受回水影响的矩形渠道流量推算方法分析

矩形渠道断面规则、稳定,一般情况下,其水位-流量关系曲线单一.实际测验过程中发现,矩形渠道受下游回水影响,测验断面处实测水位流量点散乱、关系曲线复杂,线上推流精度降低.本研究基于曼宁公式、水流运动能量方程推导出规则矩形渠道流量推算的公式,应用该公式推算渠道流量可以解决水位-流量关系曲线单一导致的线上推流精度降低问题.将本研究提出利用上下游比降、水位和糙率推算断面流量的方法并应用在某水文站的矩形渠道流量推算中,结果表明,实测流量Q和水位、糙率和水面比降的复合项P拟合度较好,具有一定的实际应用价值.     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

一种基于特征点匹配的图像拼接方法研究

针对图像间因具有旋转及光线强度差异等现象而导致的拼接效果不佳及拼接速度慢的问题,提出一种基于特征点的配准算法;在特征点提取阶段,尺度不变的特征变换方法 (SIFT)具有对图像尺度缩放、旋转、放射变换以及亮度变化保持不变的优点,文章采用了改进的SIFT特征点提取算法;在特征点匹配阶段,采用改进的RANSAC算法对特征点匹配对提纯;最后用加权平均法实现拼接图像的融合;实验证明,该算法有效提高了图像拼接的效率和准确性,拼接精度可以达到亚像素级。     

Blind estimation of the PN sequence in lower SNR DS-SS signals with residual carrier

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation in low signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS, DS) signals with residual carrier. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. Then, an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Furthermore, a DPLL is used to deal with the estimated PN sequence with residual carrier, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can effectively realize the PN sequence estimation from the input DS signals with residual carrier in lower SNR.     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

Register allocation for write activity minimization on non-volatile main memory for embedded systems

Non-volatile memories are good candidates for DRAM replacement as main memory in embedded systems and they have many desirable characteristics. Nevertheless, the disadvantages of non-volatile memory co-exist with its advantages. First, the lifetime of some of the non-volatile memories is limited by the number of erase operations. Second, read and write operations have asymmetric speed or power consumption in non-volatile memory. This paper focuses on the embedded systems using non-volatile memory as main memory. We propose register allocation technique with re-computation to reduce the number of store instructions. When non-volatile memory is applied as the main memory, reducing store instructions will reduce write activities on non-volatile memory. To re-compute the spills effectively during register allocation, a novel potential spill selection strategy is proposed. During this process, live range splitting is utilized to split certain long live ranges such that they are more likely to be assigned into registers. In addition, techniques for re-computation overhead reduction is proposed on systems with multiple functional units. With the proposed approach, the lifetime of non-volatile memory is extended accordingly. The experimental results demonstrate that the proposed technique can efficiently reduce the number of store instructions on systems with non-volatile memory by 33% on average.     

Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

Tracking Area (TA) design is one of the key tasks in location management of Long Term Evolution (LTE) networks. TA enables to trace and page User Equipments (UEs). As UEs distribution and mobility patterns change over time, TA design may have to undergo revisions. For revising the TA design, the cells to be reconfigured typically have to be temporary torn down. Consequently, this will result in service interruption and “cost”. There is always a trade-off between the performance in terms of the overall signaling overhead of the network and the reconfiguration cost. In this paper, we model this trade-off as a bi-objective optimization problem to which the solutions are characterized by Pareto-optimality. Solving the problem delivers a host of potential trade-offs among which the selection can be based on the preferences of a decision maker. An integer programming model has been developed and applied to the problem. Solving the integer programming model for various cost budget levels leads to an exact scheme for Pareto-optimization. In order to deliver Pareto-optimal solutions for large networks in one single run, a Genetic Algorithm (GA) embedded with Local Search (LS) is applied. Unlike many commonly adopted approaches in multi-objective optimization, our algorithm does not consider any weighted combination of the objectives. Comprehensive numerical results are presented in this study, using large-scale realistic or real-life network scenarios. The experiments demonstrate the effectiveness of the proposed approach.     

Integer linear programming optimization of joint RRM policies for heterogeneous wireless systems

Wireless systems will be characterized by the coexistence of heterogeneous Radio Access Technologies (RATs) with different, but also complementary, performance and technical characteristics. These heterogeneous wireless networks will provide network operators the possibility to efficiently and coordinately use the heterogeneous radio resources, for which novel Joint Radio Resource Management (JRRM) policies need to be designed. In this context, this work proposes and evaluates a JRRM policy that simultaneously determines for each user an adequate combination of RAT and number of radio resources within such RAT to guarantee the user/service QoS requirements, and efficiently distribute the radio resources considering a user fairness approach aimed at maximizing the system capacity. To this aim, the JRRM algorithm, which takes into account the discrete nature of radio resources, is based on integer linear programming optimization mechanisms.