Electrical properties of ZnSe-CdS alloy films

Thin films of ZnSe _x CdS_1–x (t 0.6 m) over the entire range of x, were deposited on glass substrates at two temperatures, T _s (350 and 470 K) by vacuum evaporation. X-ray diffraction studies showed that all the films were polycrystalline in nature. Films prepared at 470 K were nearly stoichiometric. Grain size increased with substrate temperature, T _s. The electrical conductivity and Hall measurements were carried out by d.c. van der Pauw technique. Hall effect studies/hot probe test showed that all the films were of n-type conductivity. Hall mobility increased with T _s. In addition, mobilities increased with temperature in films of all compositions, indicating the dominance of grain-boundary scattering. Grain-boundary potentials were in range 0.03–0.06 eV.     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

Evaluating the validity of data instances against ontology evolution over the Semantic Web

It is natural for ontologies to evolve over time. These changes could be at structural and semantic levels. Due to changes to an ontology, its data instances may become invalid, and as a result, may become non-interpretable. In this paper, we address precisely this problem, validity of data instances due to ontological evolution. Towards this end, we make the following three novel contributions to the area of Semantic Web. First, we propose formal notions of structural validity and semantic validity of data instances, and then present approaches to ensure them. Second, we propose semantic view as part of an ontology, and demonstrate that it is sufficient to validate a data instance against the semantic view rather than the entire ontology. We discuss how the semantic view can be generated through an implication analysis, i.e., how semantic changes to one component imply semantic changes to other components in the ontology. Third, we propose a validity identification approach that employs locally maintaining a hash value of the semantic view at the data instance.     

Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification

The wavelet transform (WT) is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In present work, a discrete wavelet, Daubechies wavelets (db1–db15) is used for feature extraction and their relative effectiveness in feature extraction is compared. The major steps in pattern classification are feature extraction and classification. This paper investigates the use of discrete wavelets for feature extraction and a Decision Tree for classification. J48 Decision Tree algorithm has been used for feature selection as well as for classification. This paper illustrates the powerfulness and flexibility of the discrete wavelet transform to decompose linear and non-linear processing of vibration signal.     

Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays

This paper is concerned with the problem of asymptotic stability of neutral type Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional (LKF), reciprocal convex technique and Jensen’s inequality are used to delay-dependent conditions are established to analysis the asymptotic stability of Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. These stability conditions are formulated as linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. Finally numerical examples are given to illustrate the usefulness of our proposed method.     

Security analysis of GTRBAC and its variants using model checking

Security analysis is a formal verification technique to ascertain certain desirable guarantees on the access control policy specification. Given a set of access control policies, a general safety requirement in such a system is to determine whether a desirable property is satisfied in all the reachable states. Such an analysis calls for the use of formal verification techniques. While formal analysis on traditional Role Based Access Control (RBAC) has been done to some extent, recent extensions to RBAC lack such an analysis. In this paper, we consider the temporal RBAC extensions and propose a formal technique using timed automata to perform security analysis by analyzing both safety and liveness properties. Using safety properties one ensures that something bad never happens while liveness properties show that some good state is also achieved. GTRBAC is a well accepted generalized temporal RBAC model which can handle a wide range of temporal constraints while specifying different access control policies. Analysis of such a model involves a process of mapping a GTRBAC based system into a state transition system. Different reduction rules are proposed to simplify the modeling process depending upon the constraints supported by the system. The effect of different constraints on the modeling process is also studied.     

Study of transformation behavior in a Ti–4.4 Ta–1.9 Nb alloy

An alloy of composition Ti–4.4 wt.% Ta–1.9 wt.% Nb is being developed as a structural material for corrosion applications, as titanium and its alloys possess excellent corrosion resistance in many oxidizing media. The primary physical metallurgy database for the Ti–4.4 wt.% Ta–1.9 wt.% Nb alloy is being presented for the first time. Determination of the β transus, M_s temperature and classification of the alloy have been carried out, employing a variety of microscopy techniques, X-ray diffraction (XRD), micro-hardness and differential scanning calorimetry (DSC). The β transition temperature or β transus determined using different experimental techniques was found to agree very well with evaluations based on empirical calculations. Based on chemistry and observed room temperature microstructure, the alloy has been classified as an + β titanium alloy. The high temperature β transforms to either ′ or + β by a martensitic or Widmanstatten transformation. The mechanisms of transformation of β under different conditions and characteristics of different types of have been studied and discussed in this paper.     

Simulated annealing heuristics for the dynamic facility layout problem

In today's economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in product mix and demand. Therefore, this paper considers the problem of arranging and rearranging (when there are changes between the flows of materials between departments) manufacturing facilities such that the sum of the material handling and rearrangement costs is minimized. This problem is known as the dynamic facility layout problem (DFLP). In this paper, two simulated annealing (SA) heuristics are developed for the DFLP. The first SA heuristic (SA I) is a direct adaptation of SA to the DFLP. The second SA heuristic (SA II) is the same as SA I with a look-ahead/look-back strategy added. To test the performance of the heuristics, a data set taken from the literature is used in the analysis. The results obtained show that the proposed heuristics are very effective for the dynamic facility layout problem.     

Condition monitoring studies on spindle bearing of a lathe

In modern industry, machinery must become increasingly flexible and automatic. In order to increase productivity, enhance quality and reduce cost, machine tools have to work free of any failure. When a failure occurs in a machine tool, it is necessary to identify the causes as early as possible. Machine tool condition monitoring is very important to achieve this goal. Condition monitoring is generally used on the critical subsystem of any machine tool. This paper endeavors to focus on the condition monitoring aspects on the machine tool element. In the present study, a critical subsystem has been identified based on the failure data analysis. Condition monitoring techniques like vibration monitoring, acoustic emission, Shock Pulse Method (SPM) and surface roughness have been successfully used for fault identification.