Correlation Between The Stability Of Carbonates In Ternary Ln2o3-H2o-Co2 Hydrothermal Systems And Lanthanide Systematics

Phase diagrams for ternary Ln2O3-H2O-CO2 systems for the entire lanthanide series (except promethium) were studied at temperatures in the range 100–950 °C and pressures up to 3000 bar. The phase diagrams obtained for the heavier lanthanides are far more complex, with the appearance of a number of stable carbonate phases. New carbonates isolated from lanthanide systems (Ln ≡ Tm, Yb, Lu) include Ln6(OH)4(CO3)7, Ln4(OH)6-(CO3)3, Ln2O(OH)2CO3, Ln6O2(OH)8(CO3)3 and Ln12O7(OH)10(CO3)6. Stable carbonate phases common to all the lighter lanthanides are hexagonal LnOHCO3 and hexagonal Ln2O2CO3. Ln2(CO3)3• 3H2O is stable from samarium onwards and orthorhombic LnOHCO3 is stable from gadolinium onwards. On the basis of the appearance of stable carbonates, four different groups of lanthanides were established: lanthanum to neodymium, promethium to europium, terbium to erbium and thulium to lutetium. Gadolinium is the connecting element between groups II and III. This is in accordance with the tetrad classification for f transition elements.

Hydrothermal phase equilibria in Ln2O3-H2O-CO2 systems for Tm, Yb and Lu

Phase diagrams for Tm2O3-H2O-CO2. Yb2O3-H2O-CO2 and Lu2O3-H2O-CO2 systems at 650 and 1300 bars have been investigated in the temperature range of 100–800°C. The phase diagrams are far more complex than those for the lighter lanthanides. The stable phases are Ln(OH)3, Ln2(CO3)3.3H2O (tengerite phase), orthorhombic-LnOHCO3, hexagonal-Ln2O2CO3. LnOOH and cubic-Ln2O3. Ln(OH)3 is stable only at very low partial pressures of CO2. Additional phases stabilised are Ln2O(OH)2CO3and Ln6(OH)4(CO3)7 which are absent in lighter lanthanide systems. Other phases, isolated in the presence of minor alkali impurities, are Ln6O2(OH)8(CO3)3. Ln4(OH)6(CO3)3 and Ln12O7(OH)10,(CO3)6. The chemical equilibria prevailing in these hydrothermal systems may be best explained on the basis of the four-fold classification of lanthanides.

Optimal iterative schemes for computing the Moore-Penrose matrix inverse

Among the iterative schemes for computing the Moore — Penrose inverse of a woll-conditioned matrix, only those which have an order of convergence three or two are computationally efficient. A Fortran programme for these schemes is provided.

Design of multimedia surveillance systems

This article addresses the problem of how to select the optimal combination of sensors and how to determine their optimal placement in a surveillance region in order to meet the given performance requirements at a minimal cost for a multimedia surveillance system. We propose to solve this problem by obtaining a performance vector, with its elements representing the performances of subtasks, for a given input combination of sensors and their placement. Then we show that the optimal sensor selection problem can be converted into the form of Integer Linear Programming problem (ILP) by using a linear model for computing the optimal performance vector corresponding to a sensor combination. Optimal performance vector corresponding to a sensor combination refers to the performance vector corresponding to the optimal placement of a sensor combination. To demonstrate the utility of our technique, we design and build a surveillance system consisting of PTZ (Pan-Tilt-Zoom) cameras and active motion sensors for capturing faces. Finally, we show experimentally that optimal placement of sensors based on the design maximizes the system performance.

Computing magnetic anisotropy constants of single molecule magnets

We present here a theoretical approach to compute the molecular magnetic anisotropy parameters, D (M) and E (M) for single molecule magnets in any given spin eigenstate of exchange spin Hamiltonian. We first describe a hybrid constant M (S) valence bond (VB) technique of solving spin Hamiltonians employing full spatial and spin symmetry adaptation and we illustrate this technique by solving the exchange Hamiltonian of the Cu6Fe8 system. Treating the anisotropy Hamiltonian as perturbation, we compute the D (M)and E(M) values for various eigenstates of the exchange Hamiltonian. Since, the dipolar contribution to the magnetic anisotropy is negligibly small, we calculate the molecular anisotropy from the single-ion anisotropies of the metal centers. We have studied the variation of D (M) and E(M) by rotating the single-ion anisotropies in the case of Mn12Ac and Fe-8 SMMs in ground and few low-lying excited states of the exchange Hamiltonian. In both the systems, we find that the molecular anisotropy changes drastically when the single-ion anisotropies are rotated. While in Mn12Ac SMM D (M) values depend strongly on the spin of the eigenstate, it is almost independent of the spin of the eigenstate in Fe-8 SMM. We also find that the D (M)value is almost insensitive to the orientation of the anisotropy of the core Mn(IV) ions. The dependence of D (M) on the energy gap between the ground and the excited states in both the systems has also been studied by using different sets of exchange constants.

A method of fraud & intrusion detection for e-payment systems in mobile

The need for paying with mobile devices has urged the development of payment systems for mobile electronic commerce. In this paper we have considered two important abuses in electronic payments systems for detection. The fraud, which is an intentional deception accomplished to secure an unfair gain, and an intrusion which are any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Most of the available fraud and intrusion detection systems for e-payments are specific to the systems where they have been incorporated. This paper proposes a generic model called as Activity-Event-Symptoms(AES) model for detecting fraud and intrusion attacks which appears during payment process in the mobile commerce environment. The AES model is designed to identify the symptoms of fraud and intrusions by observing various events/transactions occurs during mobile commerce activity. The symptoms identification is followed by computing the suspicion factors for event attributes, and the certainty factor for a fraud and intrusion is generated using these suspicion factors. We have tested the proposed system by conducting various case studies, on the in-house established mobile commerce environment over wired and wire-less networks test bed.

Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

An efficient and scalable checkpointing and recovery algorithm for distributed systems

In this paper, we describe an efficient coordinated-checkpointing and recovery algorithm which can work even when the channels are assumed to be non-FIFO, and messages may be lost. Nodes are assumed to be autonomous, and they do not block while taking checkpoints. Based on the local conditions, any process can request the previous coordinator for the 'permission' to initiate a new checkpoint. Allowing multiple initiators of checkpoints avoids the bottleneck associated with a single initiator, but the algorithm permits only a single instance of checkpointing process at any given time, thus reducing much of the overhead associated with multiple initiators of distributed algorithms.

A new methodology for analyzing distributed systems modeled by petri nets

Distributed computing systems can be modeled adequately by Petri nets. The computation of invariants of Petri nets becomes necessary for proving the properties of modeled systems. This paper presents a two-phase, bottom-up approach for invariant computation and analysis of Petri nets. In the first phase, a newly defined subnet, called the RP-subnet, with an invariant is chosen. In the second phase, the selected RP-subnet is analyzed. Our methodology is illustrated with two examples viz., the dining philosophers' problem and the connection-disconnection phase of a transport protocol. We believe that this new method, which is computationally no worse than the existing techniques, would simplify the analysis of many practical distributed systems.

Performability of automated manufacturing systems with centralized material handling

We present a framework for performance evaluation of manufacturing systems subject to failure and repair. In particular, we determine the mean and variance of accumulated production over a specified time frame and show the usefulness of these results in system design and in evaluating operational policies for manufacturing systems. We extend this analysis for lead time as well. A detailed performability study is carried out for the generic model of a manufacturing system with centralized material handling. Several numerical results are presented, and the relevance of performability analysis in resolving system design issues is highlighted. Specific problems addressed include computing the distribution of total production over a shift period, determining the shift length necessary to deliver a given production target with a desired probability, and obtaining the distribution of Manufacturing Lead Time, all in the face of potential subsystem failures.

Parallel computing concepts and methods for floquet analysis of helicopter trim and stability

Floquet analysis is widely used for small-order systems (say, order M < 100) to find trim results of control inputs and periodic responses, and stability results of damping levels and frequencies, Presently, however, it is practical neither for design applications nor for comprehensive analysis models that lead to large systems (M > 100); the run time on a sequential computer is simply prohibitive, Accordingly, a massively parallel Floquet analysis is developed with emphasis on large systems, and it is implemented on two SIMD or single-instruction, multiple-data computers with 4096 and 8192 processors, The focus of this development is a parallel shooting method with damped Newton iteration to generate trim results; the Floquet transition matrix (FTM) comes out as a byproduct, The eigenvalues and eigenvectors of the FTM are computed by a parallel QR method, and thereby stability results are generated, For illustration, flap and flap-lag stability of isolated rotors are treated by the parallel analysis and by a corresponding sequential analysis with the conventional shooting and QR methods; linear quasisteady airfoil aerodynamics and a finite-state three-dimensional wake model are used, Computational reliability is quantified by the condition numbers of the Jacobian matrices in Newton iteration, the condition numbers of the eigenvalues and the residual errors of the eigenpairs, and reliability figures are comparable in both the parallel and sequential analyses, Compared to the sequential analysis, the parallel analysis reduces the run time of large systems dramatically, and the reduction increases with increasing system order; this finding offers considerable promise for design and comprehensive-analysis applications.

Solving a part classification problem using simulated annealing-like hybrid

Part classification and coding is still considered as laborious and time-consuming exercise. Keeping in view, the crucial role, which it plays, in developing automated CAPP systems, the attempts have been made in this article to automate a few elements of this exercise using a shape analysis model. In this study, a 24-vector directional template is contemplated to represent the feature elements of the parts (candidate and prototype). Various transformation processes such as deformation, straightening, bypassing, insertion and deletion are embedded in the proposed simulated annealing (SA)-like hybrid algorithm to match the candidate part with their prototype. For a candidate part, searching its matching prototype from the information data is computationally expensive and requires large search space. However, the proposed SA-like hybrid algorithm for solving the part classification problem considerably minimizes the search space and ensures early convergence of the solution. The application of the proposed approach is illustrated by an example part. The proposed approach is applied for the classification of 100 candidate parts and their prototypes to demonstrate the effectiveness of the algorithm. (C) 2003 Elsevier Science Ltd. All rights reserved.

A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features

In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Minimal unsatisfiable sets: Classification and bounds

Proving the unsatisfiability of propositional Boolean formulas has applications in a wide range of fields. Minimal Unsatisfiable Sets (MUS) are signatures of the property of unsatisfiability in formulas and our understanding of these signatures can be very helpful in answering various algorithmic and structural questions relating to unsatisfiability. In this paper, we explore some combinatorial properties of MUS and use them to devise a classification scheme for MUS. We also derive bounds on the sizes of MUS in Horn, 2-SAT and 3-SAT formulas.