Variational stability of infinite-dimensional optimal control problems

In this paper we examine the variational stability of infinite-dimensional optimal control problems governed by nonlinear evolution equations. Our tools are the Kuratowski—Mosco convergence of sets and the corresponding τ-convergence of functions. We prove the τ-convergence of cost functional and the convergence of the values of the problems, and we examine the variational stability of the solution and reachable sets. These results are then applied to a sequence of nonlinear parabolic distributed-parameter optimal control problems     

Substrate constructed by an array of split ring resonators for a THz planar antenna

Metamaterials are artificial materials offering unique properties which render them useful for various applications. In the present paper, we examine whether it is possible to design a split ring resonators’ (SRRs) based metamaterial substrate for enhancing the performance of planar antennas operating at THz frequencies. Initially, the radiating characteristics of a simple rectangular patch antenna over two well-known SRR substrates are studied. The yielded results are then compared to those of two novel designs of metamaterial antenna substrate with SRRs of decreasing size which are proposed to improve the antenna’s performance.     

An adaptive design for quantized feedback control of uncertain switched linear systems

This paper addresses the problem of asymptotic tracking for switched linear systems with parametric uncertainties and dwell‐time switching, when input measurements are quantized due to the presence of a communication network closing the control loop. The problem is solved via a dynamic quantizer with dynamic offset that, embedded in a model reference adaptive control framework, allows the design of the adaptive adjustments for the control parameters and for the dynamic range and dynamic offset of the quantizer. The overall design is carried out via a Lyapunov‐based zooming procedure, whose main feature is overcoming the need for zooming out at every switching instant, in order to compensate for the possible increment of the Lyapunov function at the switching instants. It is proven analytically that the resulting adjustments guarantee asymptotic state tracking. The proposed quantized adaptive control is applied to the piecewise linear model of the NASA Generic Transport Model aircraft linearized at multiple operating points.     

A higher-order unsplit-field perfectly matched layer for the reflectionless truncation of 3-D spherical FDTD lattices

An enhanced higher-order finite-difference time-domain (FDTD) method for the systematic implementation of 3-D reflectionless perfectly matched layers (PML) in spherical coordinates is presented. By establishing a topologically unsplit-field formulation, the novel technique introduces accurate nonstandard schemes that eliminate the notably intricate lattice dispersion errors. Moreover, the wider spatial increments are treated via self-adaptive compact operators, while a mesh expansion process yields a significant reduction of the absorber's depth. For the temporal variable, the proposed method employs a multistage leapfrog integration that guarantees stability and excitation universality. Hence, because of the optimal configuration of the new PML, it attains a critical annihilation of both propagating and spurious wave families, even for complicated domains. Numerical investigation verifies the superiority of the higher-order algorithm via several unbounded radiation and scattering spherical problems.     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.     

Optimal Placement of Phasor Measurement Units with Linear and Non-linear Models

Abstract—This article presents a non-linear programming-based model for the optimal placement of phasor measurement units. The optimal phasor measurement units placement is formulated to minimize the number of phasor measurement units required for full system observability and to maximize the measurement redundancy at all buses in a power system. A sequential quadratic programming algorithm is used for the solution of the proposed model. The existence of power flow and injection measurements, the limited phasor measurement units channel capacity, the lack of communication facilities in substations, and the single phasor measurement units loss are also incorporated into the initial proposed formulation. The non-linear programming model is applied to IEEE 14- and 118-bus test systems in MATLAB. The accuracy and the effectiveness of the proposed method is verified by comparing the simulation results to those obtained by a binary integer programming model also implemented in MATLAB. The comparative study shows that the proposed non-linear programming model yields the same number of phasor measurement units as the binary integer programming model. A remarkable advantage of the non-linear programming against binary integer linear programming is its capability to give more than one optimal solution, each one having the same minimum number of phasor measurement units (same minimum objective value), but at different locations.     

Immunomodulatory Drugs for the Treatment of B Cell Malignancies

Accumulating evidence suggests that the tumor microenvironment (TME) is involved in disease progression and drug resistance in B cell malignancies, by supporting tumor growth and facilitating the ability of malignant cells to avoid immune recognition. Immunomodulatory drugs (IMiDs) such as lenalidomide have some direct anti-tumor activity, but critically also target various cellular compartments of the TME including T cells, NK cells, and stromal cells, which interfere with pro-tumor signaling while activating anti-tumor immune responses. Lenalidomide has delivered favorable clinical outcomes as a single-agent, and in combination therapy leads to durable responses in chronic lymphocytic leukemia (CLL) and several non-Hodgkin lymphomas (NHLs) including follicular lymphoma (FL), diffuse large B cell lymphoma (DLBCL), and mantle cell lymphoma (MCL). Recently, avadomide, a next generation cereblon E3 ligase modulator (CELMoD), has shown potent anti-tumor and TME immunomodulatory effects, as well as promising clinical efficacy in DLBCL. This review describes how the pleiotropic effects of IMiDs and CELMoDs could make them excellent candidates for combination therapy in the immuno-oncology era—a concept supported by preclinical data, as well as the recent approval of lenalidomide in combination with rituximab for the treatment of relapsed/refractory (R/R) FL.     

Accessibility-based reranking in multimedia search engines

Multimedia Tools and Applications - Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide...     

Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.