Abstract:Scientific evaluation of urbanization quality is of great significance to measure the level of urban development and promote sustainable urban development.Based on the two dimensions of urban development and urban environmental quality,an evaluation index system of urbanization quality in Liaoning province was constructed,and methods including entropy weight,standard deviation ellipse and geographic detector were comprehensively used to explore the urbanization quality and its spatial distribution,spatial evolution trend and influencing factors in Liaoning province.The results show that,from 2005 to 2019,the overall urbanization quality index increased from 2.74 to 4.74,of which the quality of economic urbanization subsystem had the largest increase,the quality of social urbanization and environmental governance subsystem had an obvious upward trend,and the quality of environmental state and environmental pressure subsystem was relatively stable.For the spatial distribution of urbanization quality,the index ranged from 0.087 to 0.740 for Liaoning's cities,with Shenyang as the "big core" and Dalian as the "small core" of regional agglomeration.The center of gravity ellipse of urbanization quality standard deviation fell on Liaoyang,and the deflection trajectory was along southeast-northwest-southwest from 2005 to 2019,with annual increasing distance and speed of the center of gravity ellipse of standard deviation.Among all the subsystems,economic urbanization,social urbanization and environmental management posed more influence on urbanization quality in Liaoning province;specifically,main influencing indexes included urbanization area,fixed assets investment,number of people participating in primary pension insurance,number of college students per ten thousand people,and capacity of domestic garbage removal and urban sewage treatment,which played growing influence on urbanization quality.

Abstract:The ground-level ozone (O₃) concentration is continuously increasing in middle areas of China during recent years.Volatile organic compounds (VOCs) are important precursors in photochemical production of ozone,which is of great significance for O₃ prevention and control.In order to clarify the characteristics of ambient VOCs and their role in ozone formation in China's middle areas,we conducted online VOCs observation in Changsha from May to October in 2021,monitoring 116 VOC species in total.The mean value of mixing ratios for measured VOCs was (23.09±9.97)×10^-9.Monthly average concentration of VOCs in Changsha showed a "U" shape,with the lowest in July and the highest in October,while the diurnal variation of VOCs concentration showed a bimodal pattern,indicating the influence of human activities.OVOCs were the most abundant component of VOCs,followed by alkane and halo carbon.However,OVOCs and aromatics were the two largest contributors to ozone formation potential (OFP),with a cumulative contribution of 68.3％.Propionaldehyde,acetaldehyde,m／p-xylene,ethylene and toluene were the most important VOCs species to OFP.The observation-based model (OBM) showed that O₃ formation in Changsha was in a transition regime in May,August,September and in a VOCs-limited regime from June to July,while in a NO_x-limited regime in October.For the anthropogenic VOCs,ALDP,OLEP and PARP should be given priority in emission control measures.

Abstract:The widely used word vector representation is incapable of fully representing the specialized texts and phrases in sphere of highly specialized chemical industry,which were quite professional and complex,resulting in the low accuracy of classification.Here,we propose a text classification model incorporating multi-granularity dynamic semantic representation.First,the adversarial perturbation was introduced into the word embedding layer of the model to enhance the ability of dynamic word vectors to represent the semantics.Then the word vector weights were redistributed by a multi-headed attention mechanism to obtain a better textual representation of key semantic information.Finally,text representations of different granularities were extracted through the proposed multi-scale residual shrinkage deep pyramidal convolutional neural network (MSRS-DPCNN) and hybrid attention capsule bidirectional LSTM (HAC-BiLSTM) network model,which were then fused for classification.The experimental results showed that the proposed model achieved an F1-score up to 84.62％ on the chemical domain text dataset when using different word vector representations,an improvement of 0.38-5.58 percentage points compared with existing models.The model also had pretty good generalization performance on the publicly available Chinese dataset THUCNews and the Tan Songbo hotel review dataset ChnSentiCorp.

Abstract:The Convolutional Neural Network (CNN) has unsatisfactory performance in water body extraction from high-resolution optical remote sensing images with complex background,which is low in accuracy,unable to capture multi-scale features,and complex in model structure.Here,we propose an RFA-LinkNet (Receptive Field Attention LinkNet) approach combining Receptive Field Block (RFB) and Channel Attention Block (CAB),from which the high-level water body semantic information and multi-scale feature map can be obtained by RFB,then the CAB is used to realize the weighted fusion of encoding and decoding features,to suppress background features as well as enhance water body semantics.Compared with state-of-the-art CNN models,the proposed RFA-LinkNet can extract water body information from high-resolution optical remote sensing images more efficiently and robustly with high precision.

Abstract: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: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 R² 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: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:As an important component of Autonomous Driving (AD) system,High-Definition Map (HDM) can provide highly accurate prior data of lane lines and road auxiliary facilities for AD system.The reliable evaluation of HDM accuracy is extremely necessary,but has been troubled by the evaluation methods used in mapping.Here,a method based on point set alignment and resampling is proposed to evaluate the relative accuracy of lane lines,and experiments are conducted based on relevant HDM data.First,the points on the verification curve are fitted and sampled,and the aligned point pairs are registered and then resampled,based on which the relative accuracy is calculated.The results showed that the relative limit errors of all the 4 groups of lane lines were verified to be less than 20 cm,meeting the relative accuracy requirements,of which the first group has the relative limit error of 15.9 cm.It can be concluded that the proposed method is more accurate and reliable in accuracy evaluation of HDM than traditional methods.

Abstract:An autonomous navigation system was proposed based on Soft Actor-Critic under the security barrier mechanism to improve the intelligence and security of mobile robot autonomous navigation system.The return function was designed based on distance between the robot and the nearest obstacle,the distance from the target point,and the yaw angle.On the Gazebo simulation platform,a mobile robot with lidar and its surrounding environment were built.Experiments showed that the security barrier mechanism reduced the probability of collision with obstacles to a certain extent,improved the success rate of navigation,and made the SAC-based mobile robot autonomous navigation system have high generalization ability.The system still had the ability of autonomous navigation when changing the origin and destination or even changing the environment from static to dynamic.

Abstract:To address the large error and low efficiency of traditional manual design in cable laying task,the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning.The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning.Furthermore,the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor,thus improves the ACA in both convergence speed and global performance.The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying,which saves the cost of manpower and materials,and improves the design accuracy.

Abstract:The dynamic event triggered mechanism is used to design a distributed optimization algorithm for multi-agent systems.Compared with traditional static triggered control,the dynamic event triggered controller based on Lyapunov function can effectively reduce the communication burden between agents as well as the calculation burden of controllers.In addition,the event triggering condition is designed using periodic sampling information,thus is not required to be checked repeatedly by agents.Moreover,Zeno behavior can be avoided.A numerical simulation is given to verify the effectiveness of the algorithm.

Abstract: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: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:Nowadays spoofing is the dominant threats for GNSS receiver due to its low cost and high efficiency.However,the nulling antenna,which is widely used in GNSS receiver,can only mitigate the interference via high power.According to the DOA information of spoofing,some pseudo interferences are added to the received signal and imping on the antenna array from the same DOAs as the spoofing.Then the following traditional nulling antenna can suppress the spoofing since the beam pattern generate nulls at the DOAs of spoofing.Finally,the simulations of beam pattern and PN code synchronization illustrate the performance of the proposed method.

Abstract:The spatial and temporal distribution of the oceanic boundary layer height is determined by wavelet covariance transform method using COSMIC and ERA-Interim refractive index data independently,then the difference between results of the two datasets is comparatively analyzed.The results show that a roughly same spatial pattern of not completely symmetrical distribution of the boundary layer height along the equator is observed for the inversion results of the two datasets.As for the seasonal and monthly variations,the oceanic boundary layer is relatively high in summer.Though no obvious diurnal variation is observed,it should be noted that the diurnal variation of boundary layer height from ERA-Interim data is more consistent with that of solar radiation.The comparison shows that the inversion results of COSMIC data are about 500-1 000 meters higher than those of ERA-Interim data,and the difference is greater in high latitudes than in low latitudes and smaller in summer than in winter.