Thermal properties and crystalline structure of liquid crystalline poly(ethylene terephthalate‐co‐2(3)‐chloro‐1,4‐phenylene terephthalate)

Thermal properties and crystalline structure of liquid crystalline (LC) poly(ethylene terephthalate‐co‐2(3)‐chloro‐1,4‐phenylene terephthalate) [copoly(ET/CPT)] were investigated using differential scanning calorimetry (DSC), thermogravimetry (TGA), limiting oxygen index (LOI) measurement, electron dispersive X‐ray analysis (EDX), X‐ray diffractometry, and infrared spectrometry (IR). The thermal transition temperatures of copoly(ET/CPT) were changed with the composition. Copoly(ET/CPT) showed two thermal decomposition steps and the residues at 700°C and LOI values of copoly(ET/CPT) were almost proportional to its chlorine content. The activation energy of thermal decomposition of LC units was very low compared to that of poly(ethylene terephthalate)(PET) units. Crystal structure of copoly(ET/CPT) (20/80) was of triclinic system with the lattice constants of a = 9.98 A?, b = 8.78 A?, c = 12.93 A?, α = 97.4°, β = 96.1°, and γ = 90.8°, which is very close to that of poly(chloro‐p‐phenylene terephthlate) (PCPT) with the lattice constants of a = 9.51 A?, b = 8.61 A?, c = 12.73 A?, α = 96.8°, β = 95.4°, and γ = 90.8°. When copoly(ET/CPT)(50/50) was annealed at 220°C in vacuum, crystallization induced sequential reordering (CISR) was not observed but the heat of fusion was slightly increased due to the increase of the trans isomer content in PET units. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 1286–1294, 2002; DOI 10.1002/app.10451     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

Finding a maximum matching in a permutation graph

We present anO(n log logn) time algorithm for finding a maximum matching in a permutation graph withn vertices, assuming that the input graph is represented by a permutation.     

Selective Oxidation of Methanol to Formaldehyde Using Modified Iron-Molybdate Catalysts

Methanol selective oxidation to formaldehyde over a modified Fe-Mo catalyst with two different stoichiometric (Mo/Fe atomic ratio = 1.5 and 3.0) was studied experimentally in a fixed bed reactor over a wide range of reaction conditions. The physicochemical characterization of the prepared catalysts provides evidence that Fe₂(MoO₄)₃ is in fact the active phase of the catalyst. The experimental results of conversion of methanol and selectivity towards formaldehyde for various residence times were studied. The results showed that as the residence time increases the yield of formaldehyde decreases. Selectivity of formaldehyde decreases with increase in residence time. This result is attributable to subsequent oxidation of formaldehyde to carbon monoxide due to longer residence time.     

Optimal reaction conditions for the minimization of energy consumption and by‐product formation in a poly(ethylene terephthalate) process

We determined the optimal reaction conditions to minimize the energy cost and the quantities of by‐products for a poly(ethylene terephthalate) process by using the iterative dynamic programming (IDP) algorithm. Here, we employed a sequence of three reactor models: the semibatch transesterification reactor model, the semibatch prepolymerization reactor model, and the rotating‐disc‐type polycondensation reactor model. We selectively chose or developed the reactor models by incorporating experimentally verified kinetic models reported in the literature. We established the model for the entire reactor system by connecting the three reactor models in series and by resolving some joint problems arising when different types of reactor models were interconnected. On the basis of the simulation results of the reactor system, we scrutinized the cause and effect between the reaction conditions and the final quality of the polymer product. Here, we set up the optimization strategy by using IDP on the basis of the integrated reactor model, and the process variables with significant influence on the properties of polymer were selected as control variables with the help of a simulation study. With this method, we could refine the reaction conditions at the end of each iteration step by contracting the spectra of control regions, and the iteration process finally stopped when the profile of the optimal trajectory converged. We also took the constraints on the control variables into account to guarantee polymer quality and to suppress side reactions. Constituting six different strategies by setting weighting vectors differently, we examined the differences in optimal trajectories, the trend of optimality, and the quality of the final polymer product. For each of the strategies, we conducted the optimization to examine whether the number‐average degree of polymerization approached the desired value. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 993–1008, 2002     

A parallel image encryption method based on compressive sensing

Recently, compressive sensing-based encryption methods which combine sampling, compression and encryption together have been proposed. However, since the quantized measurement data obtained from linear dimension reduction projection directly serve as the encrypted image, the existing compressive sensing-based encryption methods fail to resist against the chosen-plaintext attack. To enhance the security, a block cipher structure consisting of scrambling, mixing, S-box and chaotic lattice XOR is designed to further encrypt the quantized measurement data. In particular, the proposed method works efficiently in the parallel computing environment. Moreover, a communication unit exchanges data among the multiple processors without collision. This collision-free property is equivalent to optimal diffusion. The experimental results demonstrate that the proposed encryption method not only achieves the remarkable confusion, diffusion and sensitivity but also outperforms the existing parallel image encryption methods with respect to the compressibility and the encryption speed.     

Integration Concepts for the Fabrication of LTCC Structures

At the Keck Smart Materials Integration Laboratory at Penn State University, low-temperature co-fired ceramic (LTCC) material systems have been used to fabricate a number of devices for a variety of applications. This article presents an overview of the integration of the concepts and materials that we have used to achieve miniaturization and unique device function. Examples of microwave filters, metamaterial antennas, and a dielectrophoretic cell sorter will be presented, with emphasis on device modeling and design, prototype construction methods, and test results.     

Pattern Classification With Class Probability Output Network

The output of a classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily represent the posterior probability for the soft decision of classification. In this context, it is desirable that the output of a classifier be calibrated in such a way to give the meaning of the posterior probability of class membership. This paper presents a new method of postprocessing for the probabilistic scaling of classifier's output. For this purpose, the output of a classifier is analyzed and the distribution of the output is described by the beta distribution parameters. For more accurate approximation of class output distribution, the beta distribution parameters as well as the kernel parameters describing the discriminant function are adjusted in such a way to improve the uniformity of beta cumulative distribution function (CDF) values for the given class output samples. As a result, the classifier with the proposed scaling method referred to as the class probability output network (CPON) can provide accurate posterior probabilities for the soft decision of classification. To show the effectiveness of the proposed method, the simulation for pattern classification using the support vector machine (SVM) classifiers is performed for the University of California at Irvine (UCI) data sets. The simulation results using the SVM classifiers with the proposed CPON demonstrated a statistically meaningful performance improvement over the SVM and SVM-related classifiers, and also other probabilistic scaling methods.     

ΔK ol level effects on fatigue crack propagation

In order to determine the effects of K _ol level on fatigue life, a single peak load was applied at distinct K levels of 7.8×10.3 and 9.8×10³ p.s.i. in^1/2. Here the K _ol level was defined to be a K level at which overload was applied. Three different overload ratios of 1.5, 2.0, and 2.5 were used to determine the overload ratio effect on the recovery factor. The result showed that the recovery factor, Z, was linearly related to K as Z = qK+Z _o, where q was a function of overload ratio. The value of q decreased as the overload ratio increased in a given K _ol level and seemed to be an important factor as well as retardation cycles in determining the fatigue life. For the same overload ratio, specimens that underwent overload at a smaller K _ol level showed more improved fatigue life.Nomenclature a Crack length - a ^* Overload affected zone size - B Specimen thickness - (da/dN)_ca Crack growth rate due to constant amplitude fatigue load - (da/dN)_ol Crack growth rate after overload is applied - E Young's modulus - K Stress intensity factor - K _min Minimum stress intensity factor - K _max Maximum stress intensity factor - K _ol K level at which overload is applied - N Number of cycles - N _D Number of delayed cycles - N _f Number of cycles needed for a specimen to be completely fractured - r _p Assumed plastic zone size - S Load - _ys Yield stress - W Width - Z Recovery factor     

Efficient skycube computation using point and domain-based filtering

Skyline queries have been increasingly used in multi-criteria decision making and data mining applications. They retrieve a set of interesting points from a potentially large set of data points. A point is said to be interesting if it is not dominated by any other point. Skyline cube (skycube) consists of skylines of all possible non-empty subsets of a given set of dimensions. In this paper, we propose two algorithms for computing skycube using bitmaps that are derivable from indexes. The Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. The Point-based algorithm processes one point at a time to check for skylines in all subspaces. The Domain-based skycube algorithm views points as value combinations and probes entire search space for potential skyline points. It significantly reduces bitmap access for low cardinality dimensions. Our experimental study shows that the two algorithms strictly dominate, or at least comparable to, the current skycube algorithm in most of the cases, suggesting that such an approach could be a useful addition to the set of skyline query processing techniques.