Development and Fabrication of a Multi-Layer Planar Solar Light Absorber Achieving High Absorptivity and Ultra-Wideband Response from Visible Light to Infrared.

The objective of this study is to create a planar solar light absorber that exhibits exceptional absorption characteristics spanning from visible light to infrared across an ultra-wide spectral range. The eight layered structures of the absorber, from top to bottom, consisted of Al2O3, Ti, Al2O3, Ti, Al2O3, Ni, Al2O3, and Al. The COMSOL Multiphysics® simulation software (version 6.0) was utilized to construct the absorber model and perform simulation analyses. The first significant finding of this study is that as compared to absorbers featuring seven-layered structures (excluding the top Al2O3 layer) or using TiO2 or SiO2 layers as substituted for Al2O3 layer, the presence of the top Al2O3 layer demonstrated superior anti-reflection properties. Another noteworthy finding was that the top Al2O3 layer provided better impedance matching compared to scenarios where it was absent or replaced with TiO2 or SiO2 layers, enhancing the absorber's overall efficiency. Consequently, across the ultra-wideband spectrum spanning 350 to 1970 nm, the average absorptivity reached an impressive 96.76%. One significant novelty of this study was the utilization of various top-layer materials to assess the absorption and reflection spectra, along with the optical-impedance-matching properties of the designed absorber. Another notable contribution was the successful implementation of evaporation techniques for depositing and manufacturing this optimized absorber. A further innovation involved the use of transmission electron microscopy to observe the thickness of each deposition layer. Subsequently, the simulated and calculated absorption spectra of solar energy across the AM1.5 spectrum for both the designed and fabricated absorbers were compared, demonstrating a match between the measured and simulated results.

PriMPSO: A Privacy-Preserving Multiagent Particle Swarm Optimization Algorithm.

Centralized particle swarm optimization (PSO) does not fully exploit the potential of distributed or parallel computing and suffers from single-point-of-failure. Particularly, each particle in PSO comprises a potential solution (e.g., traveling route and neural network model parameters) which is essentially viewed as private data. Unfortunately, previously neither centralized nor distributed PSO algorithms fail to protect privacy effectively. Inspired by secure multiparty computation and multiagent system, this article proposes a privacy-preserving multiagent PSO algorithm (called PriMPSO) to protect each particle's data and enable private data sharing in a privacy-preserving manner. The goal of PriMPSO is to protect each particle's data in a distributed computing paradigm via existing PSO algorithms with competitive performance. Specifically, each particle is executed by an independent agent with its own data, and all agents jointly perform global optimization without sacrificing any particle's data. Thorough investigations show that selecting an exemplar from all particles and updating particles through the exemplar are critical operations for PSO algorithms. To this end, this article designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Strict privacy analyses and extensive experiments on a benchmark and a realistic task confirm that PriMPSO not only protects particles' privacy but also has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.

Feature Selection Based on a Large-Scale Many-Objective Evolutionary Algorithm.

The feature selection problem is a fundamental issue in many research fields. In this paper, the feature selection problem is regarded as an optimization problem and addressed by utilizing a large-scale many-objective evolutionary algorithm. Considering the number of selected features, accuracy, relevance, redundancy, interclass distance, and intraclass distance, a large-scale many-objective feature selection model is constructed. It is difficult to optimize the large-scale many-objective feature selection optimization problem by using the traditional evolutionary algorithms. Therefore, this paper proposes a modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA). The proposed algorithm uses polynomial mutation based on variable grouping instead of naive polynomial mutation to improve the efficiency of solving large-scale problems. And a novel worst-case solution replacement strategy using shift-based density estimation is used to replace the poor solution of two individuals with similar search directions to enhance convergence. The experimental results show that MALSMEA is competitive and can effectively optimize the proposed model.

Discriminative Codebook Hashing for Supervised Video Retrieval.

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback-Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.