Study of an implicit scheme for integrating Maxwell's equations

We study theoretically and numerically an implicit scheme for solving Maxwell's equations. Space discretization is obtained by the finite element method, while Newmark's scheme provides the time discretization.     

Constrained multibody system dynamics an automated approach

The governing equations for constrained multibody systems are formulated in a manner suitable for their automated, numerical development and solution. Specifically, the “closed loop” problem of multibody chain systems is addressed.

The governing equations are developed by modifying dynamical equations obtained from Lagrange's form of d'Alembert's principle. This modification, which is based upon a solution of the constraint equations obtained through a “zero eigenvalues theorem,” is, in effect, a contraction of the dynamical equations.

It is observed that, for a system with n generalized coordinates and m constraint equations, the coefficients in the constraint equations may be viewed as “constraint vectors” in n-dimensional space. Then, in this setting the system itself is free to move in the n−m directions which are “orthogonal” to the constraint vectors.     

Classifying inventory using an artificial neural network approach

This paper presents artificial neural networks (ANNs) for ABC classification of stock keeping units (SKUs) in a pharmaceutical company. Two learning methods were utilized in the ANNs, namely back propagation (BP) and genetic algorithms (GA). The reliability of the models was tested by comparing their classification ability with two data sets (a hold-out sample and an external data set). Furthermore, the ANN models were compared with the multiple discriminate analysis (MDA) technique. The results showed that both ANN models had higher predictive accuracy than MDA. The results also indicate that there was no significant difference between the two learning methods used to develop the ANN.     

An electronic form system—an experience in prototyping

A prototype electronic form system was implemented to test the feasibility of a high level form definition mechanism. This mechanism is based on the concept of abstract data types in programming languages. This paper briefly discusses prototypes in general and describes the prototype electronic form system—specific objectives, design decisions, the implementation and evaluation. The prototype has proven invaluable in providing a better understanding of the facilities that should be included in a form definition language and of the implementation issues. Success of the prototype can be attributed to keeping prototype objectives in perspective throughout its implementation and ensuring that the objectives were not violated by any design decisions or implementation restrictions.     

Controlled experiment replication in evaluation of e-learning system’s educational influence

We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system’s advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal replication that investigated the effectiveness of intelligent authoring shell eXtended Tutor–Expert System (xTEx-Sys). The initial and the replicated experiment were based on our approach that combines classical two-group experimental design and with factoral design. A trait that distinguishes this approach from others is the existence of arbitrary number of checkpoint-tests to determine the effectiveness in intermediate states. We call it a pre-and-post test control group experimental design with checkpoint-tests. The gained results revealed small or even negative effect sizes, which could be explained by the fact that the xTEx-Sys’s domain knowledge presentation is rather novel for students and therefore difficult to grasp and apply in earlier phases of the experiment. In order to develop and improve the xTEx-Sys, further experiments must be conducted.     

A framework for dynamic implicit curve approximation by an irregular discrete approach

The approximation of implicit planar curves by line segments is a very classical problem. Many algorithms use interval analysis to approximate this curve, and to handle the topology of the final reconstruction. In this article, we use discrete geometry tools to build an original geometrical and topological representation of the implicit curve. The polygonal approximation contains few segments, and the Reeb graph permits to sum up efficiently the shape and the topology of the curve. Furthermore, we propose two algorithms to process local cells refinement and local cells grouping schemes. We illustrate these schemes with a global system that efficiently handles manual or automatic fast updates on the global reconstruction, by considering topological or geometrical constraints. We also compare the speed and the quality of our approach with two classical methods.     

A Bayesian approach to a dynamic inventory model under an unknown demand distribution

In this paper, the Bayesian approach to demand estimation is outlined for the cases of stationary as well as non-stationary demand. The optimal policy is derived for an inventory model that allows stock disposal, and is shown to be the solution of a dynamic programming backward recursion. Then, a method is given to search for the optimal order level around the myopic order level. Finally, a numerical study is performed to make a profit comparison between the Bayesian and non-Bayesian approaches, when the demand follows a stationary lognormal distribution. A profit comparison is also made between the stationary and non-stationary Bayesian approaches to observe whether the Bayesian approach incorporates non-stationarity in the demand. And, it is observed whether stock disposal reduces the losses due to ignoring non-stationarity in the demand.Scope and purposeIn the context of inventory models, one of the crucial factors to determine an optimal inventory policy, is the accurate forecasting or estimation of the demand for items in the inventory. The assumption of a constant demand is seriously questioned in recent times, since in reality the demand is generally uncertain and may even vary with time. For instance, the demand for new products, spare parts, or style goods, is likely to fluctuate widely, the average demand is quite likely to be low, and may exhibit a trend. In such situations, the Bayesian approach is a very useful tool for demand estimation, which is applicable even when past observations are scarce. In this paper, we use this approach to estimate the demand for an item, and obtain the expressions for finding the optimal inventory policies. We give a simpler method to find the optimal inventory policy, since the procedure to obtain the optimal inventory policy in the Bayesian framework, is quite tedious especially for long planning horizons, and in cases where the future demand becomes unpredictable. To widen the application of the method, we have given a general procedure which is not restricted to any particular probability distribution for the demand. We compare the Bayesian approach with the corresponding non-Bayesian approach, in terms of the optimum expected profits, when the demand follows a lognormal distribution. We also investigate how well the Bayesian approach incorporates non-stationarity in the demand.     

A BDI approach to infer student’s emotions in an intelligent learning environment

In this article we describe the use of mental states approach, more specifically the belief-desire-intention (BDI) model, to implement the process of affective diagnosis in an educational environment. We use the psychological OCC model, which is based on the cognitive theory of emotions and is possible to be implemented computationally, in order to infer the learner’s emotions from his actions in the system interface. In our work we profit from the reasoning capacity of the BDI model in order to infer the student’s appraisal (a cognitive evaluation of a person that elicits an emotion), which allows us to deduce student’s emotions. The system reasons about an emotion-generating situation and tries to infer the user’s emotion by using the OCC model. Besides, the BDI model is very adequate to infer and also model students affective states since the emotions have a dynamic nature.     

Determining critical power equipment inventory using extreme value approach and an auxiliary Poisson model

This paper is devoted to the establishment of reasonable levels of critical power equipment inventory using available historical data and strict legal requirements for reliable electrical power supply. Several models, including an Extreme Value Theory and a Homogeneous Poisson model, are used with results compared to provide a probabilistic formulation of suggested inventory. Several procedures are provided to establish proper inventory levels of critical power equipment. Simplifications are suggested for automating the calculation process for large samples. The presented approaches have a wide scope of industrial applicability, and can be applied to different types of critical equipment. Mathematical background is provided to assist practitioners that do not have extensive knowledge of probabilistic tools such as Extreme Value Theory or Homogeneous Poisson Process models.     

Using exact Jacobians in an implicit Newton–Krylov method

In an implicit Newton–Krylov method for inviscid, compressible fluid flow, the derivation of the analytic flux Jacobian can become quite complicated depending on the complexity of the numerical flux calculation. Practically, the derivation of the exact Jacobian by hand is an unrealistic option because of the enormous man-hour investment needed. In this work, automatic differentiation is used to evaluate the exact Jacobian of upwind schemes implemented in the flow solver QUADFLOW. We compare the use of exact Jacobians and Jacobians numerically approximated by first-order forward differences. For a two-dimensional airfoil under three different flight conditions (quasi-incompressible flow, compressible subsonic flow, and transonic flow), we show that the robustness and performance of the present finite volume scheme is significantly improved by using exact Jacobians.     

Development of implicit methods in CFD NASA Ames Research Center 1970s–1980s

The focus here is on the early development (mid 1970s–1980s) at NASA Ames Research Center of implicit methods in Computational Fluid Dynamics (CFD). A class of implicit finite difference schemes of the Beam and Warming approximate factorization type will be addressed. The emphasis will be on the Euler equations. A review of material pertinent to the solution of the Euler equations within the framework of implicit methods will be presented. The eigensystem of the equations will be used extensively in developing a framework for various methods applied to the Euler equations. The development and analysis of various aspects of this class of schemes will be given along with the motivations behind many of the choices. Various acceleration and efficiency modifications such as matrix reduction, diagonalization and flux split schemes will be presented.     

Clustering algorithms for the design of a cellular manufacturing system—an analysis of their performance

The heuristic clustering methods based on similarity coefficient are considered to be very efficient for providing modularity and flexibility in a cellular manufacturing systems (CMS's). Various algorithms have been implemented in these heuristic methods. However, these algorithms suffer from string effect which is also known as “chaining”. Sedveral studies have reported this problem, yet not much research has been conducted to investigate its impact on actual clustering process.

This paper presents results from an analytical study performed to determine the severity of chaining problem and other characteristics associated with the clustering process of four selected algorithms. The four algorithms are Single linkage clustering (SLINK), Average linkage clustering (ALINK), Weighted average linkage clustering (WLINK), and Complete linkage clustering (CLINK). A sample of fifty problems with randomly generated data sets was used to determine feasible solutions consisting of machine cells and corresponding part families from each of the four algorithms. A quantitative measure is proposed for evaluating the performance of different algorithms. The study concludes that the chaining effect for CLINK, WLINK, ALINK and SLINK progresively worsens from CLINK to SLINK in the same order. The study also provides important guidelines to designers of a CMS in selecting the most efficient algorithm for a given problem data. Several important statistical results are also presented.     

求解约束优化问题的粒子进化变异遗传算法

设计一种求解约束优化问题的粒子进化变异遗传算法(IGA_PSE).首先,分析候选解约束条件离差统计信息与约束违反函数之间的关系及其性质,基于约束条件离差统计信息提出一种改进约束处理方法;其次,基于粒子进化策略提出3种新变异算子;然后,讨论该算法早熟收敛的3种情况,并提出相应的种群多样化维持策略;最后,通过数值实验表明所提出的算法能够有效求解约束优化问题.     

NASA Langley’s approach to the Sandia’s structural dynamics challenge problem

The objective of this challenge is to develop a data-based probabilistic model of uncertainty to predict the acceleration response of subsystems (payloads) by themselves and while coupled to a primary (target) system. Although deterministic analyses of this type are routinely performed and representative of issues faced in real-world system design and integration, there are still several key technical challenges that must be addressed when analyzing the uncertainties of interconnected systems. For example, one key technical challenge is related to the fact that there is limited data on the target configurations. Also, while multiple data sets from experiments conducted at the subsystem level are provided, samples sizes are not sufficient to compute high confidence statistics. Moreover, in this challenge problem, additional constraints, in the form of ground rules, have been added. One such constraint is that mathematical models of the subsystem are limited to linear approximations of the nonlinear physics of the problem at hand. Also, participants are constrained to use these subsystem models and the multiple data sets to make predictions about the target system response under completely different forcing functions.Initially, our approach involved the screening of several different methods to arrive at the three presented herein. The first one is based on a transformation of the structural dynamic data in the modal domain to an orthogonal space where the mean and covariance of the data are matched. The other two approaches worked solutions in physical space where the uncertain parameter set is made of masses, stiffnessess, and damping coefficients; one matches the confidence intervals of low order moments of the statistics via optimization while the second one uses a Kernel density estimation approach. The paper will touch on the approaches, lessons learned, validation metrics and their comparison, data quantity restriction, and assumptions/limitations of each approach.     

Optimal lot sizing in an unreliable two-stage serial production–inventory system

This paper deals with a two‐stage lot sizing problem in an unreliable production environment in which the machine at the first stage (stage 1) is failure‐prone while the machine at the final stage (stage 2) is failure‐free. The process goods are obtained in batches by manufacturing and are transferred continuously from stage 1 to stage 2 where the finished goods are produced and then shipped out to customers. If the machine at stage 1 breaks down then the production of the interrupted lot is not resumed. Instead, a new production cycle is initiated after machine repair. The model is formulated assuming that the production rate of the machine at stage 1 is greater than that at stage 2 and the time to machine failure and repair time are arbitrarily distributed. Specific formulation of the model under exponential failure and exponential repair time distributions is derived and a procedure for finding the optimal production policy is presented. The dependence of the optimal production policy on the model parameters is also examined with numerical examples.     

An integral system‐ergonomic approach for IT‐based process management in complex work environments by example of manufacturing and health care

Recent trends in manufacturing and health care move these two work systems closer together from a system ergonomics point of view. Individual treatment of products, especially patients, by specialists in a distributed environment demand information technology (IT)‐based support suitable for complex systems. IT‐based support of processes in complex systems is difficult due to the lack of standard processes. IT support also means to rethink processes to use efficiency potentials. Close cooperation of users and software developers is needed to increase the ergonomic quality of the system. Therefore, suitable tools are needed: UML is available as the standard industry modeling language, Zope/Plone as the quasi‐standard for content management systems, SimPy as an object‐oriented simulation tool for event‐triggered processes, and ACT‐R as a powerful cognitive architecture for simulation of human information processes. The integration of these tools enables system‐ergonomic support of processes in the complex work system as well as of the development and deployment process. It is the base of an integral system‐ergonomic approach for IT‐based process management. Knowledge gained during process analysis either enters models or leads to the extension and adaptation of the tool chain. The models serve as basis for discussion among system ergonomists, programmers, and specialists from the work system. Further, they are understood by simulation and process support tools. Transcoding efforts between humans with different professional backgrounds and machines are reduced, and the flexibility demanded by complex systems is met. © 2008 Wiley Periodicals, Inc.     

Supply chain coordination with an option contract under vendor‐managed inventory

In this paper, we propose an option contract for a two‐echelon supply chain operating under vendor‐managed inventory (VMI). We find that the supply chain coordination and Pareto improvement can be achieved synchronously. As a comparison, a subsidy contract is introduced to coordinate the supply chain. The paper further considers the influence of a replenishment tactic on the performance of the supply chain. After introducing a replenishment tactic, the supply chain can replenish the products to satisfy unmet demand during the selling season. We further use an option contract to coordinate the supply chain, and a Pareto improvement can be gained. All our findings are numerically illustrated.