-空间的一点注记

给出了－空间的一种刻画，主要结果如下：X是－空间当且仅当X具有σ-离散CS*-网络．

Characteristic analysis of 1.06μm long-cavity diode lasers based on asymmetric waveguide structures

In long-cavity edge-emitting diode lasers,longitudinal spatial hole burning(LSHB),two-photon ab⁃sorption(TPA)and free carrier absorption(FCA)are among the key factors that affect the linear increase in out⁃put power at high injection currents.In this paper,a simplified numerical analysis model is proposed for 1.06μm long-cavity diode lasers by combining TPA and FCA losses with one-dimensional(1D)rate equations.The ef⁃fects of LSHB,TPA and FCA on the output characteristics are systematically analyzed,and it is proposed that ad⁃justing the front facet reflectivity and the position of the quantum well(QW)in the waveguide layer can improve the front facet output power.

Attention Mechanism-Based Method for Intrusion Target Recognition in Railway

The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.

Three-dimensional conjugate gradient inversion of magnetotelluric sounding data

Based on the analysis of the conjugate gradient algorithm, we implement a threedimensional （3D） conjugate gradient inversion algorithm with magnetotelluric impedance data. During the inversion process, the 3D conjugate gradient inversion algorithm doesn＇ t need to compute and store the Jacobian matrix but directly updates the model from the computation of the Jacobian matrix. Requiring only one forward and four pseudo-forward modeling applications per frequency to produce the model update at each iteration, this algorithm efficiently reduces the computation of the inversion. From a trial inversion with synthetic magnetotelluric data, the validity and stability of the 3D conjugate gradient inversion algorithm is verified.

Flow equation and similarity criterion during centrifugal casting in micro-channel

Liquid metal filling flow process in the microscale during the centrifugal casting process was studied by means of similar physical simulation. The research was focused on derived similarity criterion. Based on the traditional flow equations, the flow equation and the Bernoulli＇s equation for liquid metal flows in micro-scale space were derived, which provides a mathematical model for numerical simulation of micro-scale flow. In the meanwhile, according to the micro-flow equation and the similarity theory, the similarity criterion for the physical simulation of the mold filling behaviors was presented under centrifugal force field, so as to achieve the visual observation and quantitative analysis of micro-flow process.

Regression-Kriging of Soil Organic Matter Using the Environmental Variables Derived from MODIS and DEM

[Objective] The objective of this project was to evaluate and compare spa- tial estimation accuracy by ordinary kriging and regression kriging with MODIS data, predicting SOM contents using limited available data in Shimen County, Hunan Province, China. [Method] Terrain parameters （derived from DEM） and Normalized differential vegetation index （NDVI）, Land surface temperature （LST） （derived from MODIS data） were used as auxiliary data to predict the SOM spatial distribution. The mean error （ME） and mean square error （RMSE） were adopted to validate the SOM prediction accuracy. The descriptive statistics and data transformation were conducted by using computer technology. [Result] Regression kriging with terrain and remotely sensed data was superior to ordinary kriging in the case of limited available samples; even the linear relationship between environmental variables and SOM content was moderate. The accuracy assessment showed that the regression kriging method combining with environmental factors obtained a lower mean predication error and root mean square prediction error. The relative improvement was 6.03% compared with ordinary kriging. [Conclusion] Remotely sensed data such as MODIS im- age have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction in the hilly regions.

Fuzzy c-means text clustering based on topic concept sub-space

To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.

A new methodology for estimating forest NPP based on forest in-ventory data——a case study of Chinese pine forest

Accurately estimating forest net primary productivity (NPP) plays an important role in study of global carbon budget. A NPP model reflecting the synthetic effects of both biotic (forest stand age, A and stem volume, V) and climatic factors (mean annual actual evapotranspiration, E) was developed for Chinese pine (Pinus tabulaeformis) forest by making full use of Forest Inventory Data (FID) and dynamically assessing forest productivity. The NPP of Chinese pine forest was estimated by using this model and the fourth FID (1989–1993), and the spatial pattern of NPP of Chinese pine forest was given by Geography Information System (GIS) software. The results indicated that mean NPP value, of Chinese pine forest was 7.82 t m?2·a?1 and varied at the range of 3.32–11.87 t hm?2·a?1. NPP distribution of Chinese pine forests was significantly different in different regions, higher in the south and lower in the north of China. In terms of the main distribution regions of Chinese pine, the NPPs of Chinese pine forest in Shanxi and Shaanxi provinces were in middle level, with an average NPP of 7.4 t hm?2·a?1, that in the southern and the eastern parts (e.g. Shichuang Hunan, Henan, and Liaoning provinces) was higher (over 7.7 t hm?2·a?1), and that in the northern part and western part (e.g. Neimenggu and Ningxia provinces) was lower (below 5 t hm?2·a?1). This study provides an efficient way for using FID to understand the dynamics of foest NPP and evaluate its effects on global climate change. Keywords Forest NPP - Forest inventory data - Chinese pine forest - Climatic and biotic NPP model - Spatial distribution pattern CLC number S727.22 - S757.2 Document code A Foundation item: This study was supported by the National Natural Science Foundation of China (Nos. 30028001, 49905005), National Key Basic Research Specific Foundation (G1999043407); the Chinese Academy of Sciences (KSC2-1-07).Biography: ZHAO Min (1973-), female, Ph. D. in Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, P. R. China.Responsible editor: Zhu Hong

The numerical simulation for a 3D two-phase anisotropic medium based on BISQ model

Biot-flow and squirt-flow are the two most important fluid flow mechanisms in porous media containing fluids. Based on the BISQ （Biot-Squirt） model where the two mechanisms are treated simultaneously, the elastic wave-field simulation in the porous medium is limited to two-dimensions and two-components （2D2C） or two-dimensions and three-components （2D3C）. There is no previous report on wave simulation in three- dimensions and three-components. Only through three dimensional numerical simulations can we have an overall understanding of wave field coupling relations and the spatial distribution characteristics between the solid and fluid phases in the dual-phase anisotropic medium. In this paper, based on the BISQ equation, we present elastic wave propagation in a three dimensional dual-phase anisotropic medium simulated by the staggered-grid high-order finite-difference method. We analyze the resulting wave fields and show that the results are an improvement.

Phytoplankton Community Structure and Its Spatial Distribution along Anhui-Jiangsu Reaches of the Yangtze River in Autumn 2009

[Objective] Surveys of phytoplankton were taken during the autumn of 2009 along Anhui-Jiangsu reaches of the Yangtze River to understand the species com- position and their spatial distributions. [Method] Phytoplankton species, their quanti- ties, biomass and the Mcnaughton＇s dominance indices were all measured and their spatial distribution characteristics were determined using cluster analysis. [Result] There were 27 species, belonging to 5 phyla, namely Chlorophyta, Bacillariophyta, Cyanophyta, Euglenophyta and Cryptophyta, of phytoplankton collected and identified from the surveys. Results showed that Bacillariophyta was the predominant phyto- plankton with 16 species collected which accounted for 59.3% of the total species identified. The number of species collected belonging to Chlorophyta, Cyanophyta, Cryptophyta and Euglenophyta were 6, 2, 2, and 1, accounting for 22.2%, 7.4%, 7.4% and 3.7% of the total number identified, respectively. At the species level, the predominant species were Fragilaria crotonensis, Melosira italica, Navicula crypto- cephala, Cyclotella striata and Chroomonas acuta. Phytoplankton abundance was ranging from 5.68×10^4 to 7.08×10^4 cells/L with its average of 6.01×10^4 cells/L. Phyto- plankton biomass was ranging from 30.43 to 34.73 μg/L with its average of 32.46 μg/L. Compared with the previous reports, the number of phytoplankton species was decreased but its abundance and biomass was increased along the Jiangsu reach of the Yangtze River. However, Bacillariophyta species were still the predominant species and the phytoplankton community structure had not significantly changed from the previous studies. [Conclusion] These results might be explained as that the water quality in the Yangtze River was deteriorated but had not come to the worst. The results of similarity analysis gave two clusters of phytoplankton community as Nanjing, Wuhu and Jiangyin sampling sites were clustered into one group and Tongling and Anqing were clustered into another group.

Multi-granularity spatial-temporal access control model for web GIS

The multi-granularity spatial-temporal-related access control（MSTAC） model was proposed to meet the spatial access control requirements for the service-oriented spatial data infrastructure（SDI）. MSTAC extends the attribute constraints of role-based access control（RBAC）, which includes the user＇s location attribute, the role＇s time constraint, the layer vector constraint of a map class, the scale and time constraints of a geographic layer, the topological constraints of geographic features, the semantic attribute expression constraints of geographic features, and the field constraint of feature views. Through this model, authorized users would be limited to access different granularity spatial datasets, such as the map granularity, the graphic layer granularity, the feature object granularity and the feature view granularity. Finally, the MSTAC model is achieved in a web GIS, which shows the positive and negative authorizations to different services in different data granularities and time periods.

ADAPTIVE PREDICTIVE CONTROL OF NEAR-SPACE VEHICLE USING FUNCTIONAL LINK NETWORK

A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.

Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

Orientation of Pigments in the Isolated PhotosystemⅡSub-core Reaction Center CP47/D1/D2/Cyt b-559 Complexes:A Linear Dichroism Study

Linear dichroism (LD) spectroscopy is an important technique in the study of the orientation and organization of pigments in the photosynthetic membrane complexes in vivo and in vitro . In this work, the orientation of the pigments in the isolated photosystem Ⅱ (PSⅡ) sub_core reaction center complexes was analyzed and characterized by means of low temperature absorption and LD spectroscopy. The preparations containing different amounts of CP47 isolated from spinach (Spinacia oleracea L.) chloroplast were used in order to investigate the orientation of pigments in the PSⅡ sub_core CP47/D1/D2/Cyt b_559 (CP47/D1/D2) complexes. Chlorophyll a (Chl a) absorbing at 680 nm in CP47/D1/D2/Cyt b_559 complex showed an orientation of the Q y transition parallel to the membrane plane. It is proposed that there are two forms of β_carotene (β_Car) in CP47/D1/D2/Cyt b_559 complex, denoted as β_Car (Ⅰ) and β_Car (Ⅱ), with different orientations, β_Car (Ⅰ) at 470 and 505 nm is roughly parallel to the membrane plane, and β_Car (Ⅱ) at 460 and 490 nm seems to be perpendicular orientation. Upon the photoinhibitory experiment β_Car (Ⅱ) was found to be photosensitive and easily photodamaged. It also showed that the positive LD signal observed at 680 nm was quite complicated. This signal is tentatively attributed to P680 and some Chl a of antenna in CP47 protein based upon our measurements.

A Reduced-Order Modeling of Multi-Port RC Networks by Means of Graph Partitioning

A modified reduced-order method for RC networks which takes a division-and-conquest strategy is presented.The whole network is partitioned into a set of sub-networks at first,then each of them is reduced by Krylov subspace techniques,and finally all the reduced sub-networks are incorporated together.With some accuracy,this method can reduce the number of both nodes and components of the circuit comparing to the traditional methods which usually only offer a reduced net with less nodes.This can markedly accelerate the sparse-matrix-based simulators whose performance is dominated by the entity of the matrix or the number of components of the circuits.

Measurement of Coordinate Parameter by Multi-baseline Digital Close-range Photogrammetry System and Data Analysis for Jujube

[Objective] The aim was to explore the measurement of coordinate parameter by multi-baseline digital close-range photogrammetry system.[Method] The 3-dimensional coordinate of 8-year-old Jujube was measured by using Lensphoto multi-baseline digital close-range photogrammetry system,and through comparing with measured data of Total Station,the error and accuracy of photogrammetry data were analyzed.[Result] The absolute error of X,Y and Z coordinate was 0-0.014,0-0.018 and 0-0.004 m respectively,and the relative error of X,Y and Z coordinate was less than 0.145%.The significance test of pairs for the photogrammetry data and measured data of Total Station indicated that the space coordinate data of stumpage were accurately measured by using the multi-baseline digital close-range photogrammetry method,and the photogrammetry data meet the need of space coordinate measurement for virtual plant growth simulation.[Conclusion] This study had provided theoretical basis for the growth measurement of virtual plant growth simulation.

Study on Tri-Stimulus Transformation

To decrease number of samples for the implementation of color space transformation, a method for modeling the chromatic characterization of video cameras was proposed. An additional transformation was required to predict output RGB values for an input color. This additional transformation was based on spectral reflectance relationship. The transformed color coordinates were taken as inputs of a multilayer neural network. Based on network outputs, the RGB values to be predicted were calculated. Experimental results were given to illustrate the performance of the method. Even though much less number of training samples are used, this method can also perform well on this color space transformation.

New Color-Difference Formula Based on CIECAM97s

To deduce a new color difference formula based on CIE 1997 Color Appearance Model(CIECAM97s), a color space J a 1 b 1 is first constructed with color appearance descriptors J,a,b in CIECAM97s. The new formula is then deduced in the space and named CDF CIECAM97s. The factors for lightness, chroma and hue correction in the formula are derived by linear regression according to BFD? CP data sets. It is found by statistical analysis that CDF CIECAM97s is in closer accordance with the visual assessments when compared with CMC(1∶1), CIE94 and CIE L *a *b * color difference formulae. Based on color appearance model, the new color difference formula can be used to predict color difference perception in a varity of different viewing conditions.

Accelerating SAGE algorithm in PET image reconstruction by rescaled block-iterative method

A new method to accelerate the convergent rate of the space-alternatinggeneralized expectation-maximization (SAGE) algorithm is proposed. The new rescaled block-iterativeSAGE (RBI-SAGE) algorithm combines the RBI algorithm with the SAGE algorithm for PET imagereconstruction. In the new approach, the projection data is partitioned into disjoint blocks; eachiteration step involves only one of these blocks. SAGE updates the parameters sequentially in eachblock. In experiments, the RBI-SAGE algorithm and classical SAGE algorithm are compared in theapplication on positron emission tomography (PET) image reconstruction. Simulation results show thatRBI-SAGE has better performance than SAGE in both convergence and image quality.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.