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In the era of mobile internet, it is more and more convenient to access professional knowledge and information, meanwhile, academic misconducts such as plagiarism and fraud are receiving increasing attention. With the emergence of digital currencies such as Bitcoin, the underlying blockchain technology also enters into people’s sights.Based on the decentralization, trustworthiness and traceability of blockchain technology, this paper proposes a blockchain-based academic paper review and traceability system.While eliminating the high cost and security problems brought by the centralized system,the proposed system retrieves,records,and stores the academic papers with their reviewing and publishing processes, thus make die papers safe and traceable. [ABSTRACT FROM AUTHOR]

Sonar images are prone to problems such as low contrast, low resolution, and edge distortion, so it is difficult to accurately separate effective signals from noise when removing noise from sonar images, resulting in reduced image contrast, unclear edge contours, and severe detail loss after denoising. Therefore, this paper proposes a sonar image denoising algorithm based on adaptive Wiener filtering and 2D-VMD (Two Dimensional Variational Mode Decomposition). First, a noisy image is decomposed using 2D-VMD to obtain a series of sub modes with different center frequencies. Effective modal components are obtained via correlation coefficients and structural similarity, then processed by adaptive Wiener filtering, and finally the filtered modal components are reconstructed to remove noise. The experimental results show that the proposed image denoising algorithm achieves the best results in terms of correlation coefficient and structural similarity, with a peak signal-to-noise ratio slightly lower than that of NSST domain denoising. Taking into account objective data and visual effects, the algorithm proposed in this paper achieves the best performance in image details and edge preservation after removing noise. [ABSTRACT FROM AUTHOR]

In this paper, we prepare m-Bi2 O4 via a simple hydrothermal method, and analyze its structure, morphology, surface valence and piezoelectric photocatalytic performance using characterization methods such as XRD, XPS, SEM, TEM, UV-vis DRS and PFM. Furthermore, we test the piezoelectric photocatalytic activity of the material using sulfamethazine (SM) as a simulated pollutant. The results show that m-Bi2 O4 outperforms BaTiO3 and BiOCl in catalytic performance, indicated by its high SM degradation efficiency (96.46%) after 60 min of synergistic piezoelectric-light action, and m-Bi2 O4 still holds high catalytic activity under weakening light energy. In addition, superoxide radicals with high oxidation activity and a small amount of hydroxyl radicals and singlet oxygen have been captured in the reaction system. We also propose a possible mechanism of piezoelectric photocatalysis. [ABSTRACT FROM AUTHOR]

Federated learning is an important method to address two critical challenges in machine learning: data sharing and privacy protection. However, federated learning itself faces challenges related to data heterogeneity and model heterogeneity. Existing researches often focus on addressing one of these issues while overlook the correlation between them. To address this, this paper introduces a framework named PFKD (Personalized Federated learning based on Knowledge Distillation).This framework utilizes knowledge distillation techniques to address model heterogeneity and personalized algorithms to tackle data heterogeneity, thereby achieving more personalized federated learning. Experimental analysis validates the effectiveness of the proposed framework. The experimental results demonstrate that the framework can overcome model performance bottlenecks and improve model accuracy by approximately one percentage point. Furthermore, with appropriate hyperparameter adjustment, the framework's performance is further enhanced. [ABSTRACT FROM AUTHOR]

In the islanded microgrid, the mismatch in line impedance hinders the traditional droop control from achieving an equal distribution of reactive power among Distributed Generation (DG) sources. To address this problem and enhance the flexibility and reliability of the controller, this paper analyzes the reasons behind the failure of traditional droop control and proposes an adaptive droop coefficient that can be dynamically adjusted to meet the conditions for reactive power sharing. Additionally, a dynamic distributed observer is designed and its convergence is proven, which allows DG sources to obtain necessary information flexibly and reliably in a distributed manner. Finally, the proposed control strategy is verified through simulations in four different scenarios, and the results demonstrate its superiority and effectiveness. [ABSTRACT FROM AUTHOR]

Computational offloading is an essential technology in Mobile Edge Computing (MEC). To address the shortage of computational offloading strategies in multi-user and multi-MEC server scenarios,this paper proposes a hybrid artificial bee colony approach (Artificial Reverse Sine-Cosine, ARSC). First, the opposition-based learning strategy is used to initialize the population and optimize the initial solution of the population. Then the global optimal bootstrap information of the sine-cosine algorithm is exploited to improve the local search capability in the employed bee stage. Finally, to balance the global and local search capability of the approach, the step size factor is adapted by introducing dynamic perception. Simulation results show that the proposed ARSC approach outperforms offloading strategies based on particle swarm algorithm and artificial bee colony algorithm in convergence, latency, and energy consumption. [ABSTRACT FROM AUTHOR]

RGB-Infrared person re-identification (Re-ID) is a challenging task which aims to match person images between visible and infrared modalities, playing a crucial role in criminal investigation and intelligent video surveillance. To address the weak feature extraction capability for fine-grained features in current cross-modal person Re-ID tasks, this paper proposes a person re-identification model based on fused attention and feature enhancement. First, automatic data augmentation techniques are employed to mitigate the differences in perspectives and scales among different cameras, and a cross-attention multi-scale Vision Transformer is proposed to generate more discriminative feature representations by processing multi-scale features. Then the channel attention and spatial attention mechanisms are introduced to learn information important for distinguishing features when fusing visible and infrared image features. Finally, a loss function is designed, which adopts the adaptive weight based hard triplet loss, to enhance the correlation between each sample and improve the capability of identifying different persons from visible and infrared images. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets show that the proposed approach achieves mAP of 68.05% and 85.19%, respectively, outperforming many state-of-the-art approaches. Moreover, ablation experiments and comparative analysis validate the superiority and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

There are quite a few comment sentences without emotional words in aspect-level emotional texts, and the study of their emotions is called aspect-level implicit sentiment analysis. The existing models have the problems that the context information related to aspect words may be lost in the pre-training process, and the deep features in the context cannot be accurately extracted. Aiming at the first problem, this paper constructs an aspect-aware BERT pre-training model, and introduces aspect words into the input embedding structure of basic BERT to generate word vectors related to aspect words. Aiming at the second problem, this paper constructs a context-aware attention mechanism. For the deep hidden vectors obtained from the coding layer, the semantic and syntactic information is introduced into the attention weight calculation process, so that the attention mechanism can more accurately assign weights to the context related to aspect words. The results of comparative experiments show that the proposed model outperforms the baseline model. BERT [ABSTRACT FROM AUTHOR]

To address the low prediction accuracy in agricultural commodity futures due to their nonlinear and non-smooth features resulting from various influencing factors, this paper proposes a decomposition and ensemble forecasting approach based on CEEMDAN and Transformer-Encoder-TCN. First, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to decompose the time series into multiscale Intrinsic Mode Function (IMF) and residuals, reducing the complexity of series modeling. Second, each subseries is predicted via Temporal Convolutional Network (TCN) incorporating multi-stage self-attention unit (Transformer-Encoder),which optimizes the modeling weights of significant features. Finally, the prediction results of each subseries are linearly summed and integrated to obtain the final prediction results. The soybean futures revenue index in the agricultural commodity index of South China Futures Company is used as the research object. The model is retrained by time-series cross-validation and parameter transfer. The ablation and comparison experimental results show that the proposed model has superiority in RMSE,MAE and DS, verifying its effectiveness in predicting agricultural commodity futures. [ABSTRACT FROM AUTHOR]

To address the low segmentation accuracy of multifidus muscle lesion sites in MRI images of patients with lumbar disc herniation, this paper proposes a new model to improve the U2-Net network with the goal that the encoding and decoding subnetworks are interconnected by a series of nested jump paths. To reduce the semantic missing of feature maps in the encoding and decoding subnetworks, the jump connections in the middle of RSU-7,RSU-6,RSU-5,and RSU-4 in the U2-Net model are redesigned, while the RSU-4F part remains unchanged. In addition, the channel attention module is added to enable the net to focus on channels of higher contribution to task, thus extract high quality multifractal muscle features. The experiments on the multifidus muscle MRI image dataset show that the improved U2-Net outperforms U-Net,U2-Net and U-Net++ network in indicators of Dice, HD and MIoU. It can be concluded that the proposed approach has good performance on MRI image segmentation of multifidus muscle, which can assist doctors to make diagnosis. [ABSTRACT FROM AUTHOR]

In the past few years, robots have become an important means of substation inspection, and robotic inspection technology for non-fixed lines has received increasing attention in order to perform inspection tasks more flexibly. How to achieve high-precision positioning in complex substation environment is one of the core problems to be solved. It is difficult for a single sensor to meet the requirements of reliable positioning in substations, therefore, this paper designs a multi-sensor fusion LINS-GNSS positioning method. Its front-end tightly couples LiDAR and inertial navigation based on an iterative error-state Kalman filter framework, which recursively corrects the estimated state by generating new feature correspondences in each iteration. The back-end uses a factor graph optimization approach to loosely couple the localization results from the satellite navigation with the localization results output from the LINS back-end. The optimization process first aligns the local coordinate system with the global coordinate system, then adds the position constraints of the GNSS as a priori edge to the factor graph in the back-end, and finally outputs the positioning results in the global coordinate system. In order to evaluate the performance of the LINS-GNSS system in the substation environment, this paper conducted field tests under real scenarios. The experimental results show that the LINS-GNSS system can achieve a positioning accuracy better than 0.5 m in the substation environment, better than LIO-SAM. [ABSTRACT FROM AUTHOR]

Aiming at the distributed convex optimization problem in multi-agent systems, a zero-gradient-sum optimization algorithm based on adaptive event-triggered mechanism is proposed in this paper. The adaptive event-triggered condition is designed based on virtual clock, and the condition will not be triggered until the virtual clock of each agent meets the condition, which effectively reduces the update times of the controller as well as the communication burden of the system. Through constructing the Lyapunov function, it is proved that the states of all agents can converge asymptotically to the global optimal solution under the algorithm. In addition, the designed event-triggered condition makes the minimum inter-event time designable, which effectively excludes the Zeno behavior. Finally, simulation results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper uses the dual-frequency GNSS observation data of the France's BRST port and the UK's Severn Bridge monitoring system to perform GPS-IR water level inversion in static and high dynamic environments, respectively, to explore the feasibility and accuracy of traditional GNSS monitoring system for water level inversion. The results show that the inversion accuracy of the L1 band is higher than that of the L2 band; in the static scene, the correlation coefficient between the GPS-IR water level inversion results and the tide gauge data is greater than 0. 98; in the high dynamic scene, the retrieval accuracy of F001 station is slightly lower. Then the Empirical Mode Decomposition (EMD) is used to improve the accuracy of GPS-IR water level retrieval results in complex bridge environment, which can reduce the root mean square error by about 50%. The EMD-improved GPS-IR water level retrieval approach improves the applicability of GPS-IR technology in different water environments, and has a good application prospect in water level monitoring. [ABSTRACT FROM AUTHOR]

To address the problem of long time-consuming and large memory consumption of A*algorithm in solving path trajectory, this paper proposes an improved A* algorithm based on adaptive step size. First, the priority order of the search direction was set according to the position relationship between the current point and the end point, with the purpose to reduce the redundant planning calculation on unreasonable directions. Second, the judgment condition for reaching the end point was modified to achieve path jumping during path planning. Then an adaptive step size strategy was proposed to improve the efficiency of A* algorithm in path planning. Finally, an eight-directional search approach was proposed to address the issues of large memory usage and possible memory overflow when facing large maps. Experimental results show that compared with original A* algorithm, the improved A* algorithm greatly improves the efficiency of path planning, and solves the problem of large memory usage. [ABSTRACT FROM AUTHOR]

A three-stage rectifier containing three diodes is proposed in this paper. These diodes with different threshold voltages are located in three branches to achieve high conversion efficiency for different input power levels. By combining the three branches, the proposed rectifier is enabled to achieve high conversion efficiency over a wide dynamic input power range. A prototype of a 2.4 GHz rectifier is designed, simulated and measured. The efficiency of the three branches remains above 30% in the power range of-10-11 dBm, 11-24 dBm and 24-35 dBm, respectively. The measured results show that the conversion efficiency maintains over 40% within the input power range of -6-25 dBm, with the maximum conversion efficiency reaching up to 71. 2%. It indicates that the proposed rectifier can achieve low sensitivity to input power variation and high conversion efficiency over an ultra-wide input power range, which can be used in energy harvesting or dynamic wireless power transmission systems. [ABSTRACT FROM AUTHOR]

To address the problem of numerous hyperparameters, loss of long time series information and difficulty in distinguishing primary and secondary features in Long Short-Term Memory network (LSTM) for grain yield ca- pacity prediction, this paper proposes a combined data-driven grain capacity forecasting model. In the hyperparameter part, the proposed model performs hyperparameter search optimization for LSTM by introducing Dy- namic Weights and Laplacian variation of Bald Eagle Search Optimization Algorithm (WLBES), to avoid the process of manual parameter adjustment. In the prediction part, the proposed model uses Ridge Regression (RR) to correct the residuals of the prediction results to make up for the deficiency of LSTM data loss, and adds an attention mechanism to distinguish primary and secondary features by weight size to enhance the importance of features with greater relevance to grain production. The results show that the combined WLBES-LSTM-RR model decreases the root mean square error (RMSE) by 75% and 19% compared with the LSTM and WLBES-LSTM models, respective- ly, and substantially decreases the RMSE compared with other combined models of optimized LSTM. This combined model has higher prediction accuracy in grain yield capacity prediction. [ABSTRACT FROM AUTHOR]

The rapid development of network technology, human-computer interaction and artificial intelligence has given birth to the metaverse and further promoted the digital transformation of all aspects of people's life. The concept of metaverse emerged in 2021, and has attracted extensive attention from industry, academia, media and the public. This paper attempts to deeply analyze the metaverse from the perspective of technology and application. First, the concept and connotation of the metaverse are elaborated from the definition, origin & development, characteristics and key technologies (network and computing, Internet of Things, human-computer interaction, electronic game, block chain, digital twin, etc.). Then, the enterprises and application examples involved in the metaverse are dis- cussed. Finally, the existing challenges and opportunities in the development of the metaverse are analyzed, and the future researches and applications are prospected. Through the meta-analysis of the current development status and research trend of the metaverse and the scientific evaluation of the potential application of the metaverse, we can provide useful reference for the researchers of the metaverse. [ABSTRACT FROM AUTHOR]

With the completion of the BDS-3, China has become the third country that has global navigation satellite system. Furthermore, BDS-3 can broadcast observation signals up to five frequencies, which has great significance to achieve rapid fixing of ambiguity and improve positioning accuracy. The Ionosphere Reduced (IR) combination has minimal ionospheric delay and integer characteristics, therefore, a solution model based on IR combination is established for medium and long baseline utilizing the quad-frequency observations of BDS-3 in this paper. In the meanwhile, the tropospheric delay is estimated with tropospheric mapping function. The experimental results show that compared with traditional dual-frequency Ionosphere-Free (IF) model, the ambiguity fixing speed of the IR model is improved by more than 10%, and the positioning accuracy in north (N), east (E) and up (U) directions are improved by 7.7%,7.9% and 8.2%, respectively. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS), Strapdown Inertial Navigation System (SINS) and visual sen- sors can complement each other, and their information fusion can obtain high-precision, drift-free navigation and posi- tioning information. Aiming at the problem that GNSS/SINS/vision fusion navigation is vulnerable to the impact of mo- tion speed, light change, occlusion, etc., which leads to the decline of navigation positioning accuracy and robustness, this paper adds the SoftLone robust kernel function to the cost function of the graph optimization framework, and sets the gross error test procedure of the measured value to reduce the negative impact of outliers. Further, the chi-square test is performed on the calculated residuals of the measured value, and the weight of the over-limit residual is reduced to improve the accuracy and robustness of the system. The experimental results show that the proposed algorithm has higher accuracy and better robustness than traditional algorithm without robust kernel function, outlier elimination strategy and chi-square test, and algorithm with other robust kernel functions. It can greatly improve the positioning ac- curacy and robustness of GNSS/SINS/visual navigation. In large scale scenario, there is no large drift errors, and root mean square error and standard deviation of absolute pose are 0. 735 m and 0. 336 m, respectively. [ABSTRACT FROM AUTHOR]

To deal with the contact force for industrial robots, this paper reviewed the existing traditional control strategies including impedance control, position/force hybrid control and adaptive control, and overviewed intelligent control strategies such as fuzzy control, neural network and optimal control. This paper detailed the basic principles of each robot force control algorithm, then enumerated some application results, and prospected the future development of robot force control. [ABSTRACT FROM AUTHOR]

To reduce the influence of system dynamic coupling and external disturbance on the performance of flight control system of fixed-wing UAV thus improve its flight control accuracy, this paper proposes a singular perturbation model for fixed-wing UAV and then designs a sliding mode control approach based on disturbance observer. The velocities and attitudes of the fixed-wing UAV are modeled based on the dynamics of action. Then the dynamic model is transformed into a singular perturbation one and then decomposed to complete decoupling. Two reduced-order uncoupled subsystems are obtained, which is a fast subsystem with angular velocity as fast variable and a slow subsystem with linear velocity and attitude as slow variables. Then anti-disturbance sliding mode controllers are designed for angular velocity loop, and angle ＆ attitude loop. Finally, the feasibility and effectiveness of the sliding mode control approach based on fast and slow decomposition are verified by Simulink simulation. [ABSTRACT FROM AUTHOR]

This paper studies the stabilization problem of stochastic singular Markov jump systems with time-varying delays by static event-triggered control. By constructing the Lyapunov-Krasovskii functional, Jensen’s inequality and free weighting matrix technology, the stochastic admissibility conditions for the systems under the condition of static event-triggered control are proposed. On this basis, the state feedback controller is designed so that the corresponding closed-loop systems satisfy regular, impulse-free and the stochastically admissible in the mean square. Finally, a numerical simulation example is provided to illustrate the correctness and effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes the adaptive coupling coordination degree and adaptive coordination interval for the population and economic system, then employs the RBF network learning algorithm to realize the warning strategy and the analysis of influencing factors for the coordinated development between population and economic system for Ningxia’s Yinchuan and Shizuishan. First, the adaptive coupling coordination degree as well as the adaptive coordination interval is introduced to formulate an adaptive warning strategy for the development relationship between population and economic system. Second, a three-layer network learning model is employed to determine and analyze the key factors affecting the coupling and coordinated development between population and economic system. Finally, the approach proposed in this paper is applied to Yinchuan and Shizuishan, based on which key factors are determined and interpreted for the coupling and coordinated development between population and economic system in these two cities. [ABSTRACT FROM AUTHOR]

Here, the Response Surface Method （ RSM） is used to optimize the structure of a new Axial-Flux Permanent Magnet Memory Motor （AFPMMM） proposed in this paper. Main influencing parameters of the motor are preliminarily determined based on analysis of the electromagnetic performance equation and Orthogonal Experiment （OE), among which, three factors including soft magnetic ratio, number of rotor poles and air gap length, are selected as the design factors of OE. Then Electromotive Force （EMF), Total Harmonic Distortion （THD） and cogging torque are determined as the optimization factors, and the RSM is used as the optimization method. The Response Surface （RS） experiment is established by using the finite element software An soft Maxwell and the RS design software Design Expert. The simulation parameters and fitting curves of the motor under the combination model of different design factors are obtained. Different optimization methods are combined to meet the specific design requirements in this paper. Theoretical analysis and experimental results verify the feasibility and effectiveness of the proposed motor and optimization method. Then the optimization scheme is obtained through comparative analysis of the test data. The results show that the optimized motor not only reduces the cost of permanent magnet material, but also ensures good EMF and small axial force fluctuation. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed in extreme weather can provide important guidance for distribution network to enhance disaster prevention and resilience. This paper proposes a method based on Temporal Convolutional Network (TCN), Bi-directional Long Short-Term Memory(BiLSTM)and error correction for wind speed prediction in extreme weather. First, the time series characteristics of multi-feature weather data are extracted by TCN, and then input into BiLSTM for wind speed prediction. To further improve the prediction accuracy, Variational Mode Decomposition (VMD) is introduced to decompose the error sequence, and BiLSTM models are constructed to perform error prediction for the decomposed error subsequences respectively. Then the error prediction value is used to correct the wind speed prediction value. Finally, simulations are carried out for a place of Henan province, and the results show that compared with measured weather data, the proposed method can effectively predict wind speed with high accuracy when extreme weather occurs. [ABSTRACT FROM AUTHOR]

The severe haze pollution has become one of the major obstacles to the ecological integration development of the Yangtze River Delta (YRD). This paper constructs a haze control efficiency evaluation system taking labor, capital and technological innovation as input indicators, and the number of days with air quality at or better than level 2, PM2. 5 concentration and AQI as output indicators, and uses a three-stage DEA to measure the haze control efficiency of 27 cities in the YRD from 2014 to 2019. The results show that the three-stage DEA model can effectively remove the influence of environmental factors and random errors, and can truly reflect the level of haze control efficiency in the YRD. The change of return to scale is significantly influenced by environmental and random factors, and most cities are in the stage of increasing return to scale. The overall haze control efficiency of YRD cities from 2014 to 2019 is high and shows a W-shaped change trend, with significant inter-city differences. The GDP per capita and the share of secondary industry have a positive effect on haze control efficiency, while population size and urbanization rate have a negative effect on haze control efficiency. [ABSTRACT FROM AUTHOR]

In order to reduce the sulfur and olefin and the loss of octane number so as to promote the clean production of gasoline, an octane number loss prediction model is established based on data accumulated by the S Zorb device.First, the Lasso is used to screen out the modeling variables, then the index factor contributions are calculated by the BP neural network, based on which 15 main variables are screened out to build the model.Second, four modeling approaches are compared and analyzed, which shows that the BP neural network has better prediction accuracy thus is more suitable to model the octane number loss.The ten-fold cross-validation produces the average MSE value of 0.027 193 and the average R2 value of 0.904 87, verifying the reliability of the model.Furthermore, the main variables are optimized and adjusted by multiple linear regression under the premise that the sulfur content is not greater than 5 μg/g.The results show that multiple variables need to be adjusted simultaneously to reduce the octane number loss by more than 30%.The multiple linear regression model has good prediction accuracy and can adjust main variables positively or negatively according to a certain proportion.The trajectories of octane number and sulfur content are also visualized in the paper. [ABSTRACT FROM AUTHOR]

The ground temperature changes at a rate of 0.1 ℃ every 10 years, however, a solar radiation error of about 1 K can be produced by conventional radiation shields due to the influence of solar radiation.In order to improve the accuracy of surface temperature measurement and reduce working energy consumption, this paper designs a temperature sensor based on the accelerated diffusion of radiant heat via piezoelectric ceramic vibration.First, the Computational Fluid Dynamics (CFD) method is used to calculate the radiation error of the temperature sensor under multi physical factors, then the data is fitted and analyzed using the neural network algorithm, and finally the field experiment platform is built to place the temperature sensor in real environment to verify the feasibility of the scheme.The experimental results show that the absolute error and root mean square error between the corrected value and reference value of the surface temperature sensor are 0.041 ℃ and 0.055 ℃, respectively, which also verifies the superior correction performance of the neural network algorithm. [ABSTRACT FROM AUTHOR]

To address the low efficiency and high false alarm rate in detection of DDoS (Distributed Denial of Service) flood attacks, this paper proposes a DWT (Discrete Wavelet Transform) and AKD (Adaptive Knowledge Distillation) self-encoder neural network based approach to detect DDoS attacks.The approach uses the DWT to extract frequency features, the auto-encoder neural network to encode and classify the features, and the AKD to compress the model in order to achieve efficient detection of DDoS attacks.The results show that the approach has high detection efficiency for proxy server attacks, database vulnerabilities & TCP flood attacks, and UDP flood attacks, with low false alarm rate. [ABSTRACT FROM AUTHOR]

Deep space exploration aims at the exploration of planets, moons, asteroids, comets and space environment in the universe other than the Earth. It is an important task to properly select scientific instruments for deep space exploration, which enable human to figure out the physical and chemical environment, topography, dynamic characteristics and the existence of organic matter and water. It is of great significance for mankind to explore the existence of extraterrestrial water and life, as well as the origin and evolution of the solar system. This paper summarizes the scientific objectives of the deep space exploration, then introduces the scientific instruments and their characteristics for typical deep space exploration missions in recent 50 years, including mass spectrometer, spectrometer, ground penetrating radar and magnetometer, and sorts out their performance parameters, the exploration oriented missions and tasks of the instruments. Finally, this paper discusses the development trend of scientific instruments for deep space exploration, such as miniaturization, low power consumption, stronger environmental adaptability and the combination of multiple instruments. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

With the development of artificial intelligence and communication technology, it has become a key issue to release the communication resources and extand the service life of broadband and handware. Different from traditional time-based control, the event-triggered control takes samples only when the event trigger condition is satisfied to update the control policy .This paper first analyzes the event-triggered control in both continuous-time and discrete-time systems from aspect of event triggering communication, and discusses the main ideas of self-triggered control, edge-based event-triggered control, dynamic event-triggered control, and asynchronous event-triggered control. The application of event-triggered control is discussed in terms of consistency, optimization, and game .Two common theoretical analysis methods are given for stability demonstration. Besides, two suitable methods are also presented to exclude the Zeno behavior. At last, the paper summarizes the shortcomings of the current event-triggered work and puts forward some directions on future development. [ABSTRACT FROM AUTHOR]

In this paper an impedance composite control method based on disturbance observer is proposed for manipulator system with unknown disturbances. For tracking the second order impedance dynamic model a composite control strategy is designed,which includes the disturbance observer (DOB),the impedance controller, and the position controller to estimate unknown disturbances, correct input angle, and track the adjusted angle, respectively. This composite control strategy ensures that the impedance error converges to a small neighborhood, thus realizes the desired dynamic tracking of the second order impedance model. The effectiveness of the proposed control method is proved by simulation examples. [ABSTRACT FROM AUTHOR]

The existing railway lines need to be updated with the ageing of railways and the demand for capacity increase, thus the surveying of railway lines is necessary without affecting its routine operations. However, the surveying of most of the existing railway lines still relies on traditional manual methods, which are characterized by low efficiency, heavy task, complex procedures, and repeated online. To address these priblems, this paper uses a least squares based 2D template point cloud matching approach to jointly and repeatedly calculate the rail track surface centre point position and then obtain the track centre line, track gauge and other related parameters based on absolute 3D point cloud data obtained by the track mobile laser scanning system. The test and result analysis at the track test site show that the proposed approach can effectively extract the track centre line and provide accurate centre line data for the existing line survey. [ABSTRACT FROM AUTHOR]

In this paper, we consider the transmitter beamformer design at relays for a full-duplex two-way relay system, where the two sources are equipped with multiple antennas, and each one of the nearby relays is equipped with two antennas with one for transmission and the other for reception. Under the constraints of the limited transmit power and zero forcing nulling technique used at the relays, we propose two linear relay beamformer designs that are respectively to iteratively minimize the weighted mean square error of the two source nodes and maximize the signal to noise ratio of the smaller one of the two source nodes. The effectiveness and efficiency of the proposed designs are evaluated by numerical experiments in terms of the weighted sum rate and the CPU running time. [ABSTRACT FROM AUTHOR]

Snow cover is one of the important parameters in the study of hydrometeorology. At present, the most widely used Snow Cover Area（SCA） can be obtained by Moderate-resolution Imaging Spectroradiometer（MODIS）,which is often used in the study of temporal and spatial changes of snow cover. However, large area snow data missing existed in MODIS snow cover products due to the cloud occlusion. To address this, we take the Kaidu River basin as the research region, and combine the snow product data retrieved from MODIS carried on the Terra and Aqua satellites with the topographic feature data, then use a denoising autoencoder artificial neural network and the extreme snow line method to quantitatively complement the snow data loss caused by cloud occlusion in complex alpine terrain. The denoising autoencoder artificial neural network combines multi-feature data to establish a nonlinear mapping relationship between topographic features and snow grain size, which is then used to complement the missing snow grain size data. The extreme snow line method is used to remove the false report value in low altitude area and obtain the snow cover image with high precision. In contrast verification, the accuracy of the proposed cloud removal method is over 86%,which effectively improves the snow cover detection. Therefore, the approach proposed in this paper can effectively remove cloud occlusion from snow products in complex terrain areas. [ABSTRACT FROM AUTHOR]

The popular Long Short-Term Memory（LSTM） based precipitation prediction models suffer from overfitting and time lag. Broad Learning System（BLS）,which does not require multiple iterations, helps to solve the above disadvantages of LSTM. Weighted Broad Learning System（WBLS） reduces the impact of noise and outliers on precipitation prediction accuracy by introducing a weighted penalty factor constraint to assign sample weights in the BLS. Thus a LSTM-WBLS daily precipitation prediction model is proposed in this paper. The daily precipitation at Badong station in Hubei province is selected for empirical study. And the influence of air pressure, temperature, humidity, wind speed and sunshine on precipitation is considered. The experimental results demonstrate that the LSTM-BLS model has significantly improved the prediction accuracy in the evaluation indexes of RMSE,MAE and R~2 compared with existing prediction models. The prediction accuracy of the new model outperforms existing models at different time steps, proving its stability. In particular, the direct calculation of weights by WBLS does not make any reduction in operational efficiency of LSTM-WBLS. [ABSTRACT FROM AUTHOR]

To explore the impact of climate change on the lake area of Selin Co, this paper used maximum likelihood method to extract the lake areas of Selin Co for the past 33 years (1988-2020) from Landsat data, then analyzed the variations in lake area, temperature, precipitation as well as snow cover depth in Selin Co basin using linear regression and Mann-Kendall test, and discussed the correlation between lake area and climate change by Pearson correlation. The results showed that, in the past 33 years, the Selin Co Lake expanded by 650. 70 km² at the rate of 203. 34 km²/ (10 a),mostly at northward and southward directions. The average annual temperature and precipitation increased significantly at the rates of 0. 50℃/ (10 a) and 17. 32 mm/ (10 a) (p<0. 05),while the average maximum snow cover depth decreased significantly at the rate of 0. 65 cm/ (10 a) (p<0. 05) during 1988-2020. An extremely significant correlation was found between the change of lake area and the rise of air temperature in the basin as well as the decreasing maximum snow cover depth in cold season (p<0. 001),indicating that the Selin Co lake’s expansion in the past 33 years was a consequence of the increasing water supply from ice-snow meltwater due to the rising air temperature in Selin Co basin. [ABSTRACT FROM AUTHOR]

This paper proposed a Marching Trapezoidal Polyhedrons 3D modeling algorithm (MTPD) based on the cone-shaped spatial distribution of Doppler weather radar base data. In this algorithm, a trapezoidal polyhedron was introduced to replace the cube in the conventional modeling algorithm as a basic volume element for modelling. On the other hand, the hexahedral index or tetrahedral index was selected as the construction model for the 3D iso-surface based on the difference in spatial range for 3D radar modeling, to balance the efficiency of the algorithm and the precision of the modeling results. Based on this algorithm, a Doppler weather radar 3D visualization platform was developed using WebGL technology. The results revealed that the algorithm significantly improved efficiency without compromising the precision of 3D modeling when compared with the conventional modeling algorithm based on radar grid data. The durations for the algorithms were reduced by 1. 9 seconds and 0. 7 seconds, respectively under the hexahedral index mode and the tetrahedral index mode, while under the tetrahedral index mode, the 3D echo structure was more continuous with a higher level of precision. The Doppler weather 3D radar visualization platform based on the B/S architecture could provide a cross-platform 3D radar display, thus visualize the 3D structure of convective cloud effectively. [ABSTRACT FROM AUTHOR]

Global climate change caused by excessive greenhouse gas（GHG） emissions has been widely concerned. Agricultural activities are the second largest source of GHGs emissions, so it is urgent to reduce agricultural GHGs emissions. Biochar, which has stable properties, abundant aromatic carbon and pores, is produced by pyrolysis of biomass under high temperature and limited oxygen conditions. The effect of biochar amendment on GHGs mitigation and soil carbon sequestration is excellent, and biochar application has the potential to participate in China’s ongoing carbon trading of voluntary emission reduction（VER）. However, the factors affecting the carbon sequestration and GHGs emission reduction effect of biochar are complicated, so it is necessary to systematically summarize the mitigation effect, influencing factors and research progress of biochar. This paper reviewed researches on the GHGs emission reduction and carbon sequestration effect of biochar through pot and field experiment as well as meta-analysis research. At the same time, CiteSpace software was used for visual analysis to explore the research hotspots and development trends in this field. The opportunities and challenges faced by biochar application projects participating in carbon trading were summarized based on the characteristics of domestic and foreign carbon trading market development and corresponding supporting policies. Corresponding solutions were also provided in this study, which offered scientific guidance and useful reference for the development of carbon sequestration and GHGs emission reduction research of biochar and the successful participation of biochar application projects in carbon trading. [ABSTRACT FROM AUTHOR]

In view of the problems that aspect level sentiment analysis tasks cannot give full consideration to syntactic comprehensiveness and semantic relevance, and the graph volume used in most studies only considers the top-down dissemination of information and ignores the bottom-up aggregation of information, this paper proposes a sentiment analysis model based on attention and dual channel network. While expanding the dependency representation, the model uses self attention to obtain the information matrix with semantic relevance, and uses a dual channel network to combine comprehensive syntactic and semantic relevance information. The dual channel network focuses on the semantic features of top-down propagation and the structural features of bottom-up aggregation respectively. The graph convolution output in the channel will interact with the information matrix, pay attention to complement the residual, and then complete the tasks in the channel through average pooling. Finally, the final sentiment classification features are obtained by the fusion of semantic based and structure based decision-makings. The experimental results show that the accuracy and F1 value of the model are improved on three public data sets. [ABSTRACT FROM AUTHOR]

In view of the increasing concern on model efficiency in computer vision, this paper proposed several optimization schemes to improve the flame detection models in model efficiency as well as the detection performance.A backbone network (FIRE-Net) was constructed from a multi-convolution combined structure, which can efficiently extract rich flame features from multiple scales.Then an improved weighted bidirectional feature pyramid network (BiFPN-mini) was used to quickly achieve multi-scale feature fusion.In addition, a new attention mechanism (FIRE-Attention) was proposed to make the detector more sensitive to flame characteristics.The above optimizations were combined to develop a new flame detector abbreviated as FIRE-DET,which was then trained on self-built dataset and tested on internet videos.The experimental results showed that the FIRE-DET outperformed mainstream algorithms by its flame recognition accuracy of 97% and frame rate of 85 FPS,thus provides a more common solution to solve the flame detection. [ABSTRACT FROM AUTHOR]

Geosynchronous Synthetic Aperture Radar(GEO SAR) has the advantages of short revisit period and wide coverage, and has great potential in disaster prevention and mitigation. In this paper, a distributed GEO SAR system containing both close and sparse formation is proposed. On the basis of monostatic GEO SAR, multiple phase centers are formed by adding companion satellites only receiving signals, which has the advantages of rich tasks and low cost. However, different tasks require different spatial baselines for formation. Aiming at the configuration of close formation, this paper proposes a method of correcting the relative motion equation based on coordinate rotation, through which the analytical expression of spatial baseline under the influence of earth rotation can be obtained;furthermore, a method of linear time-varying baseline design based on semi-major axis correction is proposed, which can be applied to SAR tomography. For sparse formation, the minimum Position Dilution of Precision criterion under the measurable constraints of global scene is proposed, which can realize the design of sparse formation with the best combination of line of sight(LOS) thus can achieve the best three-dimensional(3 D) deformation retrieval performance. Finally, the system performances of the designed distributed GEO SAR formation are simulated and analyzed, including the diurnal variation of baseline, the resolution in altitude direction and the 3 D deformation retrieval accuracy in typical observation areas. [ABSTRACT FROM AUTHOR]

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Aiming at the low positioning accuracy of the GNSS positioning chip of the smart phone in occluded environment, this paper uses the RTK/PDR fusion method to solve the position results. Considering that the GNSS sig-nal is susceptible to environment noise, different expansion coefficients are set to amplify the measurement noise variance matrix R in Kalman fusion filtering to realize the weight reduction of GNSS position information in occluded environment, so as to obtain better fusion results. The experimental test shows that in open environment, the positioning accuracies were identical between RTK and RTK+PDR for both two test mobile phones. While in the slightly occluded environment, compared with the RTK single positioning, the RTK+PDR fusion positioning improved the plane lifting effect of the two test mobile phones by 6.24% and 3.97%,and improved the elevation lifting effect by 20.44% and 32.56%.In the severely occluded environment, the RTK+PDR fusion positioning improved the plane lifting effect of the two test mobile phones by 47.65% and 42.90% compared with the RTK single positioning, and improved the elevation lifting effect by 39.78% and 67.98%.The experimental results show that the RTK + PDR fusion positioning method, which dynamically sets the GNSS information weight according to the average satellite carrier to noise ratio, can partially compensate for the reduction of positioning accuracy caused by environment occlusion for mobile phones using RTK. [ABSTRACT FROM AUTHOR]

A baseline field test equipment which can rotate automatically is introduced in this paper. Comprehensively integrated with technologies of advanced MEMS inertial navigation, ARM embedding,Wi-Fi wireless transmission, high stability motor drive and high-efficiency power management, this equipment can realize automatic measurement of antenna phase center deviation as well as real time north pointing function. The test results show that the rotation error of the turntable is within 0.1° and the north pointing error is within 1°. This research shows that the automatic measurement system of antenna phase center deviation has advantages of high accuracy, high efficiency and high automation,and has solved the problems of low automation level and the lack of automatic north seeking perplexed tradition measurement methods. Furthermore, the system is integrated into a 35 cmx30 cmx30 cm portable suitcase and weighed less than 10 kg, which makes it easy to use and maintain. [ABSTRACT FROM AUTHOR]

In view of the difficulties in data processing and adjustment of coordinate calculation perplexed large-scale GNSS reference station network, this paper proposes a coordinate calculation method. The method is elaborated in four aspects including selection of global framework sites, division of reference station network subnets, single-day data processing of base stations, and global network adjustment calculation. Finally, the internal and external consistence accuracies of the calculation results are evaluated. The analysis results show that the method can solve reference coordinates for network with more than 3 000 stations, and the RMSE of the results is better than 3 mm in both north-south and east-west directions, and better than 6 mm in elevation. The accuracy of the calculation is higher than those of most IGS analysis centers. [ABSTRACT FROM AUTHOR]

With the emergence of a large number of autonomous navigation systems such as robots and unmanned vehicles, positioning and navigation technology has developed rapidly in the past 20 years. All can have reliable positioning and navigation capabilities. Multi-source fusion positioning algorithm is the only effective way to achieve this goal. This paper reviews satellite navigation, inertial navigation, visual sensors, The positioning method of Lidar single sensor analyzes the influence of the environment scene of positioning and navigation operation on multi-source fusion positioning, and the influence of carrier motion behavior on positioning. Finally, from the fusion framework level, the multi-source fusion positioning algorithm is divided into optimization and filtering Two categories are analyzed in depth. [ABSTRACT FROM AUTHOR]

Mobile phone RTK is vulnerable to interferences from complex environment, and the heading angle of inertial navigation data is easily divergent in scenario of mobile phone vehicle positioning. To address these problems, this paper proposes an RTK/DR loose combination positioning method combined with OSM map data. In the process of dead reckoning, based on the OSM data, combined with the information of combined positioning results and vehicle motion state,a geometric matching method from point to line was designed,and the matched road direction was used to correct heading angle. In the loose combination stage, the standard Kalman filter was used to estimate the position and velocity. In order to reduce the influence of large drift or jump of RTK on the results of loose combination, a loose combination switch was set, which can comprehensively judge whether to update the measurement by combining the covariance matrix, the vehicle's moving direction and the change of RTK positioning heading angle in RTK robust Kalman solution. Then six groups of experiment were carried out by using P30 and P40 smart phones. The results show that the introduction of OSM data can effectively solve the accumulation of heading angle error, which makes dead reckoning trajectory more consistent with the benchmark. Compared with mobile RTK, the proposed RTK/DR loose combination with switch improves positioning performance, and effectively suppresses error points of large jumps. [ABSTRACT FROM AUTHOR]