Effects of poly(methyl methacrylate)‐based low‐profile additives on the properties of cured unsaturated polyester resins. II. Volume shrinkage characteristics and internal pigmentability

The effects of three series of self‐synthesized poly(methyl methacrylate) (PMMA)‐based low‐profile additives (LPAs), including PMMA, poly(methyl methacrylate‐co‐butyl acrylate), and poly(methyl methacrylate‐co‐butyl acrylate‐co‐maleic anhydride) with different chemical structures and MWs on the volume shrinkage characteristics and internal pigmentability for low‐shrink unsaturated polyester (UP) resins during curing were investigated by an integrated approach of static phase characteristics of the ternary styrene (ST)/UP/LPA system, reaction kinetics, cured‐sample morphology, microvoid formation, and property measurements. The relative volume fraction of microvoids generated during the cure was controlled by the stiffness of the UP resin used, the compatibility of the uncured ST/UP/LPA systems, and the glass‐transition temperature of the LPAs used. On the basis of the Takayanagi mechanical model, the LPA mechanism on volume shrinkage control, which accounted for phase separation and microvoid formation, and factors leading to both a good volume shrinkage control and acceptable internal pigmentability for the molded parts are discussed. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3388–3397, 2004     

Symbolic target detection in SAR imagery via rotationally invariant-weighted feature extraction

This article introduces the application of a physics-based symbolic image partitioning method to detect targets in synthetic aperture radar (SAR) imagery. ‘Targets’ in this case refer to vehicular objects which produce a distinct radar return pattern, and have spatial characteristics that are known a priori. The proposed Rotationally Invariant Symbolic Histogram (RISH) detection method co-analyses both target and speckle statistics, and significantly reduces computational requirements by partitioning the data into a discrete number of state representations. RISH requires only one pass for robust detection, unlike other SAR detection methods which rely on difference metrics calculated using multiple passes. To improve performance in high-resolution data, RISH uses a weighted feature extraction algorithm to avoid the common requirement of processing each pixel of the image equally. The weighted structure extracts geometrically undefined and rotationally invariant target features. This article details the analysis of 24 experimentally obtained very high-frequency (VHF)-band SAR magnitude images using this novel approach to SAR target detection. In localizing small (~8.4 m²) foliage-concealed targets, without the aid of pre-processing, this method results in high performance characteristics (90% true positive) with a low Type-II error rate of 6.4 false alarms per 1 × 10⁶ m². With the addition of change detection, RISH lowers the error rate by 85%.     

Computer-aided design of microstrip filters by iterated analysis

An iterative method for the design of microstrip low-pass elliptic function filters is described. The method, which is a direct extension of the work of H.J. Orchard (IEEE Trans. Circuit Syst., vol.CAS-32, no.11, p.1089-96, 1985), determines the microstrip line parameters that produce the same locations of the frequencies of transmission zeros and reflection zeros of an equivalent lumped-element prototype. Effects of the discontinuities at the junction are easily accounted for in the iteration. A design example is included, and an experimental seventh-order filter designed and constructed using the procedure gives measured results that agree closely with theory     

Monitoring and classification of dimensional faults for automotive body assembly

Sudden process changes occurring during automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could result in a decreased production rate and an increase in the cost if sudden process changes are so serious that the production line has to be stopped for investigation. Thus, sudden process changes should be detected and eliminated as soon as possible to prevent defective products from being produced and to reduce the cost of repairs/reworks. A monitoring algorithm is developed to detect, classify, and group process changes by analyzing the dimensional data of car bodies. The results of this monitoring algorithm can help diagnose the root causes of variation according to the locations of measurement points, body structure, assembly sequence, and tooling layout. Measurement data obtained from an optical coordinate measuring machine (OCMM) are used to demonstrate the monitoring technique.     

A fuzzy search algorithm for the estimation of motion vectors

The block motion estimation/compensation techniques are efficient methods to reduce the required bitrate by eliminating temporal redundancy of the image sequence. In this paper, an efficient search algorithm for block motion estimation is presented. By using the inter-block and inter-frame fuzzy prediction, the algorithm can determine the motion vectors of image blocks quickly and correctly. According to the experimental results, our algorithm performs better than other search algorithms, such as TSS, NTSS, FSS, BBGDS, SES, PSA, GPS, and FPS in terms of seven different measures     

Comparing K-Means and Mean Shift Algorithms Performance Using Mahout in a Private Cloud Environment

Cloud computing has providing the possibility of services like store data, run applications and scalability of resources. The ability to measure computing resources according to request allows a great quantity of data to be stored in Data Kernels. Hadoop is a promising solution to solve problems with big sets of data. Mahout is a project developed with Hadoop that, by default, implements several grouping and classification algorithms, such as K-Means and Mean Shift, which are grouping algorithms successfully used during the last years in small databases. This paper presents a performance analysis of K-Means and Mean Shift in a standard implementation of Mahout in MapReduce distributed paradigm.     

The copper sulfide coating on polyacrylonitrile with a chelating agent of ethylenediaminetetraacetic acid by an electroless deposition method and its EMI shielding effectiveness

In this study, a copper sulfide layer was instantaneously coated on the polyacrylonitrile (PAN) by an electroless plating method with the reduction agents NaHSO₃ and Na₂S₂O₃·5H₂O and a chelating agent (ethylenediaminetetraacetic acid, EDTA). A variety of concentrations of EDTA was added to obtain the anchoring effect and chelating effect in the electroless plating bath. The mechanism of the Cu_{x(x = 1, 2)}S growth and the electromagnetic interference shielding effectiveness (EMI SE) of the composite were studied. It was found that the vinyl acetate was residual in PAN substrate would be purged due to the swelling effect by EDTA solution. Then, the anchoring effect occurred due to the hydrogen bonding between the pits of PAN substrate and the chelating agent. Consequently, the copper sulfide layer deposited successfully by the electroless plating reacted upon EDTA. The swelling degree (S_d) was proposed and evaluated from the FTIR spectra. The relationship between swelling degree of the PAN composite and EDTA concentration (C) is expressed as follows: S_d = 0.13 + 0.90 × e^(?15.15C). On the other hand, the FESEM micrograph showed that the average thickness of copper sulfide increased from 76 to 383 nm when the concentration of EDTA increased from 0.00 to 0.20M. For this reason, the EMI SE of the composites increased from 10–12 dB to 20–23 dB. The GIA‐XRD and laser Raman analysis indicated that the deposited layer consisted of CuS and Cu₂S. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Detecting anomalous patterns in time-series data using sparse hierarchically parameterized transition matrices

Anomaly detection in time-series data is a relevant problem in many fields such as stochastic data analysis, quality assurance, and predictive modeling. Markov models are an effective tool for time-series data analysis. Previous approaches utilizing Markov models incorporate transition matrices (TMs) at varying dimensionalities and resolutions. Other analysis methods treat TMs as vectors for comparison using search algorithms such as the nearest neighbors comparison algorithm, or use TMs to calculate the probability of discrete subsets of time-series data. We propose an analysis method that treats the elements of a TM as random variables, parameterizing them hierarchically. This approach creates a metric for determining the “normalcy” of a TM generated from a subset of time-series data. The advantages of this novel approach are discussed in terms of computational efficiency, accuracy of anomaly detection, and robustness when analyzing sparse data. Unlike previous approaches, this algorithm is developed with the expectation of sparse TMs. Accounting for this sparseness significantly improves the detection accuracy of the proposed method. Detection rates in a variety of time-series data types range from (97 % TPR, 2.1 % FPR) to (100 % TPR, <0.1 % FPR) with very small sample sizes (20–40 samples) in data with sparse transition probability matrices.