Spin-strain coupling in a 3-D transition metal oxides

ab-initio Hartree Fock (HF), density functional theory (DFT) and hybrid potentials were employed to compute the optimized lattice parameters and elastic properties of perovskite 3-d transition metal oxides. The optimized lattice parameters and elastic properties are interdependent in these materials. An interaction is observed between the electronic charge, spin and lattice degrees of freedom in 3-d transition metal oxides. The coupling between the electronic charge, spin and lattice structures originates due to localization of d-atomic orbitals. The coupling between the electronic charge, spin and crystalline lattice also contributes in the ferroelectric and ferromagnetic properties in perovskites. The cubic and tetragonal crystalline structures of perovskite transition metal oxides of ABO3 are studied. The electronic structure and the physics of 3-d perovskite materials is complex and less well considered. Moreover, the novelty of the electronic structure and properties of these perovskites transition metal oxides exceeds the challenge offered by their complex crystalline structures. To achieve the objective of understanding the structure and property relationship of these materials the first-principle computational method is employed. CRYSTAL09 code is employed for computing crystalline structure, elastic, ferromagnetic and other electronic properties. Second-order elastic constants (SOEC) and bulk moduli (B) are computed in an automated process by employing ELASTCON (elastic constants) and EOS (equation of state) programs in CRYSTAL09 code. ELASTCON, EOS and other computational algorithms are utilized to determine the elastic properties of tetragonal BaTiO3, rutile TiO2, cubic and tetragonal BaFeO3 and the ferromagentic properties of 3-d transition metal oxides. Multiple methods are employed to crosscheck the consistency of our computational results. Computational results have motivated us to explore the ferromagnetic properties of 3-d transition metal oxides. Billyscript and CRYSTAL09 code are employed to compute the optimized geometry of the cubic and tetragonal crystalline structure of transition metal oxides of Sc to Cu. Cubic crystalline structure is initially chosen to determine the effect of lattice strains on ferromagnetism due to the spin angular momentum of an electron. The 3-d transition metals and their oxides are challenging as the basis functions and potentials are not fully developed to address the complex physics of the transition metals. Moreover, perovskite crystalline structures are extremely challenging with respect to the quality of computations as the latter requires the well established methods. Ferroelectric and ferromagnetic properties of bulk, surfaces and interfaces are explored by employing CRYSTAL09 code. In our computations done on cubic TMOs of Sc-Fe it is observed that there is a coupling between the crystalline structure and FM/AFM spin polarization. Strained crystalline structures of 3-d transition metal oxides are subjected to changes in the electromagnetic and electronic properties. The electronic structure and properties of bulk, composites, surfaces of 3-d transition metal oxides are computed successfully.

OUT-OF-STEP DETECTION BASED ON ZUBOV’S APPROXIMATION BOUNDARY METHOD

Disturbances in power systems may lead to electromagnetic transient oscillations due to mismatch of mechanical input power and electrical output power. Out-of-step conditions in power system are common after the disturbances where the continuous oscillations do not damp out and the system becomes unstable. Existing out-of-step detection methods are system specific as extensive off-line studies are required for setting of relays. Most of the existing algorithms also require network reduction techniques to apply in multi-machine power systems. To overcome these issues, this research applies Phasor Measurement Unit (PMU) data and Zubov’s approximation stability boundary method, which is a modification of Lyapunov’s direct method, to develop a novel out-of-step detection algorithm. The proposed out-of-step detection algorithm is tested in a Single Machine Infinite Bus system, IEEE 3-machine 9-bus, and IEEE 10-machine 39-bus systems. Simulation results show that the proposed algorithm is capable of detecting out-of-step conditions in multi-machine power systems without using network reduction techniques and a comparative study with an existing blinder method demonstrate that the decision times are faster. The simulation case studies also demonstrate that the proposed algorithm does not depend on power system parameters, hence it avoids the need of extensive off-line system studies as needed in other algorithms.

SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.