Scattering from a thin perfectly-conducting square plate

Extensive numerical experiments on the scattering from a thin perfectly conducting square plate have been carried out to assess the performance of the exact analytical expressions for the electromagnetic field of a rectangular patch with uniform and linear distributions of current, in connection with the method of moments. Two solution schemes, employing pulses and roof-top functions for approximating the surface current on the plate, have been used. Convergence rates and results for the two solution schemes are compared with each other, as well as with an efficient solution by A.W. Glisson and D.R. Wilton (1980). The overall performance indicated by the numerical experiments suggests that it would be useful to use the exact numerical expressions in the solution of problems where accurate computation of the field radiated by such current sources is required     

Recent Advances in Hierarchical Reinforcement Learning

Reinforcement learning is bedeviled by the curse of dimensionality: the number of parameters to be learned grows exponentially with the size of any compact encoding of a state. Recent attempts to combat the curse of dimensionality have turned to principled ways of exploiting temporal abstraction, where decisions are not required at each step, but rather invoke the execution of temporally-extended activities which follow their own policies until termination. This leads naturally to hierarchical control architectures and associated learning algorithms. We review several approaches to temporal abstraction and hierarchical organization that machine learning researchers have recently developed. Common to these approaches is a reliance on the theory of semi-Markov decision processes, which we emphasize in our review. We then discuss extensions of these ideas to concurrent activities, multiagent coordination, and hierarchical memory for addressing partial observability. Concluding remarks address open challenges facing the further development of reinforcement learning in a hierarchical setting.     

Refurbishment and safety upgradation of research reactor Cirus

Cirus, a 40 MW t, vertical tank type research reactor, having wide range of research facilities, was commissioned in the year 1960. This research reactor, situated at Mumbai, India has been operated and utilized extensively for isotope production, material testing and neutron beam research for nearly four decades. With a view to assess the residual life of the reactor, detailed ageing studies were carried out during the early 1990s. Based on these studies, refurbishment of Cirus for its life extension was taken up. During refurbishment, additional safety features were incorporated in various systems to qualify them for the current safety standards. This paper gives the details of the operating experiences, utilization of the reactor along with methodologies followed for carrying out detailed ageing studies, refurbishment and safety upgradation for its life extension.     

Recursive algorithms for implementing digital image filters

The B-spline functions are used to develop recursive algorithms for the efficient implementation of two-dimensional linear digital image filters. These filters may be spatially varying. The B-splines are used in a representation of the desired point spread function. We show that this leads to recursive algorithms and hardware implementations which are more efficient than either direct spatial domain filter realizations or FFT implementations. The Z-transform is used to develop a discrete version of Duhamel's theorem. A computer architecture for B-spline image filters is proposed and a complexity analysis and comparison to other approaches is provided.     

A multi-echelon production–inventory system with supply disruption

The article investigates an integrated multi-layer supply chain model consisting of supplier, manufacturer and retailer while supply disruption, machine breakdown, safety stock, maintenance breakdown occur simultaneously. At beginning of the production, manufacturer keeps some raw materials in stock received from second supplier at high price, as safety stock due to supply disruption of first supplier. Corrective maintenance is done immediately to restore its normal stage when machine breakdown occurs. Stock out situations at manufacturer and retailer are considered due to disruption of production for machine breakdown. The integrated expected costs of the chain in centralized (collaborating) and decentralized (Stakelberg approach) system are compared. A numerical example and its sensitivity analysis are provided to test feasibility of the model.     

A non-parametric method to determine basic probability assignment for classification problems

As an important tool for knowledge representation and decision-making under uncertainty, Dempster-Shafer evidence theory (D-S theory) has been used in many fields. The application of D-S theory is critically dependent on the availability of the basic probability assignment (BPA). The determination of BPA is still an open issue. A non-parametric method to obtain BPA is proposed in this paper. This method can handle multi-attribute datasets in classification problems. Each attribute value of the dataset sample is treated as a stochastic quantity. Its non-parametric probability density function (PDF) is calculated using the training data, which can be regarded as the probability model for the corresponding attribute. The BPA function is then constructed based on the relationship between the test sample and the probability models. The missing attribute values in datasets are treated as ignorance in the framework of the evidence theory. This method does not have the assumption of any particular distribution. As a result, it can be flexibly used in many engineering applications. The obtained BPA can avoid high conflict between evidence, which is desired in data fusion. Several benchmark classification problems are used to demonstrate the proposed method and to compare against existing methods. The constructed classifier based on the proposed method compares well to the state-of-the-art algorithms.     

Al2O3─SiC Composites from Aluminosilicate Precursors

Al₂O₃ and SiC composite materials have been produced from mixtures of aluminosilicates (both natural minerals and synthetic) and carbon as precursor materials. These composites are produced by heating a mixture of kaolinite (or synthetic aluminosilicates) and carbon in stoichiometric proportion above 1550°C, so that only Al₂O₃ and SiC remain as the major phases. A similar process has also been used for synthesizing other composite powders having mixtures of Al₂O₃, SiC, TiC, and ZrO₂ in different proportions (all compounds together or selective mixtures of some of them), as desired. The microstructure of hot-pressed dense compacts, produced from these powders, revealed that the SiC phase is distributed very homogeneously, even occasionally within Al₂O₃ grains on a nanosize scale. The homogeneous distribution of SiC particles within the system produced high fracture toughness of the hot-pressed material (K_IC∼ 7.0 MPa · m^1/2) and having Vicker's hardness values greater than 2000 kgf/mm².