In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively learned weights can be seamlessly incorporated into the GSRC framework. This integrates the locality structure of the data and validity information of the features into l _2,p -norm regularization to form a unified formulation. Second, IRGSC is very flexible to different size of training set as well as feature dimension thanks to the l _2,p -norm regularization. Third, the derived solution is proved to be a stationary point (globally optimal if p ≥ 1). Comprehensive experiments on representative data sets demonstrate that IRGSC is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face occlusion, corruption, and illumination changes, and so on.

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Recently, low-rank (LR) and total variation (TV) constrained tensor completion algorithms have been broadly studied for image restoration. These algorithms, however, ignore the difference of the intrinsic properties along spatial structure, spectral correlation, and unfolded mode. In this paper, we go further by providing a detailed comparison of the LR and TV properties in matrix and tensor cases, and figure out the LRTV constraints for pixel matrices are more evident and accordant than for others. This inspires us to develop a simple yet effective multichannel LRTV model that is capable of genuinely discovering the intrinsic properties with reduced computational cost. Moreover, due to the suboptimality of nuclear norm and $l_{1}$ norm in approximating the essential low rank and low gradient properties, we employ two enhanced constraints, i.e., truncated nuclear norm (TNN) and total $p$ variation ${\text{T}}_{p}\text{V}$ , for a better performance. This results in a challenging problem since that both TNN and ${\text{T}}_{p}\text{V}$ are nonsmooth and nonconvex. Observing that the Moreau approximation of ${\text{T}}_{p}\text{V}$ constraint is a continuous difference-of-convex function, we then develop a first-order method by repeatedly computing two simple proximal operators. Under mild assumption, we further prove that the sequence generated by our method clusters at a stationary point. Extensive experimental results on color image completion show the efficacy and efficiency of our method over state-of-the-art competitors.

Underwater wet-mateable connectors have been widely used to reduce the cost and the time of installation, maintenance and reconfiguration in many fields, such as the oil and gas (O&G) industry, offshore renewable energy (ORE), and undersea observatories. In the past few years, the authors’ group has made some efforts in developing wet-mateable connectors. This paper presents a methodology for designing and testing a wet-mateable electrical connector. First, an innovative wet-mateable electrical connector with dual-bladder pressure-balanced oil-filled (PBOF) technology is proposed. Second, the generalized equations of differential pressure are derived. Then, a procedure of thermal-electric-structure (TES) coupling simulation is proposed, and a series of finite element analysis (FEA) involving coupled multi-field problems is conducted, including thermal-electric coupling analysis, static structural analysis, and dynamic analysis. Finally, a prototype of the proposed connector is developed successfully, and its electrical performance is verified by the online test in a hydrostatic pressure environment with an ocean depth of 3000 m, which has reached the leading level in China. This paper is the first discloser on wet-mateable connectors in the aspects of design, theory, simulation and testing, which might be helpful to many ocean scientists in developing countries who are technically blocked or could not afford the high cost.