铸造闸门在苏丹麦洛维灌溉工程的应用

介绍苏丹麦洛维大坝工程灌溉渠滑动闸门的设计经验,属小孔口高水头滑动闸门设计,门槽尺寸较小,动水启闭.达到闸门关闭后止水效果好、启闭过程中不发生射流,安装方便的要求.     

基于SAR模型和蒙特卡罗法的防洪调度风险分析

应用一阶季节性自回归模型生成多场入库洪水过程,考虑泥沙淤积状况和泄流能力不确定性影响,在给定汛限水位和调洪规则下对模拟洪水进行调洪演算,统计得到水库防洪风险率。实例分析结果表明：水文因素的随机性、库容与水位关系的不确定性是万家寨水库防洪风险的主要影响因素,泄流能力不确定因素对水库泄洪影响不明显;同时得出了各汛限水位方案下水库的防洪风险率,为万家寨水库前汛期动态汛限水位选择提供了一定的参考依据。     

三峡水库175 m试验性蓄水过程水文泥沙分析

为掌握三峡水库175 m试验性蓄水过程中库区水位、流量、含沙量变化过程,通过三峡库区寸滩、清溪场、万县、朱沱（三）水文站实测的水位、流量、含沙量、水面比降资料,分析了蓄水过程中三峡库区沿程水面比降、库区水文站的水位流量关系、含沙量变化、库区冲淤变化等。分析结果为认识掌握三峡水库175 m蓄水后库区水文泥沙的变化规律积累了资料,对探索三峡库区水文泥沙的监测方式和分析研究将具有积极作用。     

Joint video adaptation and erasure code for video broadcast in wireless networks

Advances in wireless access technologies such as WiMax and LTE have accelerated the development of wireless video applications.Though existing erasure codes are designed for wireless data broadcasting,their potential can only be realized for wireless video broadcasting if the distortions associated with video packets and their complex decoding dependency are taken into consideration.In this paper,we propose the greedy video erasure code(GVEC)to minimize average video distortion by accounting for the temporal scalable decoding dependency among video frames e.g.I,P and B frames in MPEG.GVEC is a greedy algorithm and sufers from high computational complexity.For practical implementation,we have designed the joint video adaptation and erasure code(JVEC)with lower complexity,which can generate erasure codes on the fly to guarantee the decoding of important video frames.Simulation results show that the performance of JVEC is very close to that of GVEC,and is superior to that of existing erasure codes.Our results also show the existence of a performance tradeof between video distortion and video packet throughput.     

Poisson image fusion based on Markov random field fusion model

In this paper, we present a gradient domain image fusion framework based on the Markov Random Field (MRF) fusion model. In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients. To fuse the structures in the gradient domain, an effective MRF-based fusion model is designed based on both the per-pixel fusion rule defined by the local saliency and also the smoothness constraints over the fusion weights, which is optimized by graph cut algorithm. This MRF-based fusion model enables the accurate estimation of region-based fusion weights for the salient objects or structures. We apply this method to the applications of multi-sensor image fusion, including infrared and visible image fusion, multi-focus image fusion and medical image fusion. Extensive experiments and comparisons show that the proposed fusion model is able to better fuse the multi-sensor images and produces high-quality fusion results compared with the other state-of-the-art methods.     

A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem

Solving a task scheduling problem is a key challenge for automatic test technology to improve throughput, reduce test time, and operate the necessary instruments at their maximum capacity. Therefore, this paper attempts to solve the automatic test task scheduling problem (TTSP) with the objectives of minimizing the maximal test completion time (makespan) and the mean workload of the instruments. In this paper, the formal formulation and the constraints of the TTSP are established to describe this problem. Then, a new encoding method called the integrated encoding scheme (IES) is proposed. This encoding scheme is able to transform a combinatorial optimization problem into a continuous optimization problem, thus improving the encoding efficiency and reducing the complexity of the genetic manipulations. More importantly, because the TTSP has many local optima, a chaotic non-dominated sorting genetic algorithm (CNSGA) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. This approach introduces a chaotic initial population, a crossover operator, and a mutation operator into the non-dominated sorting genetic algorithm II (NSGA-II) to enhance the local searching ability. Both the logistic map and the cat map are used to design the chaotic operators, and their performances are compared. To identify a good approach for hybridizing NSGA-II and chaos, and indicate the effectiveness of IES, several experiments are performed based on the following: (1) a small-scale TTSP and a large-scale TTSP in real-world applications and (2) a TTSP used in other research. Computational simulations and comparisons show that CNSGA improves the local searching ability and is suitable for solving the TTSP.     

A genetic algorithm for finding a path subject to two constraints

Multi-constrained routing (MCR) aims to find the feasible path in the network that satisfies multiple independent constraints, it is usually used for routing multimedia traffic with quality-of-service (QoS) guarantees. It is well known that MCR is NP-complete. Heuristic and approximate algorithms for MCR are not effective in dynamic network environment for real-time applications when the state information of the network is out of date. This paper presents a genetic algorithm to solve the MCR problem subject to transmission delay and transmission success ratio. Three key design problems are investigated for this new algorithm, i.e., how to encode the problem in genetic representation, how to avoid the illegal chromosomes in the process of population initialization and genetic operation, and how to design effective genetic operator. We propose the gene structure (GS) to deal with the first problem, and the gene structure algorithm (GSA) to generate the GS. Based on the GS, we provide the heuristic chromosome initialization and mutation operator to solve the last two problems. Computer simulations show that the proposed GA exhibits much faster computation speed so as to satisfy the real-time requirement, and much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network scales and topologies). Furthermore, simulation results show that the proposed GA is effective and efficient in dynamic network environment.     

Trust fraud: A crucial challenge for China’s e-commerce market

Currently, China’s e-commerce market is growing at an unprecedented pace, however, it is faced with many challenges, among which the trust fraud problem is the biggest issue. In this article, we use Taobao as an example and conduct a thorough investigation of the trust fraud phenomenon in China’s e-commerce market. We present the development history of trust fraud, summarize its unique characteristics, and explore the reasons why so many sellers commit fraud. We further propose a dynamic time decay trust model that aims to deter trust fraud by raising its cost and promote the growth of small and medium-sized sellers. The model utilizes detailed seller ratings as the data source, and incorporates a transaction amount weight, a time decay coefficient, and three trust factors in the calculation of trust. We test the model on real transaction data from Taobao, and the experimental results verify its effectiveness. Our proposed trust model yields a practical approach to online trust management not only in the Taobao market but also for other e-commerce platforms.     

Sliding-mode observers for nonlinear systems with unknown inputs and measurement noise

This paper deals with the design of observers for Lipschitz nonlinear systems with not only unknown inputs but also measurement noise when the observer matching condition is not satisfied. First, an augmented vector is introduced to construct an augmented system, and an auxiliary output vector is constructed such that the observer matching condition is satisfied and then a high-gain sliding mode observer is considered to get the exact estimates of both the auxiliary outputs and their derivatives in a finite time. Second, for nonlinear system with both unknown inputs and measurement noise, an adaptive robust sliding mode observer is developed to asymptotically estimate the system’s states, and then an unknown input and measurement noise reconstruction method is proposed. Finally, a numerical simulation example is given to illustrate the effectiveness of the proposed methods.     

Data filtering based least squares algorithms for multivariable CARAR-like systems

This paper focuses on the identification problem of multivariable controlled autoregressive autoregressive (CARAR-like) systems. The corresponding identification model contains a parameter vector and a parameter matrix, and thus the conventional least squares methods cannot be applied to directly estimate the parameters of the systems. By using the hierarchical identification principle, this paper presents a hierarchical generalized least squares algorithm and a filtering based hierarchical least squares algorithm for the multivariable CARAR-like systems. The simulation results show that the two hierarchical least squares algorithms are effective.