Spatial-temporal Dynamics and Driving Forces of Land Development Intensity in the Western China from 2000 to 2015

The change in land development intensity is an important perspective to reflect the variation in regional social and economic development and spatial differentiation.In this paper,spatial statistical analysis,Ordinary Least Squares(OLS),and Geographically weighted regression(GWR)methods are used to systematically analyse the spatial-temporal characteristics and driving forces of land development intensity for 131 spatial units in the western China from 2000 to 2015.The findings of the study are as follows:1)The land development intensity in the western China has been increasing rapidly.From 2000 to 2015,land development intensity increased by 3.4 times on average.2)The hotspot areas have shifted from central Inner Mongolia,northern Shaanxi and the Beibu Gulf of Guangxi to the Guanzhong Plain and the Chengdu-Chongqing urban agglomeration.The areas of cold spots were mainly concentrated in the Qinghai-Tibet Plateau,Yunnan,and Xinjiang.3)Investment intensity and the natural environment have always been the main drivers of land development intensity in the western China.Investment played a powerful role in promoting land development intensity,while the natural and ecological environment distinctly constrained such development.The effect of the economic factors on land development intensity in the western China has changed,which is reflected in the driving factor of construction land development shifting from economic growth in 2000 to economic structure,especially industrial structure,in 2015.

SPATIAL-TEMPORAL DYNAMICS OF REGIONAL CONVERGENCE AT COUNTY LEVEL IN JIANGSU

The dynamics of regional convergence include spatial and temporal dimensions. Spatial Markov chain can be used to explore how regions evolve by considering both individual regions and their geographic neighbors. Based on per capita GDP data set of 77 counties from 1978 to 2000, this paper attempts to investigate the spatial-temporal dynamics of regional convergence in Jiangsu. First, traditional Markov matrix for five per capita GDP classes is constructed for later comparison. Moreover, each region’s spatial lag is derived by averaging all its neighbors’ per capita GDP data. Conditioning on per capita GDP class of its spatial lag at the beginning of each year, spatial Markov transition probabilities of each region are calculated accordingly. Quantitatively, for a poor region, the probability of moving upward is 3.3% if it is surrounded by its poor neighbors, and even increases to 18.4% if it is surrounded by its rich neighbors, but it goes down to 6.2% on average if ignoring regional context. For a rich region, the probability of moving down ward is 1.2% if it is surrounded by its rich neighbors, but increases to 3.0% if it is surrounded by its poor neighbors, and averages 1.5% irrespective of regional context. Spatial analysis of regional GDP class transitions indicates those 10 upward moves of both regions and their neighbors are unexceptionally located in the southern Jiangsu, while downward moves of regions or their neighbors are almost in the northern Jiangsu. These empirical results provide a spatial explanation to the "convergence clubs" detected by traditional Markov chain.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Research on Tectonic Features and Dynamics in the Northwestern Yunnan Extensional Region

The paper discusses the features of active tectonics,seismicity and neotectonic environment in the Northwestern Yunnan extensional region.The intensity of both tectonic activity and seismicity is strong near the south and north boundaries in the areas,but weak in the middle.The distribution of the strongest subsided areas,lacustrine terrace and Quaternary fold is characterized by the diagonal symmetry.Formation of extensional tectonics in the Northwestern Yunnan can be explained by passive model,experiencing the action of compressional force in the N-S direction and shear force in the SW-NE direction,and classified as a special pull-apart tectonics.The direction of the composite force is NNE,which is coincided with the results acquired by the methods of water-compressed rupture and physical modelling.

Flame Recognition in Video Images with Color and Dynamic Features of Flames

Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this paper,we develop a new algorithm that can significantly improve the accuracy of flame detection in video images.The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model.A few new dynamic and hierarchical features associated with the suspected regions,including the flicker frequency of flames,are then extracted and analyzed.The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a classifier,which can be a BP neural network,a k nearest neighbor classifier or a support vector machine.Testing results show that this algorithm is robust and efficient,and is able to significantly reduce the probability of false alarms.

Simulation on Dynamic Bending Features of Fabric Based on Fluid-Solid Interaction Technique

This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.

DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.

MOLECULAR DYNAMICS SIMULATION ON VIERATIONAL SPECTROSCOPIC FEATURES OF HYDROGEN BONDS IN CRYSTALLINE POLYMERS

Introduction The molecular dynamics simulation technique has recently proved to be a suitable alternative approachfor simulation of vibrational spectroscopy. In this study, molecular dynamics was utilized to understandlow frequency vibrations in highly ordered poly(ρ-phenylene terephthalmide) (PPTA). A key structuralfeature of this polymer is the presence of hydrogen bonds. There is little question that this strong localized

MOVING OBJECT TRACKING IN DYNAMIC IMAGE SEQUENCE BASED ON ESTIMATION OF MOTION VECTORS OF FEATURE POINTS

An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.

Dynamic feature of flavonoids content in different organs of larch （Larix gmelinii）

Flavonoids in plants is very important in its ecological role and economicvalue. The dynamic features of flavonoids content in different organs of larch (Larix gmelinii) atdifferent light and temperature conditions were investigated in this study. Results showed that theorder of flavonoids content in different organs from high to low was 7.78% (stem bark) > 2.79%(leaves) > 1.72% (branches) > 1.19% (stem xylem)and different organs had a great seasonal variationin flavonoids content, but the change of flavonoids content at different temperature was not obviousin different organs., The content of flavonoids in barck had, a positive correlation withtemperature (R^2=0.75), but that in other organs had slight variation with the change oftemperatures. For all the tested organs, the flavonoids content in summer and autumn wasapproximately 3-4 times higher than in spring and winter. This is attributed to the great stressfrom environmental physical variables such as UV radiation, high temperature that induce theaccumulation of flavonoids. The flavonoid content of sun leaves was evidently higher than that ofshade leaves, and leaves at upper part of canopy had a higher flavonoids content compared with thatat other parts. This result indicates that sun radiation could improve flavonoids production inleaves (R^2=0.76). The flavonoids may actively evolve in plant defenses to environmental stress,protecting larch from the damage of high temperature and radiation, and its main function isdifferent in different organs.

Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features

Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.

Micropillar compression using discrete dislocation dynamics and machine learning

Discrete dislocation dynamics(DDD)simulations reveal the evolution of dislocation structures and the interaction of dislocations.This study investigated the compression behavior of single-crystal copper micropillars using fewshot machine learning with data provided by DDD simulations.Two types of features are considered:external features comprising specimen size and loading orientation and internal features involving dislocation source length,Schmid factor,the orientation of the most easily activated dislocations and their distance from the free boundary.The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs.It is found that the machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features.However,the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars.Overall,incorporating internal features,especially the information of most easily activated dislocations,improves predictive capabilities across diverse sample sizes and orientations.

A process monitoring method for autoregressive-dynamic inner total latent structure projection

As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design

In order to maintain vibration performances within the limits of the design, a vibration-based feature extraction method for dynamic characteristic using empirical mode decomposition （EMD） and wavelet analysis was proposed. The proposed method was verified experimentally and numerically by implementing the scheme on engine block. In the implementation process, the following steps were identified to be important： 1） EMD technique in order to solve the feature extraction of vibration signals; 2） Vibration measurement for the purpose of confirming the structural weak regions of engine block in experiment; 3） Finite element modeling for the purpose of determining dynamic characteristic in time region and frequency region to affirm the comparability of response character corresponding to improvement schemes; 4） Adopting a feature index oflMF for structural improvement based on EMD and wavelet analysis. The obtained results show that IMF of signal is more sensitive to response character corresponding to improvement schemes. Finally, examination of the results confirms that the proposed vibration-based feature extraction method is very robust, and focuses on the relative merits of modification and full-scale structural optimization of engine, together with the creation of new low-vibration designs.

Dynamic Characteristics and Driving Forces of Wetland Landscapes in Northeast China

Dynamic characteristics and spatial-temporal distribution patterns of wetland landscapes in northeast China from 1986 to 2000 were quantitatively analyzed and studied by applying theories and methods of landscape ecology,Land Change Science,remote sensing and GIS techniques.Through analyzing the dynamic spatial-temporal change degree,direction and pattern of wetland within the study area,as well as the characteristics of landscape pattern change and landscape transformation,this study got the following results:area and patch amount of wetland in northeast China showed a decreasing trend as a whole in the past 15 years.In terms of dynamic landscape changes,although the annual decrease rate of the last 5 years was 28 times more than that of the first 10 years,the first 10 years was a period with relatively more drastic patch changes of wetland landscapes in northeast China.By reviewing the overall changes of wetland landscapes in northeast China,the following characteristics were summarized:expanding in certain periods but decreasing in the overall trend,shrinking in parts but expanding from boundaries,showing high fragmentation and so on.Study on its driving forces showed that a unique spatial pattern of wetland landscapes was formed with the dual intervention of natural and artificial factors.

Analysis of the Effects of Slope Geometry on the Dynamic Response of a Near-field Mountain from the Wenchuan Earthquake

Fourier spectra and acceleration response spectra of near-field acceleration records of the 2008 Wenchuan Earthquake have been calculated.Relative fundamental frequencies(or predominant periods) were characterized.Then,the natural frequencies of a range of slopes with different geometric characteristics,such as height,slope ratio,and pattern,were analyzed.The seismic responses of the slopes were compared,and the variability of seismic response with the above geometric elements was found.Results show that if slope height increases,and provided that other conditions are unchanged,the natural frequency of the first mode of a doublesurface slope will change as a power law.However,natural frequencies will diminish(based on a parabolic function) as the slope angle becomes large.Both the surface pattern and the number of surfaces on a slope can have a great impact on the seismic response of the slope.Moreover,within a certain range of slope heights or angles,either height or angle will also greatly influence the variability of the seismic response.The results of this research will be helpful to understanding seismic dynamic response features and explaining the ways that slope stability can be affected by earthquakes.

Shallow groundwater dynamics in North China Plain

The groundwater level of 39 observation wells including 35 unconfined wells and 4 confined wells from 2004 to 2006 in North China Plain(NCP) was monitored using automatic groundwater monitoring data loggers KADEC-MIZU II of Japan.The automatic groundwater sensors were installed for the corporation project between China and Japan.Combined with the monitoring results from 2004 to 2006 with the major factors affecting the dynamic patterns of groundwater, such as topography and landform, depth of groundwater level, exploitation or discharge extent, rivers and lakes, the dynamic regions of NCP groundwater were gotten.According to the dynamic features of groundwater in NCP, six dynamic patterns of groundwater level were identified, including discharge pattern in the piedmont plain, lateral recharge-runoff-discharge pattern in the piedmont plain, recharge-discharge pattern in the central channel zone, precipitation infiltration-evaporation pattern in the shallow groundwater region of the central plain, lateral recharge-evaporation pattern in the recharge-affected area along the Yellow River and infiltration-discharge-evaporation pattern in the littoral plain.Based on this, the groundwater fluctuation features of various dynamic patterns were interpreted and the influencing factors of different dynamic patterns were compared.

A long-term record(1995-2019)of the dynamics of land desertification in the middle reaches of Yarlung Zangbo River basin derived from Landsat data

Widespread desertification in the middle part of the Yarlung Zangbo River(YZR)basin is threatening the sustain-able development of this region.To capture this process,a method was proposed for large-scale desertification monitoring by using Landsat images from 1995 to 2019.The method used an integrated classification method combined with a hierarchical decision tree and nearest neighbor classifiers.The spatio-temporal dynamics of the desertification pattern were analyzed to assist in the detection of possible driving forces.Using validation samples collected from Google Earth high-resolution images and field investigations,the overall accuracy of the classification in 2019 was 92.3%with a Kappa coefficient of 0.84.The major results were:(1)total sandy land area in 2019 was 734.1 km^(2),which accounted for 3.7%of the study area,prominently distributed along the wide river valleys and inlets of tributaries with a strip and discontinuous pattern.Sandy land tends to be distributed in the southern aspect regions with lower elevations and that are closer to rivers;(2)sandy land areas showed two temporal stages:a gradual increase of 102.4 km^(2)from 1995 to 2015 and a large decrease of 106.8 km^(2)from 2015 to 2019;(3)newly increased sandy land was distributed in the YZR Valley,while the revegetation on sandy land occurred mainly in the Lhasa River basin and some regions in the YZR Valley;and(4)increased sandy land area of 142.1 km^(2)was mainly distributed in the southern band of the two rivers.Correspondingly,revegetation on sandy land was more effective on the northern banks of the river valleys.These findings provide guidance for implementing vegetation recovery on sandy lands and provide important insights for maintaining sustainable development.