Thermodynamic properties of oxygen in RE-O (RE = Gd, Tb, Dy, Er) solid solutions

The oxygen potentials of four rare-earth metal – oxygen (RE–O: RE=Gd, Dy, Tb, Er) solid solutions have been measured by equilibration with yttrium – oxygen (Y–O) and titanium – oxygen (Ti–O) solid solutions. Rare-earth metal, yttrium and titanium samples were immersed in calcium-saturated CaCl2 melt at temperatures between 1093 and 1233 K. Homogeneous oxygen potential was established in the metallic samples through the fused salt, which contains some dissolved CaO. The metallic samples were analyzed for oxygen after quenching. The oxygen potentials of RE–O solid solutions were determined using either Y–O or Ti–O solid solution as the reference. This method enabled reliable measurement of extremely low oxygen potentials at high temperature (circa pO2=10−48 atm at 1173 K). It was found that the oxygen affinity of the metals decreases in the order: Y>Er>Dy>Tb>Gd>Ti. Values for the standard Gibbs energy of solution of oxygen in RE metals obtained in this study, permit assessment of the extent of deoxidation that can be achieved with various purification techniques. It may be possible to achieve an oxygen level of 10 mass ppm using an electrochemical deoxidation method.

Electrochemical deoxidation of RE–O (RE=Gd, Tb, Dy, Er) solid solutions

The removal of oxygen from rare-earth metals (RE, RE=Gd, Tb, Dy, Er) by an electrochemical deoxidation method was investigated. A titanium basket containing the rare-earth metal sample, submerged in molten CaCl2 electrolyte, formed the cathode of an electrolysis cell. A high-purity graphite anode was used. The calcium metal produced at the cathode effectively deoxidized the rare-earth metal. Carbon monoxide and dioxide were generated at the graphite anode. Rare-earth metals containing more than 2000 mass ppm oxygen were deoxidized to 10–50 mass ppm level by electrolysis at 1189 K for 36 ks (10 h). Cyclic voltammetry was used to characterize the molten salt at different stages of the process. The effectiveness of the process is discussed with the aid of a chemical potential diagram for RE–O solid solutions. The new electrochemical technique is compared with the conventional deoxidation methods reported in the literature. The possibility of nitrogen removal from the rare-earth metals by the electrochemical method is outlined.

Electrochemical detection and removal of lead in water using poly(propylene imine) modified re-compressed exfoliated graphite electrodes

Modification of exfoliated graphite (EG) electrode with generation 2 poly(propylene imine) dendrimer by electrodeposition resulted in an electrochemical sensor which was used to detect lead ions in water to a limit of 1 ppb and a linear response between 2.5 and 40 ppb using square wave anodic stripping voltammetry (SW-ASV). Pb(II) was also removed from spiked water sample using a 40-mm diameter unmodified EG electrode with an applied potential of -1,000 mV for 180 min. A removal efficiency of 99% was calculated from a 150 mL sample. The results obtained in both cases using SW-ASV, correlated with atomic absorption spectroscopy.

Hypersonic wave drag reduction in re-entry capsules using concentrated energy deposition

Experiments are carried out in a shock tunnel at a nominal Mach number of 5.75 in order to study the effect of concentrated energy deposition on the drag force experienced by a 120° blunt cone. Electrical energy was deposited along the stagnation streamline of the model using a high voltage DC discharge circuit (1.5 – 3.5KW) and the drag force was measured by a single component accelerometer balance. Numerical simulations were also carried complimenting the experiments. These simulations showed a substantial drag reduction (20% ~ 65%) whereas the experiments show no appreciable reduction in drag

Strong electron correlation of Re 5d electrons in Ca2FeReO6

We have investigated the electronic structure of a double perovskite Ca2FeReO6 using photoemission spectroscopy and LDA+U bandstructure calculations. Small spectral weight at the Fermi level observed above the metal–insulator transition temperature, gradually disappears with decreasing T, forming a small (≤50 meV) energy gap. To reproduce this small energy gap, we require a very large effective U (Ueff) for Re (4 eV) in addition to Ueff of 4 eV for Fe. From simple calculations in terms of the ionic radii, we demonstrate that the Fe–Re bandwidth is smaller than that of Fe–Mo in Ca2FeMoO6, which should yield a strong electron correlation in the Re 5d bands.

Reinforcement learning controllers for automatic generation control in power systems having re-heat units with GRC and dead-band

A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently pro- posed [1]. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.

Spin-Electron-Phonon Excitation in Re-based Half-Metallic Double Perovskites

A remarkable hardening (similar to 30 cm(-1)) of the normal mode of vibration associated with the symmetric stretching of the oxygen octahedra for the Ba2FeReO6 and Sr2CrReO6 double perovskites is observed below the corresponding magnetic ordering temperatures. The very large magnitude of this effect and its absence for the antisymmetric stretching mode provide evidence against a conventional spin-phonon coupling mechanism. Our observations are consistent with a collective excitation formed by the combination of the vibrational mode with oscillations of Fe or Cr 3d and Re 5d occupations and spin magnitudes.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

Pattern clustering using cooperative game theory

In this paper, we approach the classical problem of clustering using solution concepts from cooperative game theory such as Nucleolus and Shapley value. We formulate the problem of clustering as a characteristic form game and develop a novel algorithm DRAC (Density-Restricted Agglomerative Clustering) for clustering. With extensive experimentation on standard data sets, we compare the performance of DRAC with that of well known algorithms. We show an interesting result that four prominent solution concepts, Nucleolus, Shapley value, Gately point and \tau-value coincide for the defined characteristic form game. This vindicates the choice of the characteristic function of the clustering game and also provides strong intuitive foundation for our approach.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Clustering using Levy Flight Cuckoo Search

In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role.

Identification of coherent synchronous generators in a multi- machine power system using support vector clustering

This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.