing plant. However, stationary pipelines are not capable for dealing with the rapidly changing configuration of the mining faces. In this paper, the authors develop the ground articulating pipeline (GAP) technology to address this problem. The GAP system consists of pipelines connected together with flexible joints in each pipe section, which allows deflection to avoid torsional stresses from the adjoining frames. This flexible arrangement accommodates the horizontal and vertical displacements of the mobile system as it follows the hydraulic shovels in the excavation process. The mechanics of the GAP system, as well as the production–economic function, are formulated and simulated over an extended period using data and information from Syncrude’s North Mine. The results show that the GAP system is technically and economically viable for productivity between 6,300 and 6,500 tons per hour. The simulated head loss for the GAP system is 15.66 m per 400 m, which compares with 20 m per 400 m for the existing stationary system at Syncrude. The pressure gradient-radius curves are asymptotic to the pipe boundaries, which indicates steep axial pressure gradient in these areas.     

Regulation through comparative performance evaluation

Ofwat used comparative performance regulation in the 1994 periodic review of prices. We consider such a regulatory mechanism when there are technological spillovers. The regulator cannot observe firms effort and must design a regulatory contract which induces the socially optimal level of cost reduction. The extent to which cost reducing activities are non-appropriable is shown to affect the optimal choice of regulation. In general, comparative performance regulation is optimal and yardstick competition emerges only as a special case. However, when spillovers are spread evenly across an industry, an optimal comparative regime does not exist.     

MULSOPS: Multivariate Optimized Pit Shells Simulator for tactical mine planning

Abstract

Production planning, scheduling and allocation of resources in large-scale surface mining operations present a great challenge to mine planning engineers. Ore and waste extraction plans must be executed to achieve tactical objectives using appropriate tools. Many production planning and scheduling and resource allocation methods are based on trial and error, crisis management or subjective judgements with no detailed economic basis or mathematical rigour. In addition, these methods do not consider the random processes governing critical development and production variables. In this study, the authors develop a multivariate pit shell simulator, MULSOPS, which addresses these problems. Rigorous geometric formulations of the ellipsoidal approximations of the pit shells geometry, their expansions and sequential interactions are modeled to mimic material displacement dynamics in an open pit operation. Stochastic and numerical modeling techniques are used to provide solutions to the time-dependent geometric models in random multivariate states Under different production and economic paradigms, the geometric models are simulated to yield the source and characteristics of appropriate cuts. Combined production from successive exposed cuts provides periodic targets for tactical planning. Variance simulation is also used to provide analysts with sensitive stochastic variables for input data definition and tight production target tolerance. A numerical example is used to illustrate the use of MULSOPS for tactical planning in a typical open pit operation.     

Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation

We introduce a new architecture of feed-forward neural networks called hybrid fuzzy set-based polynomial neural networks (HFSPNNs) that are composed of heterogeneous feed-forward neural networks such as polynomial neural networks (PNNs) and fuzzy set-based polynomial neural networks (FSPNNs). We develop their comprehensive design methodology by embracing mechanisms of genetic optimization and information granulation. The construction of information granulation-driven HFSPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting information granulation-driven genetically optimized HFSPNN results from a synergistic usage of the hybrid system generated by combining original fuzzy set-based polynomial neurons (FSPNs)-based FSPNN with polynomial neurons (PNs)-based PNN. The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling (GMDH) whose some essential parameters of the network being tuned with the use of genetic algorithms throughout the overall development process. Two general optimization mechanisms are explored. First, the structural optimization is realized via GAs while the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through extensive experimentation where we considered a number of modeling benchmarks (synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling).     

PEM: The small company-dedicated software process quality evaluation method combining CMMISM and ISO/IEC 14598

Many small software organizations have recognized the need to improve their software product. Evaluating the software product alone seems insufficient since it is known that its quality is largely dependant on the process that is used to create it. Thus, small organizations are asking for evaluation of their software processes and products. The ISO/IEC 14598-5 standard is already used as a methodology basis for evaluating software products. This article explores how it can be combined with the CMMI to produce a methodology that can be tailored for process evaluation in order to improve their software processes. ^SM: CMMI is a service mark of Carnegie-Mellon University. Sylvie Trudel has over 20 years of experience in software. She worked for more than 10 years in development and implementation of management information systems and embedded real-time systems. Since 1996, she works as a process improvement specialist, implementing best practices into organizations processes from CMM and CMMI models. She performed several CMM and CMMI assessments and participated in many other CMM assessments such as CBA IPI, SCE, and other proprietary methods. She obtained a bachelors degree in computer science in 1986 from Laval University in Québec City and a Masters degree in Software Engineering at école de Technologie Supérieure (éTS) in Montréal. Sylvie is currently working as a software engineering advisor at the Centre de Recherche Informatique de Montréal (CRIM). Jean-Marc Lavoie has been working in software development for over 10 years. He performed and published a comparative study between the guide to the SWEBOK and the CMMI in 2003. Jean-Marc obtained a bachelor degree in Electrical Engineering. He is pursuing a Masters degree in Software Engineering at école de Technologie Supérieure (éTS) in Montréal while working as a software architect at Trisotech. Marie-Claude Pare has been working in software development for 7 years. Marie-Claude obtained a bachelor degree in Software Engineering from école Polytechnique in Montréal. She is pursuing a Masters degree in Software Engineering at école de Technologie Supérieure (éTS) in Montréal while working as a software engineer at Motorola GSG Canada. Dr Witold Suryn is a Professor at the école de technologie supérieure, Montreal, Canada (engineering school of the Université du Québec network of institutions) where he teaches graduate and undergraduate software engineering courses and conducts research in the domain of software quality engineering, software engineering body of knowledge and software engineering fundamental principles. Dr Suryn is also the principal researcher and the director of GELOG : IQUAL, the Software Quality Engineering Research Group at école de technologie supérieure. From October 2003 Dr. Suryn holds the position of the International Secretary of ISO/IEC SC7 – System and Software Engineering.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.