宜昌至杨家脑河段初期冲刷发展过程与趋势的分析

由于三峡水利枢纽蓄水拦沙作用,下游河段将发生冲刷调整.冲刷调整的沿程发展将给防洪、航运、取水等带来影响.本文依据宜昌至杨家脑河段的实测资料,对该河段蓄水后的冲刷纵向和横向发展过程以及发展趋势进行了分析.结果表明:宜昌至杨家脑河段冲刷遵循枢纽下游河段冲刷发展的一般规律,即冲刷自上面下逐渐发展;该河段内单一的顺直河段较弯曲分汊的河段稳定,即冲刷主要集中在河段内的弯曲分汊段;目前受来水条件的限制,宜昌河段冲刷已趋于平衡,而宜都河段目前处于剧烈冲刷期,枝江河段尚未达到剧烈冲刷阶段;未来宜昌河段仍会有一定的冲刷,但是幅度不大,宜都河段将会继续冲刷,逐渐达到冲淤平衡,而枝江河段逐渐进入剧烈冲刷阶段.     

Hydrological Changes of the Irtysh River and the Possible Causes

Hydrological changes of the Irtysh River were analyzed concerning the changes of annual runoff and its distribution features within a year measured by coefficient of variation and concentration degree. Abrupt changes were detected by the heuristic segmentation method. Possible causes of the hydrological changes were investigated considering climate changes and human activities (especially the reservoir operation). The Mann-Kendall method was applied to estimate whether the temperature and precipitation was changed. The increased precipitation in winter may increase the runoff of April. The increased temperature and the decreased precipitation in the flood season may decrease the runoff. At the middle reaches, the impact of the reservoirs at the upper reaches is significant and may be the main factor leading to the abrupt decreases in annual runoff and its intra-annual variability and concentration. The increased water surface area of the reservoirs aggravates the evaporation and leads to annual runoff reduction. The reservoirs regulate runoff by storing water in the flood season and releasing water in the dry season. While at the lower reaches, the annual runoff remained steady and its intra-annual variation and concentration were reduced gradually because the impact of the reservoirs is relative small and the climatic impact may be more relevant.     

Development of a compact filament-discharge multi-cusp H- ion source

A 14 MeV medical cyclotron with the external ion source has been designed and is being constructed at China Institute of Atomic Energy. The H(-) ion will be accelerated by this machine and the proton beam will be extracted by carbon strippers in dual opposite direction. The compact multi-cusp H(-) ion source has been developed for the cyclotron. The 79.5 mm long ion source is 48 mm in diameter, which is consisting of a special shape filament, ten columns of permanent magnets providing a multi-cusp field, and a three-electrode extraction system. So far, the 3 mA∕25 keV H(-) beam with an emittance of 0.3 π mm mrad has been obtained from the ion source. The paper gives the design details and the beam test results. Further experimental study is under way and an extracted beam of 5 mA is expected.     

An automated vision based on-line novel percept detection method for a mobile robot

It is generally agreed that learning, either supervised or unsupervised, can provide the best possible specification of known classes and offer inference for outlier detection by a dissimilarity threshold from the nominal feature space. Novel percept detection can take a step further by investigating whether these outliers form new dense clusters in both the feature space and the image space. By defining a novel percept to be a pattern group that has not been seen before in the feature space and the image space, in this paper, a non-conventional approach is proposed for multiple-novel-percept detection problem in robotic applications. Based on a computer vision system inspired loosely by neurobiological evidence, our approach can work in near real time for highly sparse high-dimensional feature vectors extracted from image patches while maintaining robustness to image transformations. Experiments conducted in an indoor environment and an outdoor environment demonstrate the efficacy of our method.     

Optimal Packing Configuration Design with Finite-Circle Method

The Finite-circle Method (FCM) is further developed to solve 2D and 3D packing optimization problems with system compactness and moment of inertia constraints here. Instead of using the real geometrical shape as in existing solutions, we approximate the components and the design domain with circles of variant radii. Such approximation makes it possible to transform the original problem into a basic packing problem of FCM approximated components. Meanwhile, the overlapping between different components can be easily avoided by limiting the distance between corresponding circles in terms of their radii. With this formulation, the FCM provides a general and systematic approach and makes gradient-based optimization algorithms applicable. Furthermore, FCM has been extended to 3D packing problems by simply replacing circles with spheres in this paper. Several examples designing the compactness and moment of inertia of the component systems are presented to show the effect of FCM.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Node trust evaluation in mobile ad hoc networks based on multi-dimensional fuzzy and Markov SCGM(1, 1) model

Due to the nature of distribution and self-organization, mobile ad hoc networks rely on cooperation between nodes to transfer information. One of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next potential cooperative nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All of the above make it difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition with feedback, SCGM(1, 1) model and Markov chain, we present a pattern of prediction making. The analysis and experimental computation show that this scheme is efficient in trust prediction for ad hoc networks.     

2D representation of facial surfaces for multi-pose 3D face recognition

The increasing availability of 3D facial data offers the potential to overcome the intrinsic difficulties faced by conventional face recognition using 2D images. Instead of extending 2D recognition algorithms for 3D purpose, this letter proposes a novel strategy for 3D face recognition from the perspective of representing each 3D facial surface with a 2D attribute image and taking the advantage of the advances in 2D face recognition. In our approach, each 3D facial surface is mapped homeomorphically onto a 2D lattice, where the value at each site is an attribute that represents the local 3D geometrical or textural properties on the surface, therefore invariant to pose changes. This lattice is then interpolated to generate a 2D attribute image. 3D face recognition can be achieved by applying the traditional 2D face recognition techniques to obtained attribute images. In this study, we chose the pose invariant local mean curvature calculated at each vertex on the 3D facial surface to construct the 2D attribute image and adopted the eigenface algorithm for attribute image recognition. We compared our approach to state-of-the-art 3D face recognition algorithms in the FRGC (Version 2.0), GavabDB and NPU3D database. Our results show that the proposed approach has improved the robustness to head pose variation and can produce more accurate 3D multi-pose face recognition.     

A parallel matrix factorization based recommender by alternating stochastic gradient decent

Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high prediction accuracy and scalability. Most of the current MF based recommenders, however, are serial, which prevent them sharing the efficiency brought by the rapid progress in parallel programming techniques. Aiming at parallelizing the CF recommender based on Regularized Matrix Factorization (RMF), we first carry out the theoretical analysis on the parameter updating process of RMF, whereby we can figure out that the main obstacle preventing the model from parallelism is the inter-dependence between item and user features. To remove the inter-dependence among parameters, we apply the Alternating Stochastic Gradient Solver (ASGD) solver to deal with the parameter training process. On this basis, we subsequently propose the parallel RMF (P-RMF) model, of which the training process can be parallelized through simultaneously training different user/item features. Experiments on two large, real datasets illustrate that our P-RMF model can provide a faster solution to CF problem when compared to the original RMF and another parallel MF based recommender.     

适应动态安全需求的实时任务调度算法研究

现有的实时容错调度算法没有将实时任务的动态安全需求与其可调度性结合起来考虑.针对这一问题,文章展开以下研究工作:基于安全分级思想,构建了一个适应动态安全需求的实时调度模型,该模型详细地描述了实时任务、系统安全服务、任务容错等实时调度过程涉及的关键要素.以此模型为基础,提出了一种自适应实时容错调度算法(AFTS),该算法支持优先级抢占式调度策略,以牺牲普通任务的运行为代价来保证关键任务的可调度性,并采用主副本备份技术实现了关键任务的容错功能.当系统安全级别被动态调整时,该算法能够为实时任务选择满足当前安全需求的最优安全策略.仿真实验表明,文中提出的算法与同类算法相比,在系统动态安全需求的适应性,以及关键任务的可调度性和容错能力等方面有较好的表现.