Unnecessary inferences in associative-commutative completion procedures

Theoretical results for identifying unnecessary inferences are discussed in the context of the use of a completion-procedure-based approach toward automated reasoning. The notion of a general superposition is introduced and it is proved that in a completion procedure, once a general superposition is considered, all its instances are unnecessary inferences and, thus, do not have to be considered. It is also shown that this result can be combined with another criterion, called the prime superposition criterion, proposed by Kapur, Musser, and Narendran, thus implying that prime and general superpositions are sufficient. These results should be applicable to other approaches toward automated reasoning, too. These criteria can be effectively implemented, and their implementation has resulted in automatically proving instances of Jacobson's theorem (also known as the ring commutativity problems) usingRRL (Rewrite Rule Laboratory), a theorem prover based on rewriting techniques and completion.A preliminary version of this paper appeared in a paper entitled Consider only general superpositions in completion procedures in theProceedings of the Third International Conference on Rewriting Techniques and Applications, Chapel Hill, NC, April, 1989, Lecture Notes in Computer Science, Vol. 355, Springer-Verlag, Berlin, pp. 513–527. Part of the work of Hantao Zhang was done at the Rensselaer Polytechnic Institute, New York, and he was partially supported by National Science Foundation Grant No. CCR-8408461; also affiliated with Institute of Programming and Logics at SUNY, Albany, NY, and RPI. Deepak Kapur was partially supported by National Science Foundation Grantr Nos. CCR-8408461 and CCR-8906678.     

Deformation structure in a Ti-24Al-11Nb alloy

A Ti-24Al-11Nb alloy has been heat-treated so as to obtain a microstructure of coarse α₂ particles (D0₁₉ structure based on Ti₃Al) in a matrix of the ordered β_o phase (B2 structure based on Ti₂AlNb). Dislocation structures generated by tensile strains of ∼2 pct at room temperature have been analyzed by transmission electron microscopy The β_o phase is shown to deform inhomogeneously on {110}, {112}, and {123} planes by α/〈211〉 slip. The slipband structure is complex, consisting of segments of heavily pinned edge dislocations with periodic cross slip of screw components on to secondary slip planes. Incompatibility stresses at α₂/β_o interfaces can generate fine α[100] slip as well. The α₂ phase deforms independently by α dislocation slip. Slipbands in the β_o phase can shear the α₂ phase by activatingc +a/2 slip on and slip planes, as well asa slip on higher order pyramidal planes, where the parallelism of the specific slip system is permitted by the Burgers relationship between the two phases.     

Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

Least squares large margin distribution machine for regression

Better prediction ability is the main objective of any regression-based model. Large margin Distribution Machine for Regression (LDMR) is an efficient approach where it tries to reduce both loss functions, i.e. ε-insensitive and quadratic loss to diminish the effects of outliers. However, still, it has a significant drawback, i.e. high computational complexity. To achieve the improved generalization of the regression model with less computational cost, we propose an enhanced form of LDMR named as Least Squares Large margin Distribution Machine-based Regression (LS-LDMR) by transforming the inequality conditions alleviate to equality conditions. The elucidation is attained by handling a system of linear equations where we need to measure the inverse of the matrix only. Hence, there is no need to solve the large size of the quadratic programming problem, unlike in the case of other regression-based algorithms as SVR, Twin SVR, and LDMR. The numerical experiment has been performed on the benchmark real-life datasets along with synthetically generated datasets by using the linear and Gaussian kernel. All the experiments of presented LS-LDMR are analyzed with standard SVR, Twin SVR, primal least squares Twin SVR (PLSTSVR), ε-Huber SVR (ε-HSVR), ε-support vector quantile regression (ε-SVQR), minimum deviation regression (MDR), and LDMR, which shows the effectiveness and usability of LS-LDMR. This approach is also statistically validated and verified in terms of various metrics.

     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Aggregation dynamics of particles in a microchannel due to an applied magnetic field

Functionalized magnetic microspheres have promising applications in different microfluidic devices including MEMS-scale biosensors. These particles exhibit magnetic field-induced aggregation, which can be harnessed to achieve several practical tasks in microfluidic devices. For this, the particle aggregation needs to be well characterized. Herein, a numerical simulation and experimental validation of particle-chaining is presented. Simulations show that the particle aggregation time scales linearly with a group parameter. The predicted growth of one- two- and three-particle chains with time shows a similar trend as that found in the experiments. The results of the study could help predicting the performance of magnetic aggregate-based lab-on-a-chip devices.