Synthesis of Calcium Hexaboride by Electrolysis of Molten Salt

Metallurgical and Materials Transactions B - Formation of calcium hexaboride by means of electrodeposition of boron oxide in molten CaCl2 with additives of CaO and B2O3 was analyzed. It was shown...     

Does repression exist? Memory, pathogenic, unconscious and clinical evidence.

The current dispute regarding the existence of repression has mainly focused on whether people remember or forget trauma. Repression, however, is a multidimensional construct, which, in addition to the memory aspect, consists of pathogenic effects on adjustment and the unconscious. Accordingly, in order to arrive at a more accurate decision regarding the existence of repression, studies relevant to all three areas are reviewed. Moreover, since psychoanalysis regards repression as a key factor in accounting for the development and treatment of neurotic disorders, relevant research from these two domains are also taken into account. This comprehensive evaluation reveals little empirical justification for maintaining the psychoanalytic concept of repression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Approximating the risk for time-to-failure models through using statistics of extremes

This paper studies the approximation of the partitioned multiobjective risk method's (PMRM) extreme event risk function for time-to-failure models. The analytic expression of the approximation for the extreme event risk function is derived through the use of the statistics of extremes for the case where the underlying distribution is of an extreme-value type III for minima. This analytic expression provides an effective theoretical tool for understanding the behavior of conditional expected values for time-to-failure models for a large class of distribution functions used in reliability. The results are confirmed for example problems of exponential and uniform distributions.     

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators (SGs) toward inverter-based resources (IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants (VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources (DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system (MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

     

Disturbance tolerance and rejection of linear systems with imprecise knowledge of actuator input output characteristics

We study the robustness of linear systems with respect to the disturbances and the uncertainties in the actuator input output characteristics. Disturbances either bounded in energy or bounded in magnitude are considered. The actuator input output characteristics are assumed to reside in a so-called generalized sector bounded by piecewise linear curves. Robust bounded state stability of the closed-loop system is first defined and characterized in terms of linear matrix inequalities (LMIs). Based on this characterization, the evaluation of the disturbance tolerance and disturbance rejection capabilities of the closed-loop system under a given feedback law is formulated into and solved as optimization problems with LMI constraints.     

The gray-code filter kernels

In this paper, we introduce a family of filter kernels, the gray-code kernels (GCK) and demonstrate their use in image analysis. Filtering an image with a sequence of gray-code kernels is highly efficient and requires only two operations per pixel for each filter kernel, independent of the size or dimension of the kernel. We show that the family of kernels is large and includes the Walsh-Hadamard kernels, among others. The GCK can be used to approximate any desired kernel and, as such forms, a complete representation. The efficiency of computation using a sequence of GCK filters can be exploited for various real-time applications, such as, pattern detection, feature extraction, texture analysis, texture synthesis, and more     

A non-recursive method for solving the general discrete-time riccati equations related to the H∞ control problem

In this paper we propose a nonrecursive method for solving the general discrete-time algebraic Riccati equation related to the H_∞ control problem (H_∞-DARE). We have achieved this by casting the problem of solving a given H_∞-DARE to the problem of solving an auxiliary continuous-time algebraic Riccati equation associated with the H_∞ control problem (H_∞-CARE) for which the well known nonrecursive methods of solving are available. The advantages of our approach are: it reduces the computation involved in the recursive algorithms while giving much more accurate solutions, and it readily provides the properties of the general H_∞-DARE.