Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.     

The C-loss function for pattern classification

This paper presents a new loss function for neural network classification, inspired by the recently proposed similarity measure called Correntropy. We show that this function essentially behaves like the conventional square loss for samples that are well within the decision boundary and have small errors, and L₀ or counting norm for samples that are outliers or are difficult to classify. Depending on the value of the kernel size parameter, the proposed loss function moves smoothly from convex to non-convex and becomes a close approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function in the neighborhood of the ideal 0–1 loss function to train a neural network is immune to overfitting, more robust to outliers, and has consistent and better generalization performance as compared to other commonly used loss functions, even after prolonged training. The results also show that it is a close competitor to the SVM. Since the proposed method is compatible with simple gradient based online learning, it is a practical way of improving the performance of neural network classifiers.     

Design and evaluation of a high throughput QoS-aware and congestion-aware router architecture for Network-on-Chip

This paper proposes a novel QoS-aware and congestion-aware Network-on-Chip architecture that not only enables quality-oriented network transmission and maintains a feasible implementation cost but also well balance traffic load inside the network to enhance overall throughput. By differentiating application traffic into different service classes, bandwidth allocation is managed accordingly to fulfill QoS requirements. Incorporating with congestion control scheme which consists of dynamic arbitration and adaptive routing path selection, high priority traffic is directed to less congested areas and is given preference to available resources. Simulation results show that average latency of high priority and overall traffic is improved dramatically for various traffic patterns. Cost evaluation results also show that the proposed router architecture requires negligible cost overhead but provides better performance for both advanced mesh NoC platforms.     

Unified reliability estimation and management of NoC based chip multiprocessors

We present a new architecture level unified reliability evaluation methodology for chip multiprocessors (CMPs). The proposed reliability estimation (REST) is based on a Monte Carlo algorithm. What distinguishes REST from the previous work is that both the computational and communication components are considered in a unified manner to compute the reliability of the CMP. We utilize REST tool to develop a new dynamic reliability management (DRM) scheme to address time-dependent dielectric breakdown and negative-bias temperature instability aging mechanisms in network-on-chip (NoC) based CMPs. Designed as a control loop, the proposed DRM scheme uses an effective neural network based reliability estimation module. The neural-network predictor is trained using the REST tool. We investigate how system’s lifetime changes when the NoC as the communication unit of the CMP is considered or not during the reliability evaluation process and find that differences can be as high as 60%. Full-system based simulations using a customized GEM5 simulator show that reliability can be improved by up to 52% using the proposed DRM scheme in a best-effort scenario with 2–9% performance penalty (using a user set target lifetime of 7 years) over the case when no DRM is employed.     

An analysis of transport phenomena for multi-solid particle systems at higher reynolds numbers by a standard karman—pohlhausen method—I Momentum transfer

Using the concentric spheres free surface model and a standard 4th polynomial Karman—Pohlansen method, the dimensionless tangential stress distribution, and the dimensionless pressure distribution around a solid sphere in a swarm, and the viscous, form and total drag coefficient for multi-solid particle systems were computed numerically at higher Reynolds numbers. The void function obtained from the drag coefficient for multiparticle system was comparative to the experimental data. The friction factor for multi-particle systems so computed agreed with the experimental data of packed and distended beds.     

What you get is what you see: revisiting the evaluator effect in usability tests

Usability evaluation is essential to user-centred design; yet, evaluators who analyse the same usability test sessions have been found to identify substantially different sets of usability problems. We revisit this evaluator effect by having 19 experienced usability professionals analyse video-recorded test sessions with five users. Nine participants analysed moderated sessions; 10 participants analysed unmoderated sessions. For the moderated sessions, participants reported an average of 33% of the problems reported by all nine of these participants and 50% of the subset of problems reported as critical or serious by at least one participant. For the unmoderated sessions, the percentages were 32% and 40%. Thus, the evaluator effect was similar for moderated and unmoderated sessions, and it was substantial for the full set of problems and still present for the most severe problems. In addition, participants disagreed in their severity ratings. As much as 24% (moderated) and 30% (unmoderated) of the problems reported by multiple participants were rated as critical by one participant and minor by another. The majority of the participants perceived an evaluator effect when merging their individual findings into group evaluations. We discuss reasons for the evaluator effect and recommend ways of managing it.     

Social virtual world continuance among teens: uncovering the moderating role of perceived aggregate network exposure

Engagement in virtual worlds has become pervasive, particularly among the young. At the same time, the number of virtual environments has increased rapidly. Due to intensifying competition, promoting sustained usage, i.e. continuance, has become a top priority for virtual world operators. Prior research has shown that network externalities play a key role in the adoption of communication technologies. However, a small amount of research has examined the role of network externalities in continued IT usage in general or with respect to the virtual world participation in particular. To fill in this gap, we examine how perceived network externalities affect the continuance of social virtual worlds. To this end, we introduce the concept of perceived aggregate network exposure (PANE). We extend the original information systems (IS) continuance model with perceived enjoyment and position PANE as a moderator. We test the model with data collected from 2134 Finnish Habbo Hotel users and employ structural equation modelling in the analysis. The results demonstrate that PANE moderates the influence of motivational factors on continued use intention and satisfaction.     

Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals

We present a new approach to microfacet‐based BSDF importance sampling. Previously proposed sampling schemes for popular analytic BSDFs typically begin by choosing a microfacet normal at random in a way that is independent of direction of incident light. To sample the full BSDF using these normals requires arbitrarily large sample weights leading to possible fireflies. Additionally, at grazing angles nearly half of the sampled normals face away from the incident ray and must be rejected, making the sampling scheme inefficient. Instead, we show how to use the distribution of visible normals directly to generate samples, where normals are weighted by their projection factor toward the incident direction. In this way, no backfacing normals are sampled and the sample weights contain only the shadowing factor of outgoing rays (and additionally a Fresnel term for conductors). Arbitrarily large sample weights are avoided and variance is reduced. Since the BSDF depends on the microsurface model, we describe our sampling algorithm for two models: the V‐cavity and the Smith models. We demonstrate results for both isotropic and anisotropic rough conductors and dielectrics with Beckmann and GGX distributions.     

Effect of reactive diluents on properties of unsaturated polyester/montmorillonite nanocomposites

Unsaturated polyester (UP)/montmorillonite (MMT) nanocomposite was prepared by using hydroxypropylacrylate (HPA) as a reactive diluent instead of conventional styrene monomer and the effect of polarity of reactive diluent on properties of nanocomposite was investigated. X‐ray and mechanical test data indicated that mixing for an extended period of time is essential to enhance the physical properties of nanocomposites in the UP/Cloisite 6A system. This was attributed to the high polarity of HPA that may disturb the preintercalation of UP resin into the galleries of MMT. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 238–242, 2004     

3D Motion Completion in Crowded Scenes

Crowded motions refer to multiple objects moving around and interacting such as crowds, pedestrians and etc. We capture crowded scenes using a depth scanner at video frame rates. Thus, our input is a set of depth frames which sample the scene over time. Processing such data is challenging as it is highly unorganized, with large spatio‐temporal holes due to many occlusions. As no correspondence is given, locally tracking 3D points across frames is hard due to noise and missing regions. Furthermore global segmentation and motion completion in presence of large occlusions is ambiguous and hard to predict. Our algorithm utilizes Gestalt principles of common fate and good continuity to compute motion tracking and completion respectively. Our technique does not assume any pre‐given markers or motion template priors. Our key‐idea is to reduce the motion completion problem to a 1D curve fitting and matching problem which can be solved efficiently using a global optimization scheme. We demonstrate our segmentation and completion method on a variety of synthetic and real world crowded scanned scenes.