Particle Size Effects of Silicon Carbide on Wear Behavior of SiC<Subscript>p</Subscript>-Reinforced Magnesium Matrix Composites

This article investigated the particle size effect of micro-sized SiC on the tribological behavior of SiC_p-reinforced AZ91D Mg-based metal–matrix composites (MMCs). The Mg MMCs were prepared by the melt-stirring technique for wear tests. The hardness and coefficient of friction of Mg MMCs increases as particle size of SiC particle in MMCs increases, except for the hardness tendency at the region between particle size of 11 and 15 μm. The SiC_p/AZ91D MMCs exhibit superior wear resistance under lower and moderate sliding condition. However, the effects of the SiC particle reinforcements on wear resistance are not as conclusive under severe sliding condition, since the matrix of MMCs were softened at elevated temperature under such a severe condition.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

Shrinkage and model selection with correlated variables via weighted fusion

In this paper, we propose the weighted fusion, a new penalized regression and variable selection method for data with correlated variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors p is larger than the number of observations n. It allows the selection of more than n variables in a motivated way. Real data and simulation examples show that weighted fusion can improve variable selection and prediction accuracy.     

Toward effective service composition for real-time SOA-based systems

Many application domains are increasingly leveraging service-oriented architecture (SOA) techniques to facilitate rapid system deployment. Many of these applications are time-critical and, hence, real-time assurance is an essential step in the service composition process. However, there are gaps in existing service composition techniques for real-time systems. First, admission control is an essential technique to assure the time bound for service execution, but most of the service composition techniques for real-time systems do not take admission control into account. A service may be selected for a workflow during the composition phase, but then during the grounding phase, the concrete service may not be able to admit the workload. Thus, the entire composition process may have to be repeated. Second, communication time is an important factor in real-time SOA, but most of the existing works do not consider how to obtain the communication latencies between services during the composition phase. It is clear that maintaining a full table of communication latencies for all pairs of services is infeasible. Obtaining communication latencies between candidate services during the composition phase can also be costly, since many candidate services may not be used for grounding. Thus, some mechanism is needed for estimating the communication latency for composite services. In this paper, we propose a three-phase composition approach to address the above issues. In this approach, we first use a highly efficient but moderately accurate algorithm to eliminate most of the candidate compositions based on estimated communication latencies and assured service response latency. Then, a more accurate timing prediction is performed on a small number of selected compositions in the second phase based on confirmed admission and actual communication latency. In the third phase, specific concrete services are selected for grounding, and admissions are actually performed. The approach is scalable and can effectively achieve service composition for satisfying real-time requirements. Experimental studies show that the three-phase approach does improve the effectiveness and time for service composition in SOA real-time systems. In order to support the new composition approach, it is necessary to effectively specify the needed information. In this paper, we also present the specification model for timing-related information and the extension of OWL-S to support this specification model.     

Exploiting knowledge ontology and software agents for PPI network analysis

One major goal of functional genomics has been to identify and analyze molecular interactions in a cellular context to better understand the underlying design principles and mechanisms. To investigate into a PPI network from both topological and functional points of view, this work proposes a methodology that exploits ontology-based biological knowledge for network analysis. To speed up the procedure, an agent-based framework is also presented for supporting distributed computing. The preliminary results show that through the knowledge obtained from gene ontology, our work in analyzing building blocks of PPI networks can give a higher resolution than that of previous ones. Also our agent-based framework can successfully speed up the task of network analysis in an adaptive manner.     

A new buyer-seller watermarking protocol without multiple watermarks insertion

Watermarking protocols are designed for tracing illegal distributors when unauthorized copies are found. So far, most of the proposed schemes set up two or more watermarks embedded to a copy by the seller before it was sold. The main potential concerns of multiple watermarking are the image quality would be damaged and any earlier embedded watermarks would be destroyed as well. Thanks to visual cryptography which encodes the secret image into two shares, and recovers the secret by collecting these two shares. Therefore, a new buyer-seller watermarking protocol is proposed in this paper by applying the technique of visual cryptography to Lei et al.’s scheme so as to free from the disadvantages of multiple-watermarking insertion.

     

Electronic structures of quasi-one-dimensional ferrimagnetic insulator Ca3Co2O6

The electronic structures of quasi-one-dimensional ferrimagnetic Ca₃Co₂O₆ are investigated using the generalized gradient approximation (GGA) as well as the GGA plus on-site Coulomb interaction (GGA+U) scheme. GGA+U calculations reveal that the interchain ferrimagnetic Ca₃Co₂O₆ is a Mott–Hubbard insulator rather than a metal given from GGA. In addition, we found an on-site U induced 3z²−r² orbital ordering on Co_pri sublattice which drives the intrachain ferromagnetic coupling along c-axis. Our findings suggest that strong electron-electron correlation plays an important role in Ca₃Co₂O₆.     

Surface treatment,reflectance, and age effects on electronic‐paper reading performance

Abstract— This study is intended to explore the legibility and visual fatigue of different age users under various surface treatments and reflectance of electronic paper. Through the method of character‐search task, the results indicated that compared with single types of treatment [anti‐reflection (AR) 0.8%, anti‐glare, 43% haze), the compound treatment of anti‐reflection and anti‐glare (ARC) exhibited the same legibility, and it showed superior properties to effectively reduce visual fatigue. Hence, it is suggested that electronic‐paper manufacturers should choose the compound surface treatment for better visual performance. On the other hand, the findings also validated that enhancing the reflectance of electronic paper to the same level as regular paper (about 80%) is worthy to be practically implemented. Based on the results of this study, electronic‐paper manufacturers can take useful information to fulfill ergonomic requirements on product design.     

A model-based hand gesture recognition system

This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000