基于混合处理模型的乱序数据流分布式聚合查询处理技术

为了解决现有的乱序数据流聚合查询处理技术不能在降低查询处理延迟,同时保障聚合查询结果的最终正确性的局限性问题,本研究设计了混合嵌入分布式流处理模块和分布式批处理模块的乱序数据流分布式聚合查询处理技术。该技术一方面基于用户给定的结果质量,限制自适应地优化流处理模块所用的缓冲区大小,从而尽可能降低流处理的查询处理延迟;另一方面基于备份于分布式数据存储系统的历史流数据,并以批处理的方式实现对极其晚到流元组的查询处理,从而保障聚合查询结果的最终正确性。基于真实的乱序数据流数据集对该技术进行测试分析表明:该技术在平均查询处理时延、查询结果精度和系统可扩展性方面,比目前最好的基于缓存的乱序数据流处理技术均具有显著优势。

国有农场土地经营权流转促进了耕地养护吗?

随着土地经营权流转向市场化、正规化转变,转入主体在养护耕地上是否仍然比承包户消极,产权期限较短是否是经营主体不养护耕地的主要原因。中国国有农场土地制度与农村地区存在差异,但有助于回答以上问题,其结论不仅能提供新的经验和证据,对农村耕地保护亦有前瞻和启示作用。文章构建了经营主体养护耕地的理论模型,基于新疆生产建设兵团国有农场1191个棉花种植户数据,运用考虑了内生性的估计策略,考察了高商品化率、市场化流转的大规模农业中,不同产权性质和产权期限地块的养护差异。结果表明:①尽管租入地的合约期限远低于承包地的承包期限,但通过市场化流转而获得耕地的规模租入户改良土壤比承包户更积极。而在残膜对产出的影响未超过“危害阈值”的情形下,租入户在地膜回收上比承包户消极;②产权期限与种植户改良土壤并不总是显著正相关,与较长产权期限相关联的负面特征(较低的治污价值认知)导致了其与地膜回收行为的反向影响关系;③经营权流转能够促进耕地养护,特别是对于规模租入户以及政府无差别干预的养护行为,同时经营主体所具备的大规模、高管理技术水平特质使其获取的收益能够抵消产权期限短带来的不利影响。为避免委托代理风险、促进不同经营主体用养结合,应采取能够提升远期农地价值和现期作物收益的措施,如完善土地承包经营退出机制以提升养护耕地的远期土地价值,政策激励瞄准经营主体的长期行为结果,并为经营主体提升养护耕地的作物收益提供技术支持和信息干预等。

Indirect tension test of epoxy asphalt mixtureusing microstructural finite-element model

A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is divided into two phases：aggregates and mastic.A viscoelastic constitutive relationship,which is obtained from the results of a creep test,is used to represent the mastic phase at intermittent temperatures.Model simulation results of the stiffness modulus in IDT compare favorably with experimental data.Different loading directions and velocities are employed in order to account for their influence on the modulus and the localized stress of the microstructure model.It is pointed out that the modulus is not consistent when the loading direction changes since the heterogeneous distribution of the mixture internal structure,and the loading velocity affects the localized stress as a result of the viscoelasticity of the mastic.The study results can provide a theoretical basis for the finite-element method,which can be extended to the numerical simulations of asphalt mixture micromechanical behavior.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

农地确权、土地细碎化与农户生产要素投入决策——来自广东粤北山区农户的调查分析

文章基于中国农地确权制度改革的政策背景,以及备受关注的土地细碎化的资源特征,构建“产权权利界定、产权资源特性与农户生产要素投入”的理论分析框架,借助广东粤北山区阳山县农户的调查数据,采用条件混合处理模型的计量结果表明:农地确权未能有效激励农户的生产要素投入行为,但土地细碎化显著抑制了农户雇佣劳动力和机械服务购买行为,还显著促进了化肥施用。进一步纳入农地确权和土地细碎化的交互项的模型结果表明,农地确权与土地细碎化两者之间不存在互补或替代的交互效应。采用中介机制、子样本分析等模型验证了上述结论的稳健性。由此认为,农地确权政策的影响具有情境依赖性,不可教条式地过度吹捧农地确权政策的生产投入影响效应;对于丘陵山区地带而言,农业政策应从聚焦于地权制度改革调整到缓解农地细碎化问题上。这不仅对激励农业生产要素的投入具有必要性与紧迫性,而且也有利于农地“减肥”,缓解中国日益紧迫的农业资源环境问题。

Comparison of wrist motion classification methods using surface electromyogram

The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.

Improved Real-time Implementation of Adaptive Gassian Mixture Model-based Object Detection Algorithm for Fixed-point DSP Processors

Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.

Improved dark channel image dehazing method based on Gaussian mixture model

To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.

A GAUSSIAN MIXTURE MODEL-BASED REGULARIZATION METHOD IN ADAPTIVE IMAGE RESTORATION

A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.

Probability-based Clustering and Its Application to WLAN Location Estimation

Wireless local area networks (WLAN) localization based on received signal strength is becoming an important enabler of location based services. Limited efficiency and accuracy are disadvantages to the deterministic location estimation techniques. The probabilistic techniques show their good accuracy but cost more computation overhead. A Gaussian mixture model based on clustering technique was presented to improve location determination efficiency. The proposed clustering algorithm reduces the number of candidate locations from the whole area to a cluster. Within a cluster, an improved nearest neighbor algorithm was used to estimate user location using signal strength from more access points. Experiments show that the location estimation time is greatly decreased while high accuracy can still be achieved.

Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform

As an advanced generation instrument of earth observation,small footprint full waveform light detection and ranging(LiDAR) technology has been widely used in the past few years.Decomposition and radiative correction is an important step in waveform data processing,it influences the accuracy of both information extraction and further applications.Based on a stepwise strategy,this study adopts Gaussian mixture model to approximate the LiDAR waveform.In addition to waveform decomposition,a relative correction model is proposed in this paper,the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction.Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye,China.The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms,center position and width of each peak well.The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.

Flow computations of multi-stages by URANS and flux balanced mixing models

The quasi-steady methods based on mixing models have been widely applied to flow computations of turbomachinery multi- stages in aerospace engineering. Meanwhile, the unsteady numerical simulation has also been used due to its ability in obtaining time-dependent flow solutions. In the paper, two different mixing treatments and the corresponding flux balanced ones are presented to exchange the flow solutions on the interfaces between adjacent blade rows. The four mixing treatments are then used for flow computations of a subsonic 1.5-stage axial turbine and a quasi-l.5-stage transonic compressor rotor. The results are compared with those by unsteady numerical method, which is implemented by using the sliding mesh technique. The effects of the quasi-steady and unsteady computation methods on the conservation of flow solutions across the interfaces are presented and addressed. Furthermore, the influence of mixing treatments on shock wave and flow separation of the transonic compressor rotor is presented in detail. All the results demonstrate that the flux balanced mixing treatments can be used for multi-stage flow computations with improved performance on interface conservation, even in the complex flows.