Separation and Extraction of Compound-Fault Signal Based on Multi-Constraint Non-Negative Matrix Factorization.

To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed. In view of the existing NMF algorithm not performing well in the underdetermined blind source separation, the ß-divergence constraints and determinant constraints are introduced in the NMF algorithm, which can enhance local feature information and reduce redundant components by constraining the objective function. In addition, the Sine-bell window function is selected as the processing method for short-time Fourier transform (STFT), and it can preserve the overall feature distribution of the original signal. The original vibration signal is first transformed into time-frequency domain with the STFT, which describes the local characteristic of the signal from the time-frequency distribution. Then, the multi-constraint NMF is applied to reduce the dimensionality of the data and separate feature components in the low dimensional space. Meanwhile, the parameter WK is constructed to filter the reconstructed signal that recombined with the feature component in the time domain. Ultimately, the separated signals will be subjected to envelope spectrum analysis to detect fault features. The simulated and experimental results indicate the effectiveness of the proposed approach, which can realize the separation of multi-source signals and their fault diagnosis of bearings. In addition, it is also confirmed that the proposed method, juxtaposed with the NMF algorithm of the traditional objective function, is more applicable for compound fault diagnosis of the rotating machinery.

Enhancing heat transfer efficiency in corrugated tube heat exchangers: A comprehensive approach through structural optimization and field synergy analysis.

In this paper, a precise and efficient method to optimize corrugated tube heat exchangers is proposed by combining computational fluid dynamics simulation with optimization. The optimization of tubular heat exchangers involves contradictory Colburn coefficient j, and the friction coefficient f, so it is a multi-objective optimization problem. The approximate model is obtained by an extreme learning machine, and the structure parameter of the heat exchanger is optimized by the nondominated sorting genetic algorithm-Ⅱ. Compared to the results between the original and optimized tube, the optimized structure Colburn coefficient increased by 5.1 % and the friction coefficient decreased by 9.3 %. Finally, the internal flow field is compared qualitatively from temperature, pressure, and velocity. The optimization effect is further emphasized by using the field synergy theory.

Natural Sunlight Photocatalytic Synthesis of Benzoxazole-Bridged Covalent Organic Framework for Photocatalysis.

Although natural sunlight-mediated photocatalysis is a clean, efficient, and green approach to access organic products, its application in the synthesis of covalent organic frameworks (COFs), however, is still unprecedented. Herein, we first report the sunlight photocatalytic synthesis of COF under ambient conditions. Furthermore, this "window ledge" reaction generated benzoxazole-linked COF is stable and can be applied as a reusable photocatalyst to highly promote visible-light-driven aerobic oxidation of sulfides to sulfoxides. These results not only enrich the COF synthetic methodology but also open a new route to access COFs in a green and sustainable way.

Research on supporting mechanism of ancillary service of PV system to grid energy efficiency based on multi-time and space-time operation.

Under the background of high-energy penetration of new energy into the power grid, this paper takes the ancillary service capability of photovoltaic energy integrated into the grid as the starting point and builds a photovoltaic system reactive power service impact evaluation model on the grid energy efficiency. This is based on the multi-temporal and spatial scale operation mode, in order to study the supporting principles of photovoltaic system reactive power services on the energy efficiency of grid operation and the law of influence on system energy efficiency changes. In this way, the space for power system energy efficiency improvement and the reactive power service market value of renewable energy are explored to improve the renewable energy auxiliary services participation in the theoretical system of electric power spot market transactions. The research conclusions can provide a decision-making reference for system dynamic energy efficiency management and can assist relevant market entities to make optimal decisions in spot market transactions, and provide empirical data for improving the theory of renewable energy participation in auxiliary service market transactions.

High Quality Plasmonic Sensors Based on Fano Resonances Created through Cascading Double Asymmetric Cavities.

In this paper, a type of compact nanosensor based on a metal-insulator-metal structure is proposed and investigated through cascading double asymmetric cavities, in which their metal cores shift along different axis directions. The cascaded asymmetric structure exhibits high transmission and sharp Fano resonance peaks via strengthening the mutual coupling of the cavities. The research results show that with the increase of the symmetry breaking in the structure, the number of Fano resonances increase accordingly. Furthermore, by modulating the geometrical parameters appropriately, Fano resonances with high sensitivities to the changes in refractive index can be realized. A maximum figure of merit (FoM) value of 74.3 is obtained. Considerable applications for this work can be found in bio/chemical sensors with excellent performance and other nanophotonic integrated circuit devices such as optical filters, switches and modulators.

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.