A methodology for quadrilateral finite element mesh coarsening

High fidelity finite element modeling of continuum mechanics problems often requires using all quadrilateral or all hexahedral meshes. The efficiency of such models is often dependent upon the ability to adapt a mesh to the physics of the phenomena. Adapting a mesh requires the ability to both refine and/or coarsen the mesh. The algorithms available to refine and coarsen triangular and tetrahedral meshes are very robust and efficient. However, the ability to locally and conformally refine or coarsen all quadrilateral and all hexahedral meshes presents many difficulties. Some research has been done on localized conformal refinement of quadrilateral and hexahedral meshes. However, little work has been done on localized conformal coarsening of quadrilateral and hexahedral meshes. A general method which provides both localized conformal coarsening and refinement for quadrilateral meshes is presented in this paper. This method is based on restructuring the mesh with simplex manipulations to the dual of the mesh. In addition, this method appears to be extensible to hexahedral meshes in three dimensions. Sandia National Laboratories is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE-AC04-94AL85000.     

Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning

A Markov chain Monte Carlo method has previously been introduced to estimate weighted sums in multiplicative weight update algorithms when the number of inputs is exponential. However, the original algorithm still required extensive simulation of the Markov chain in order to get accurate estimates of the weighted sums. We propose an optimized version of the original algorithm that produces exactly the same classifications while often using fewer Markov chain simulations. We also apply three other sampling techniques and empirically compare them with the original Metropolis sampler to determine how effective each is in drawing good samples in the least amount of time, in terms of accuracy of weighted sum estimates and in terms of Winnow’s prediction accuracy. We found that two other samplers (Gibbs and Metropolized Gibbs) were slightly better than Metropolis in their estimates of the weighted sums. For prediction errors, there is little difference between any pair of MCMC techniques we tested. Also, on the data sets we tested, we discovered that all approximations of Winnow have no disadvantage when compared to brute force Winnow (where weighted sums are exactly computed), so generalization accuracy is not compromised by our approximation. This is true even when very small sample sizes and mixing times are used. An early version of this paper appeared as Tao and Scott (2003).     

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student’s level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.     

A scalable data structure for real-time estimation of resource availability in build-to-order environments

This paper defines a highly scalable interval index structure called the Temporal Bin tree (TB-tree) that can be embedded in any resource planning application whose algorithms require efficiently estimating either the time that a resource will be available to process a specific task of known length or the net availability of a resource during a specified period of time. It is specifically engineered to meet the real-time response and space efficiency requirements of large-scale resource planning applications that are required for mass customization. Basically, the TB-tree is a binary tree structure that represents availability of a resource across a planning horizon. Representing intervals of availability hierarchically using a tree structure increases the efficiency of search for resource availability when the discretization of time is fine-grained or the planning horizon is long. The tree forms a backbone structure that does not require disruptive rebalancing during update operations, which would mitigate the ability of the tree to respond to queries in real time. Its specific implementation allows for random access at any level of the tree to further improve scalability. An application of planning to real-time promising of order due dates for custom built products provides the context for empirical evaluation. Results of analytical evaluations and simulation experiments clearly demonstrate the scalability of the TB-tree relative to existing index structures in terms of both time and space.     

Investigating teamwork and taskwork in single- and multi-display groupware systems

Multi-display groupware (MDG) systems, which typically comprise both public and personal displays, promise to enhance collaboration, yet little is understood about how they differ in use from single-display groupware (SDG) systems. While research has established the technical feasibility of MDG systems, evaluations have not addressed the question of how users’ behave in such environments, how their interface design can impact group behavior, or what advantages they offer for collaboration. This paper presents a user study that investigates the impact of display configuration and software interface design on taskwork and teamwork. Groups of three completed a collaborative optimization task in single- and multi-display environments, under different task interface constraints. Our results suggest that MDG configurations offer advantages for performing individual task duties, whereas SDG conditions offer advantages for coordinating access to shared resources. The results also reveal the importance of ergonomic design considerations when designing co-located groupware systems.     

Case-Based Learning: Predictive Features in Indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This predictive features hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.We present a specific case-based model of plan execution, RUNNER, along with its indices for recognizing when to select particular plans—appropriateness conditions—and how these predictive indices serve to enhance learning. We then discuss how this predictive features claim as implemented in the RUNNER model is then tested in a second set of psychological studies. The results show that learning appropriateness conditions results in greater success in recognizing when a past plan is in fact relevant in current processing, and produces more reliable recall of the related information. This form of collaboration has resulted in a unique integration of computational and empirical efforts to create a model of case-based learning.     

Some properties of mullite powders prepared by chemical vapour deposition

The sinterability of mullite (3Al₂O₃·2SiO₂) powder prepared by chemical vapour deposition was examined to improve the conditions for fabricating dense mullite ceramics. The starting powder contained not only mullite, but also a small amount of -Al₂O₃ (Al-Si spinel) and amorphous material. Although the compressed powder was fired at a temperature between 1550 and 1700 °C for 1, 3 and 5 h, the relative densities of the sintered compacts were limited to 90%: (i) due to the creation of pores/microcracks during the solid state reaction (1100–1350 °C), and (ii) due to restriction on the rearrangement of grains because the amount of liquid phase (1550–1700 °C) was insufficient. Calcination of the starting powder was effective for preparation of easily sinterable powder with homogeneous composition. When the compact formed by compressing the calcined powder at 1400 °C for 1 h was fired at 1650 °C for 3 h, the relative density was raised up to 97.2%; moreover, mullite was the only phase detected from the sintered compact. The sintered compact was composed of polyhedral grains with sizes of 1–2 m and elongated grains with long axes of 6 m.     

Joint power control and user scheduling for backbone-assisted industrial wireless networks with successive interference cancellation

In a backbone-assisted industrial wireless network (BAIWN), the technology of successive interference cancellation (SIC) based non-orthogonal multiple access (NOMA) provides potential solutions for improving the delay performance. Previous work emphasizes minimizing the transmission delay by user scheduling without considering power control. However, power control is beneficial for SIC-based NOMA to exploit the power domain and manage co-channel interference to simultaneously serve multiple user nodes with the high spectral and time resource utilization characteristics. In this paper, we consider joint power control and user scheduling to study the scheduling time minimization problem (STMP) with given traffic demands in BAIWNs. Specifically, STMP is formulated as an integer programming problem, which is NP-hard. To tackle the NP-hard problem, we propose a conflict graph-based greedy algorithm, to obtain a sub-optimal solution with low complexity. As a good feature, the decisions of power control and user scheduling can be made by the proposed algorithm only according to the channel state information and traffic demands. The experimental results show that compared with the other methods, the proposed method effectively improves the delay performance regardless of the channel states or the network scales.

     

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Linear buckling constraints are important in structural topology optimization for obtaining designs that can support the required loads without failure. During the optimization process, the critical buckling eigenmode can change; this poses a challenge to gradient‐based optimization and can require the computation of a large number of linear buckling eigenmodes. This is potentially both computationally difficult to achieve and prohibitively expensive. In this paper, we motivate the need for a large number of linear buckling modes and show how several features of the block Jacobi conjugate gradient (BJCG) eigenvalue method, including optimal shift estimates, the reuse of eigenvectors, adaptive eigenvector tolerances and multiple shifts, can be used to efficiently and robustly compute a large number of buckling eigenmodes. This paper also introduces linear buckling constraints for level‐set topology optimization. In our approach, the velocity function is defined as a weighted sum of the shape sensitivities for the objective and constraint functions. The weights are found by solving an optimization sub‐problem to reduce the mass while maintaining feasibility of the buckling constraints. The effectiveness of this approach in combination with the BJCG method is demonstrated using a 3D optimization problem. Copyright © 2016 John Wiley & Sons, Ltd.