Abstract:The main objective of this study is to reveal the variation characteristics of extreme temperature and precipitation events in Lijiang of Yunnan province.The data of daily maximum and minimum temperature and daily precipitation from 1951 to 2017 were obtained from Lijiang Meteorological Station.The variation trends,mutation and period characteristics of 16 extreme temperature indices and 11 extreme precipitation indices have been analyzed with methods of linear regression analysis,Mann-Kendall abrupt change test,moving t test,and Morlet complex wavelet analysis.The results of variation trends are as follows.As for extreme temperature indices,significant increasing trends were observed in TNx,TNn,SU,TN90p,TX90p and WSDI;obvious decreasing trends were observed in FD,TN10p,CSDI and DTR;non-significant increasing trends were observed in TXx,TR and GSL;non-obvious decreasing trends were observed in TXn and TX10p;and the values of ID remained 0 days during the study period.As for extreme precipitation indices,a significant decreasing trend was observed in R1;a significant increasing trend was observed in R99pTOT;non-significant decreasing trends were observed in R10,CWD,PRCPTOT and Rx5day;whereas non-significant increasing trends were observed in R20,R95pTOT,CDD,Rx1day and SDII.The results of mutation test are as follows.Significant mutation years were observed in some extreme temperature and precipitation indices,which usually fall on period from the 1980s to the beginning of the 21st century or period from the 1950s to the 1970s.Furthermore,the extreme cold events indices of FD,TN10p and TX10p showed decreasing trends after the significant mutation years,while the extreme warm events indices of SU,TN90p and TX90p showed increasing trends after the significant mutation years.The values of TR were 1 day in 2015 and 0 days in the remaining years of the study period.The periodic results of wavelet analysis are as follows.The 15 extreme temperature indices (except ID) presented 2-6 quasi-periods ranging at 3-56 years,with 1-3 main periods ranging at 10-56 years.The 11 extreme precipitation indices presented 4-6 quasi-periods ranging from 4-56 years,with 1-3 main periods ranging from 12-56 years.Therefore,there are same or similar main periods for some extreme temperature and precipitation indices.

Abstract:Given that there is very limited knowledge about how urban thermal environment varies diurnally,we first classified Local Climate Zones (LCZs) based on multi-source remote sensing data such as airborne LiDAR and IKONOS-2,and then investigated the responses of LCZs to diurnal Land Surface Temperature (LST).The results suggested that there was significant diurnal variability in LST among LCZs.During the daytime,the warmest and coolest zones were large low-rise buildings (LCZ 8) and water (LCZ G),respectively.At night,bare soil or sand (LCZ F) obtained the lowest LST,while the warmest zone was water (LCZ G).With the increasing of building height,daytime LST related to compact and open built-up types tended to decrease,and an opposite trend was observed at night.LCZs were differentiated better at night than during daytime.Moreover,regardless of day and night,open high-rise built-type (LCZ 4) and LCZ G were the most differentiated zones for built-up and land cover types,respectively.

Abstract:Urban growth and shrinkage are the inevitable results of rapid urbanization.Excessive growth or shrinkage will bring problems such as resource mismatch and ecological environment deterioration.Based on the nighttime light data of China's districts and counties from 1992 to 2019,this paper explores the temporal and spatial dynamic characteristics and underlying causes of urban growth and shrinkage in China,with purpose to provide reference for policy-making on future urbanization,balanced allocation of resources and national development.The results show that urban growth and shrinkage alternate in China with increasingly obvious polarization between growth and shrinkage.Centered in Henan's Nanyang city,the urban growth is mainly a belt structure supplemented by points,in which the belts include the coastal urban belt and the recently formed "Beijing-Zhengzhou-Nanchang" urban belt,and the points refer to inland provincial capitals as agglomeration growth cores.While the urban shrinkage,centered in Shaanxi's Ankang city (shifted from Hebei's Xingtai city),has gradually changed from scattered point shrinkage to belted shrinkage along "Beijing-Xi'an-Chengdu-Kunming".The underlying reasons of urban growth and shrinkage in China include global financial cycle,industrial structure transformation,national policies,etc.

Abstract:To select the interpolation algorithm for the refinement of Precipitable Water Vapor (PWV),this paper systematically analyzes three interpolation methods including the linear interpolation triangulation,the Kriging interpolation and the Inverse Distance Weighting (IDW) interpolation,and then proposes an improved IDW interpolation approach.First,both the influence of GNSS station distance and the distribution characteristics of atmospheric water vapor on the interpolation result is analyzed,which is then used to optimize the interpolation parameters thus make the interpolation result close to the high-precision observation value.Second,this approach is tested using GNSS data of Xuzhou continuously operated reference stations as well as the radiosonde data during the period of May to July 2017.The results demonstrate that the improved IDW interpolation approach outperforms the above three classical interpolation methods in standard deviation,mean absolute error,mean relative error,and Root Mean Square Error (RMSE).Specifically,the RMSE is lowered by 14.88%,15.70% and 4.12%,compared with the linear interpolation triangulation,the Kriging and the IDW interpolation,respectively.Moreover,the proposed interpolation approach has excellent ability in reconstructing the high-resolution atmospheric water vapor distribution map during storms,which can significantly reduce the interpolation error caused by the uneven distribution of sampling sites and the precipitation surge.The comparisons indicate that the improved IDW interpolation approach is conducive to reconstruct the high-resolution atmospheric water vapor distribution map for areas with sparse GNSS station network,thus to improve the capacity of extreme weather monitoring.

Abstract:Based on monitoring data of atmospheric pollutants in Nanjing from Jan.1,2015 to Feb.10,2021,the spatial-temporal distribution characteristics of Nanjing's ambient air quality and the contribution of potential source areas were analyzed.The average concentrations of six air pollutants (CO,NO₂,SO₂,O₃,PM₁₀,and PM_2.5) were 800 μg·m^-3,43.1 μg·m^-3,13.0 μg·m^-3,106.0 μg·m^-3,77.1 μg·m^-3,and 43.0 μg·m^-3,respectively.The average concentration of ozone in Nanjing was higher than that in China's other typical cities (Beijing,Shanghai,Guangzhou,Chengdu,Lanzhou,and Wuhan).The number of pollution days for NO₂,PM₁₀,and PM_2.5 were reduced by 29.1%,38.1%,and 28.1% during 2015 to 2020.However,the frequency of ozone pollution days was increasing (the highest value in summer and the lowest value in winter).The potential source analysis of fine particulate matter in January of 2015-2020 was carried out.It was found that the potential source for Nanjing's PM_2.5 was surrounding industrial areas (Anhui province,north of Jiangsu province,and Shandong province).The concentration of air pollutants in Nanjing in 2020 was lower than that in 2019 and 2021.It indicated that the reduction of human activity caused by COVID-19 pandemic has resulted in less air pollutant emissions and improved air quality in Nanjing.

Abstract:Rigid-flexible robots are primarily composed of rigid components coupling with compliant material.Rigid robots have high rigidity and good controllability,while flexible robots have good toughness.The rigid-flexible coupled robots integrate both advantages,thus are promising in future development.Here,we introduce the coupled robots in aspects of their current development at home and abroad,and elaborate their three actuation modes:pneumatic driving,electro active polymer driving and shape memory alloy driving.We also focus on the modeling processes commonly used in coupled robots,especially the flexible ones,as well as the control algorithms for the coupled robots.Finally,the future development of rigid-flexible coupled robots is prospected.

Abstract:This paper proposes an active control system for urban road traffic,with the purpose to provide a new way to realize the intelligent control of urban traffic and alleviate traffic congestion.The system is composed of four subsystems.The cloud-side-end support subsystem constitutes the basis of computing,communication and storage.The visualized hardware- and software-in-the-loop subsystem is the key to command,make decision,and exercise in traffic control.The real-time control subsystem and real-time simulation subsystem constitute the core of traffic control operation,which can guarantee the real-time and advanced traffic control.Instead of traditional passive control of intersection signals relying on cycle and green ratio adjustment,the system adopts an active control of intersections characterized with variable lanes,adjustable phase sequence and chain connection features.Experimental analysis shows that the system can effectively reduce the total delay time and average queue value of vehicles waiting on the traffic lights at the intersection.

Abstract:The openness of wireless media has been a security threat for traditional wireless network based on security protocol.While the Radio Frequency Fingerprint (RFF) identification is based on physical layer security,and considering the RFF is impossible to forge,the RFF identification can effectively improve the security of wireless network.Aiming at the multi-scene and multi-device identification,an RFF identification approach is constructed based on attention residual convolution neural network.The dataset contains 32 Wi-Fi modules,covering the 2.4 GHz module of 802.11b standard.The comparison results show that the recognition accuracy of the proposed approach is 90% for the 32 Wi-Fi modules,higher than that of traditional algorithm (86%) and convolutional neural network approach (89%);the recognition accuracy can be higher than 90% on the dataset with different sampling rates when the SNR is greater than 2 dB,which can reach as high as 96% when the SNR is greater than 20 dB.

Abstract:In order to achieve the goal of low power consumption and low latency for edge-end human activity recognition,this paper designs a fast recognition system based on wearable sensors and Convolutional Neural Networks (CNNs).First,the system collects data through sensors to make a human activity recognition dataset,and pre-trains a CNN-based behavior recognition model on the PC side,which achieves an accuracy of 93.61% on the test set.Then,hardware acceleration is realized through methods such as data fixed point,convolution kernel multiplexing,parallel processing of data,and pipeline.Finally,the recognition model is deployed on the FPGA,and the collected sensor data are input into the system to realize the recognition of human activity at the edge.The whole system is developed jointly with hardware and software based on Ultra96-V2.The experimental results show that when the input clock is 200 M,the system runs on FPGA with an accuracy of 91.80%;the proposed system is superior to CPU in recognition speed as well as power consumption,specifically,the power consumption is only one-tenth of CPU consumed,and energy consumption ratio is 91% higher than that of GPU.It can be concluded that the FPGA-based human activity recognition system meets the design requirements of low power consumption and low delay.

Abstract:At present,high-dimensional (HD) feature detection is an effective approach to improve the detection performance of sea-surface small targets.The main difficulty lies in the design of classifier in high-dimensional space.Therefore,a feature detection approach based on false-alarm-controllable gradient boosting decision tree (GBDT) is proposed in this paper.First,multiple features are extracted from the 1D long-term observation vector in time,frequency,time-frequency domains to construct an HD feature vector.In this way,the detection problem is converted into a binary classification problem.Second,two types of balanced training samples are solved by simulating returns with target.Third,GBDT algorithm is introduced to condense the HD feature vector into 1D predicted value in probability.The predicted value is used as detection statistics to solve the problem of uncontrollable false alarm rate perplexed the binary classifier.Finally,experimental results are verified by IPIX measured data,which show that the proposed detector can make full use of all the information from the HD characteristics,and the performance is improved by over 13%.

Abstract:Phase-Locked Loop (PLL) with a pre-filtering stage is a powerful tool to study the grid synchronization technology.However,the dynamic performance of this class of commonly used PLL (e.g.,second-order generalized integrator PLL,complex-coefficient filter PLL) is constrained by the low cut-off frequency of the front-stage structure.Thus,a three-phase PLL technique based on fractional-order generalized integrator is proposed.The front-stage filter of the PLL is composed of fractional-order integrators,which can generate two diagonal signals whose phase difference is 45°.Through the correlation linear operation,the positive- and negative-sequence components of the grid voltage can be extracted from the diagonal signals.Combined with the post-stage synchronous rotating frame PLL,a mathematical model of the entire PLL system is built,and the third order optimal design method is used to correct the system and determine the related control parameters.The study shows that,the cut-off frequency of the fractional-order generalized integrator is obviously higher than that of the second-order generalized integrator,which is helpful to improve the dynamic quality of the PLL system.The simulation and experimental results show that the dynamic performance of the proposed PLL is better than that of the multiple second-order generalized integrator PLL.

Abstract:As the most promising energy storage technology at present,the Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) can promote the regional integrated energy system to absorb new energy,make efficient use of traditional energy,and reduce environmental pollution. Here,an energy supply optimization strategy for regional integrated energy system is established with the AA-CAES,which can reduce the operation cost of the system in its daily cycle. First,we analyze the energy storage characteristics of the AA-CAES,the electric power balance as well as the thermal power balance of the regional integrated energy system,and the operation constraints of different energy suppliers. Then we use optimization software of GAMS to solve the operation cost of traditional regional integrated energy system and the proposed strategy in their daily cycles. Finally,the comparison in aspects of environmental protection and economic cost verifies the effectiveness and feasibility of the proposed strategy in reducing cost,promoting new energy consumption,and energy conservation & emission reduction.

Abstract:The rapid growth of malicious applications has posed a security threat to mobile intelligent terminals.It is of great significance to achieve high-precision detection of malicious applications for mobile network information security.Here,this paper proposes a method to detect malicious applications based on improved deep residual shrinkage network.First,the traffic features are preprocessed into convolutional neural network inputs,and then the channel attention mechanism and spatial attention mechanism are introduced to weight the sample features from the channel and spatial dimensions.Then,the deep residual shrinkage network is introduced to adaptively filter out the redundant features of the samples,and the parameters are back propagated through the identical connection optimization,so as to reduce the difficulty of model training and classification,and finally realize the high-precision identification of malicious android applications.The proposed method avoids manual feature extraction,achieves high-precision classification and has certain generalization ability.Experimental results show that the accuracy of the proposed method is 99.40%,99.95% and 97.33% in 2-classification,4-classification and 42-classification of malicious applications,respectively.Compared with the existing methods,the proposed method has better classification performance and generalization ability.