Marking student programs using graph similarity

We present a novel approach to the automated marking of student programming assignments. Our technique quantifies the structural similarity between unmarked student submissions and marked solutions, and is the basis by which we assign marks. This is accomplished through an efficient novel graph similarity measure (AssignSim). Our experiments show good correlation of assigned marks with that of a human marker.     

Developing Oregon’s renewable energy portfolio using fuzzy goal programming model

Renewable energy continues to be a hot topic in the United States affecting security and sustainability. A model to create renewable energy portfolio is established using guidelines drawn by Oregon’s Renewable Portfolio Standard (RPS) legislation with the objective of responding to a 25% of the state electricity demand by renewable resources in 2025. The fuzzy goal programming model is adaptable to accommodate changes in energy costs and future advances in technology maturity. It can also take into consideration the preferences of policy-makers and stakeholders. This model can help to reveal the costs and benefits of complex decisions regarding renewable energy.     

Motion navigation for arc welding robots based on feature mapping in a simulation environment

The efficiency of the off-line programming for the arc welding robots depends on the complexity of the workpieces and the welding experience of the technician. When the workpieces or fixtures are redesigned or the robot workstation layout is changed, the result of prior off-line programming cannot be reused. With the thought of CAD/CAPP/CAM integration, this paper presents a feature mapping algorithm, which converts the design features of the workpieces to the machining features of the seams. Then a motion navigation method based on feature mapping in a simulation environment is proposed. This method comprises initial position guiding and seam tracking. For initial position guiding, a motion path with the least energy consumption is generated based on the weighted optimal control; then the path is revised for avoiding obstacles by setting the path tags dynamically. For seam tracking, the seam is dispersed into discrete welding points; then the welding gun moves through each point according to the welding sequence. The method is implemented in the development of an off-line programming simulation system. Industrial cases of the welding robot workcell layout for automobile door frames show the method can make the off-line programming much more effective.     

A novel AR-based robot programming and path planning methodology

This paper discusses the benefits of applying Augmented Reality (AR) to facilitate intuitive robot programming, and presents a novel methodology for planning collision-free paths for an n degree-of-freedom (DOF) manipulator in an unknown environment. The targeted applications are where the end-effector is constrained to move along a visible 3D path/curve, which position is unknown, at a particular orientation with respect to the path, such as arc welding and laser cutting. The methodology is interactive as the human is involved in obtaining the 3D data points of the desired curve to be followed through performing a number of demonstrations, defining the free space relevant to the task, and planning the orientations of the end-effector along the curve. A Piecewise Linear Parameterization (PLP) algorithm is used to parameterize the data points using an interactively generated piecewise linear approximation of the desired curve. A curve learning method based on Bayesian neural networks and reparameterization is used to learn and generate 3D parametric curves from the parameterized data points. Finally, the orientation of the end-effector along the learnt curve is planned with the aid of AR. Two case studies are presented and discussed.     

Modeling data envelopment analysis by chance method in hybrid uncertain environments

This article first presents several formulas of chance distributions for trapezoidal fuzzy random variables and their functions, then develops a new class of chance model (C-model for short) about data envelopment analysis (DEA) in fuzzy random environments, in which the inputs and outputs are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. Since the objective and constraint functions contain the chance of fuzzy random events, for general fuzzy random inputs and outputs, we suggest an approximation method to compute the chance. When the inputs and outputs are mutually independent trapezoidal fuzzy random variables, we can turn the chance constraints and the chance objective into their equivalent stochastic ones by applying the established formulas for the chance distributions. In the case when the inputs and the outputs are mutually independent trapezoidal fuzzy random vectors, the proposed C-model can be transformed to its equivalent stochastic programming one, in which the objective and the constraint functions include a number of standard normal distribution functions. To solve such an equivalent stochastic programming, we design a hybrid algorithm by integrating Monte Carlo (MC) simulation and genetic algorithm (GA), in which MC simulation is used to calculate standard normal distribution functions, and GA is used to solve the optimization problems. Finally, one numerical example is presented to demonstrate the proposed modeling idea and the efficiency in the proposed model.     

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.     

Integrating customer's preferences in the QFD planning process using a combined benchmarking and imprecise goal programming model

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.     

The generalized assortment and best cutting stock length problems

This paper introduces two new one-dimensional cutting stock models: the generalized assortment problem (GAP) and the best cutting stock length (BSL) problem. These new models provide the potential to reduce waste to values lower than the optimum of current models, under the right management circumstances. In the GAP, management has a standard length and can select one or more of any additional custom stock lengths, and management wishes to minimize cutting stock waste. This model is different from existing models that assume that the selection is from a small fixed set of stock lengths. In the BSL problem, management chooses any number of custom stock lengths, but wishes to find the fewest custom stock lengths in order to have zero waste. Results show waste reductions of 80% with just one custom stock length compared with solutions from standard cutting stock formulations, when item lengths are long relative to the stock length. The models are most effective when the item lengths are nearly as long as the stock length. Solutions from the model have been implemented for a manufacturer. The model is easily generalized to allow multiple existing stock lengths and different costs.     

网格计算中费用约束的最优时间调度算法

在网格资源处理速度和资源价格异构的网格环境下,讨论基于用户费用约束的最优时间调度问题,提出一种相应的调度算法,将该任务调度问题转化为线性规划问题,采用单纯形算法获得近似最优解,从而获得费用约束下资源的最优执行时间以及该任务的最小完成时间。仿真结果表明,该算法的性能优于其他同类算法。     

基于遗传模拟退火算法的军用仓库选址研究

战区物资供应强度大,运力有限,军用仓库选址的合理与否会直接影响到战役、战术物资供应的效率,该文运用遗传模拟退火算法研究了军用仓库选址问题,实例验证充分说明了遗传模拟退火算法在收敛速度及跳出局部极值的能力诸方面明显优于标准的遗传算法和模拟退火算法。