Abstract:In recent years, an increasing number of researchers have attempted to link catalytic activity to the acidity and basicity of the oxygen vacancies in catalysts.A deeper understanding of the role played by oxygen vacancy defects in determining catalytic properties and charge carrier dynamics by combining Lewis acid-base active sites can provide valuable insights for the rational development of more efficient catalysts.Oxygen vacancies are the most common defect in transition metal oxides, and catalysts containing oxygen vacancies are closely related to how surface acidity and basicity affect the adsorption, reaction, and desorption processes of reactants, intermediates, and solid surface products.To this end, the location and the acid-base of oxygen vacancies have always played a central role in the history of multiphase catalysis, and the study of the construction of Lewis acid-base sites based on oxygen vacancy defects in catalysts is of great significance for the performance enhancement of various molecular activation and catalytic conversion reactions.The surface properties of multiphase catalysts will largely determine the electronic structure of the surface active sites.Therefore, this paper reviews the research on the oxygen vacancy-containing solid catalysts with Lewis acid-base sites, explores the effects of the corresponding active sites on the catalytic efficiency, stability and chemoselectivity of the catalysts, and summarizes their relevant applications in the field of photocatalysis.

Abstract:Nitrogen-doped Co₃O₄ nanosheets (N-Co NS) were synthesized by sacrificial template method.The morphological structure and chemical composition of the obtained materials were characterized by Transmission Electron Microscopy (TEM), Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS).In addition, the catalytic performance of the prepared catalysts was evaluated by catalytically activating peroxymonosulfate (PMS) to degrade bisphenol A (BPA) in water.Compared with Co₃O₄ nanoparticles (Co NP) and Co₃O₄ nanosheets (Co NS), N-Co NS exhibits higher catalytic performance according to the experimental results.Under the reaction conditions that the dosage of PMS is 2 mmol·L^-1 and the initial concentration of BPA is 50 mg·L^-1, N-Co NS completely degrades BPA in water within 10 minutes, indicating that the N-doping and two-dimensional nanosheet structure are beneficial to the improvement of catalyst performance.N-Co NS still has high activity in complex water chemical environment proved by the pH and ions effect experiments.Besides, the high oxidative activity hydroxyl radicals and sulfate radicals were produced in the reaction system, which was confirmed by the trapping experiments and Electron Paramagnetic Resonance (EPR) tests.

Abstract:Video coding has effectively addressed the too large data volume of raw videos, however, the achieved compression efficiency comes at the cost of video quality degradation.To improve the visual quality of compressed video, a Detail Recovery Convolutional Neural Network (DRCNN)-based video quality enhancement method is proposed, which consists of a main denoising branch and a detail compensation branch.To effectively extract and eliminate the compression distortions, a Multi-scale Distortion Feature Extraction Block (MDFEB) is added to the main denoising branch, which can pay attention to the distorted areas in the compressed video, and improve the distortion feature learning ability of the proposed DRCNN.Furthermore, to enrich the details in the compressed video, the detail compensation branch adopts a content feature extractor composed of a pre-trained ResNet-50 to provide abundant content features, such as salient objects, shapes, and details, and then involves a Detail Response Block (DRB) to efficiently extract the detailed features from the content features.Extensive experimental results show that the proposed DRCNN achieves the best performance in enhancing the compressed video quality as compared with four representative methods.

Abstract:The chaotic categories of hidden big data features, combined with the ignorance of the public key and key encapsulation of big data ciphertext, result in low extraction accuracy and high redundancy of traditional hidden big data feature extraction methods.Here, a secure extraction approach of hidden features of big data is proposed based on hybrid cryptosystem.First, the big data ciphertext is generated through public key encapsulation and cryptographic key encapsulation mechanisms in hybrid cryptosystem.Second, the hidden big data characteristics are categorized based on symmetric encryption and asymmetric encryption designed according to the content of big data ciphertext, which are then used to construct the phase space of big data hidden features and calculate the correlation dimension between big data, thus realize the secure extraction of hidden big data features.The experimental results show that, compared with traditional methods, the proposed approach has low redundancy, high accuracy of classification rate for big data hidden features up to 95%, and low error of feature extraction, verifying the feasibility and application prospect of the proposed approach.

Abstract:In order to solve the problems of high operating cost and poor service quality of school bus due to the scattered distribution of bus stops in rural areas, multi-objective SBRP (School Bus Routing Problem) models were developed for the mixed-load and non-mixed-load scenarios.In the non-mixed-load scenario, a model of the SBRP was developed to optimize the students' travel cost and school operating cost, while in the mixed-load scenario, another model of the SBRP was developed to consider the input cost and operation cost of the school bus.Several heuristic algorithms were compared, based on which the simulated annealing algorithm was selected to solve the models, and the horizontal comparison of the solution results based on genetic algorithm were determined.Tests were conducted on an international bench mark case and the constructed models were solved by introducing different search operators into the simulated annealing algorithm, then the proposed approach was applied to the optimal design of school bus routes in Wulian county, Rizhao, Shandong province.The results showed that in the non-mixed-load scenario, compared with the original school bus operation mode, the school bus input, mileage and travel cost were reduced by 28.6%, 37.8% and 35.6%, respectively, and students' travel cost was reduced by 4.3% considering the students' perception of school bus service.While in the mixed-load scenario, the proposed approach reduced the school bus input, mileage and travel cost by 37.5%, 42.0% and 35.8%, respectively;due to the complexity of the mixed-load scenario, it is difficult to take the travel cost into account, thus the students' travel cost was increased by 0.5%.The proposed SBRP models were verified to be effective and the simulated annealing approach can optimize service quality and reduce operation cost of rural school bus to a greater extent than the genetic algorithm.

Abstract:In view of the different importance of input load characteristics to the decomposition results and the high decomposition error caused by the limited time dependence of LSTM in capturing long-term power consumption information, a non-intrusive load decomposition model based on multi-attention mechanism integration is proposed.First, the probsparse self-attention mechanism is used to optimize the load characteristics extracted by one-dimensional dilated convolution.Then, the temporal pattern attention mechanism is used to give weight to the hidden state of LSTM, so as to enhance the learning ability of the network on the time dependence of long-term power consumption information.Finally, the validity of the proposed decomposition model is verified using the publicly available dataset UKDALE and REDD.Experimental results show that, compared with other decomposition algorithms, the proposed decomposition model based on multi-attention mechanism integration not only has the ability to select important load features, but also can correctly establish the time-dependent relationship between features and effectively reduce the decomposition error.

Abstract:Unmanned Surface Vehicles (USVs) usually operate in coordinated formation and exchange data through wireless ad hoc networks due to mission requirements, and the channel transmission loss of maritime ad hoc networks is usually in a dynamic state owing to the influence of ocean waves.However, the existing backoff algorithm of MAC protocol in ad hoc networks cannot distinguish between packet collision and packet loss in a dynamic maritime environment, resulting in the decline of reliability and stability.Here, we propose an adaptive minimum contention window backoff algorithm based on channel monitoring.The algorithm estimates the channel state by sensing the number of adjacent contention nodes, reduces the channel collision probability and retransmission times, thus improves the reliability and stability of the network as a whole.Simulation results show that compared with classical BEB algorithm, the proposed backoff algorithm increases the throughput and fairness by 28.67% and 62.00%, respectively, and reduces the end-to-end delay and packet loss rate by 2.84% and 15.10%, respectively.

Abstract:The performance of traditional Acoustic Echo Cancellation (AEC) is restricted due to the double-talk detector it used to determine the double-talk and single-talk scenarios.While Blind Source Separation (BSS) signal model is a full duplex model with both far-end and near-end signals, thus the BSS-based AEC does not need the double-talk detector.This paper adopts Auxiliary function based Independent Component Analysis (Aux-ICA) algorithm to realize acoustic echo cancellation in frequency domain, in which the object function is minimizing the mutual information, and the auxiliary function technique is used for optimization.Simulation results show that this method has lower computational complexity and better performance in acoustic echo cancellation under continuous double-talk scenarios.

Abstract:Day-ahead load forecasting is an important task for the power dispatching center to formulate reasonable dispatching plans thus to ensure the safety and reliability of power system operation.However, random errors exist in time series of power loads, and the intelligent algorithm based prediction models are complex in structure and incapable of fully extracting load information enough for load calculation and load forecasting.Here, we propose a day-ahead power load forecasting approach based on Repeated Wavelet Transform-Support Vector Machine (RWT-SVM) by using the historical power load time series of distributed power grids.The approach uses wavelet transform to decompose the power load time series of distributed power grids into multiple subsequences, then applies the Mean Absolute Error (MAE) to calculate the prediction errors contributed by each subsequence, and further decomposes the sequence with the largest MAE to improve the prediction ability of the model.The simulation results show that the proposed RWT-SVM approach outperforms other methods in forecasting accuracy.

Abstract:A structure of stator tooth jacking auxiliary slot and rotor outer circle eccentricity was proposed for Permanent Magnet Synchronous Motor (PMSM) to suppress the cogging torque and reduce the vibration and noise of motor thus improve the output performance of PMSM.The analytical formula of distribution function was established for the stator tooth top (before and after slotting) and the main air gap, thus clarified the relationships between the stator slotting and the main magnetic field distribution as well as the cogging torque.At the same time, the influence of the rotor outer circle eccentricity on the cogging torque was analyzed.The optimal parameter matching was obtained through optimization tests and comparative analysis of auxiliary slot parameters including slot number, width, depth, slot spacing and rotor outer circle eccentricity, via finite element method on a built-in 3-phase 8-pole 48-slot V-type PMSM.The results showed that the double rectangular symmetrical slotting on the stator tooth top and the eccentricity of the rotor outer circle can effectively improve the magnetic field distribution of the main air gap and restrain the cogging torque of the motor.The harmonic amplitude of the optimized air gap magnetic density 5, 11, 15 and 17 was decreased significantly, the peak value of the cogging torque of the motor was decreased by 59.6%, and the sine of the back EMF waveform as well as the average torque was increased, thus the motor's output performance was significantly improved.

Abstract:In view that traditional manual feature extraction method cannot effectively extract the overall deep image information, a new method of scene classification based on deep learning feature fusion is proposed for remote sensing images.First, the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) are used to extract the shallow information of texture features with relevant spatial characteristics and local texture features as well;second, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional fully connected layer is added as feature output while the last fully connected layer is removed;and the two features are adaptively integrated, then the remote sensing images are classified and identified by the Grid Search optimized Support Vector Machine (GS-SVM).The experimental results on 21 types of target data of the public dataset UC Merced and 7 types of target data of RSSCN7 produced average accuracy rates of 94.77% and 93.79%, respectively, showing that the proposed method can effectively improve the classification accuracy of remote sensing image scenes.

Abstract:As one of the important factors affecting soil erosion, rainfall erosivity (R) is related to rainfall characteristics of amount, duration, intensity, and kinetic energy.Using the daily rainfall data of 25 meteorological stations in Jiangxi province from 1961 to 2019, we analyzed the spatial distribution and variation trends of rainfall erosivity in Jiangxi province via rainfall erosivity model, as well as Mann-Kendall correlation test, wavelet analysis and Kriging spatial interpolation.The results showed that the annual average rainfall and rainfall erosivity varied similarly in spatial distribution, which were gradually increased from southern Jiangxi to northern Jiangxi;the rainfall erosivity in spring and summer ranged from 3 000 to 6 000 MJ·mm·hm^-2·h^-1·a^-1, which was significantly higher than that in autumn and winter, with the maximum rainfall erosivity occurring in spring;the average annual rainfall erosivity in Jiangxi decreased from north to south, which maximized in northern Jiangxi and followed by central Jiangxi then southern Jiangxi.The research shows that the rainfall-induced soil erosion was increasing in Jiangxi province, especially in spring and summer, and there were obvious differences in their spatial distribution.

Abstract:The development process and characteristic analysis of precipitation cloud system is an important issue in the field of cloud precipitation physics.Here, the 700 hPa Cloud Water Content (CWC) and the 1h value of airflow velocity (omega, OMG) in the vertical direction of the atmosphere are used to measure the chaos degree of CWC distribution via the information entropy and judge the cloud development via OMG time series, hence a combined prediction model is proposed based on hybrid multi-scale decomposition, Holt model, Autoregressive Integrated Moving Average model (ARIMA) and Lagrange Multiplier.The results show that, the CWC entropy has nonlinear and non-stationary characteristics;the clouds over the north have smaller means of the CWC entropy sequence and larger variance compared with those over the south regardless of the cloud development stage;a good temporal corresponding relationship is found between the regional average OMG and the extreme point reconstructed by the wavelet low-frequency of the CWC entropy, and close extreme value points account for 50% in clouds over the south and 83.3% in clouds over the north, showing that CWC entropy can somehow reflect the cloud development;the multiple timescale features of CWC entropy sequences make the multi-scale decomposed Holt-ARIMA-Lagrange Multiplier model more accurate than the single prediction method and single-layer decomposed prediction model, with accuracy improvement of more than 3%.