雨水泵站水泵机组启停优化

为解决雨水泵站运行过程中存在的水泵频繁启停问题,提出一种水泵机组启停优化方法.该方法通过分析泵站的设计参数和水泵机组运行原理,建立启泵水位优化模型,并运用暴雨洪水管理模型(storm water management model,SWMM)和粒子群优化算法(particle swarm optimization algorithm,PSO)对模型求解,最终得到水泵机组的最优启泵水位.通过实例将优化方法与传统的人工调试方法进行对比,验证了其可行性.研究结果表明:采用优化方法得到的启泵水位可使机组启停次数达到最小,实现了较好的水泵启停效果.同时优化方法也避免了复杂的人工调试过程,得出的优化结果为雨水泵站启泵水位的选取提供参考.     

快递企业人员调配问题的建模与求解方法

针对快递企业配送中心人员调配问题,提出了在服务于同类客户与不同类客户两种条件下,用时间序列季节系数法预测来件量,并分析快件处理和配送过程中的人员、车辆因素的方法.该方法对同类客户调整配送中心快件处理人员与分配人员的数量,以此达到极小化快件剩余量的目标,对不同类客户调整配送人员的数量,以此达到极小化配送中心成本的目标,并建立了数学模型.利用粒子群算法对模型进行求解,通过实例进行仿真试验,从而得到配送中心人员调配的最佳方式,以此指导企业合理运营.     

基于新型Canny算法雷达距离图像边缘检测算法

针对激光雷达距离像的噪声滤波和边缘检测问题,提出了一种结合改进环圈滤波算法和自适应Canny算法的距离像边缘检测算法.通过改进环圈滤波对距离像的噪声进行抑制,在滤除噪声的同时保留了图像的细节特征信息.从梯度幅值计算、非极大值抑制和阀值自动选择3个方面对传统Canny算法进行改进,克服了噪声影响和边缘检测模糊等缺点.实验结果表明,该算法能够可靠的对距离图像进行边缘检测,并且检测结果的信息熵和标准差指标数据优于传统的Sobel算法和Laplace算法,能够较好地满足激光雷达距离像边缘检测的实际需要.     

Research on phaseless near-field antenna measurement

A method of phaseless near-field measurement is studied which can reconstruct the far-field characteristics of the antenna by analyzing the near-field amplitude-only data. The method includes a dual-plane scanning strategy which lets the linear polarization probe collect the amplitude-only data on the two scanning planes respectively. The phase of each sampling points at one of the scanning planes can be retrieved using the iterative fourier transform algorithm. Then the far-field properties of the antenna are obtained by applying the near-field/far-field transformation technique. Theoretical research and simulations show that the phaseless near-field measurement strategy can reconstruct the far-field pattern effectively and that a better choice of the initial iterate phase can help to improve the convergence rate and the convergence precision of the method.     

Probabilistic and interval reliability design of the composite pressure vessel

Considering the randomness or interval character of physical parameters and applied loads of composite pressure vessels (COPV), the COPV reliability-analyzing model is built. And then the computational expressions for the mean value, standard deviation and deviation of fiber stress are deduced by the random factor method. The probabilistic and interval reliability designs on COPV are implemented by utilizing the probabilistic method and interval method combined with the improved particle swarm optimization (PSO) algorithm, respectively. The influence of fluctuation of structure uncertain parameters on COPV design thickness is inspected. Finally, examples are given to demonstrate that both probabilistic and interval reliability methods can satisfy the safety requirement and both are of higher rationality than the traditional safety factor method. And probabilistic method has a lower relative error but a higher computational complexity contrasted with the interval method.     

基于差分进化算法的供热节能控制系统

针对公共建筑集中供热系统能耗高、自动调节和实时监控难度大的问题,将数字温度传感器、芯片控制技术及CAN总线技术结合到一起,设计了一种基于差分进化算法的神经网络控制的公共建筑集中供热系统.系统具有降低遗传算法复杂性、快速收敛的优势,且自适应能力较强,能够实现供热流量自动调节和网络实时监控.为了验证该系统的节能效果,与传统节能控制系统的供热消耗进行了试验比较,结果表明,该系统最低平均节能10.1%,最高节能16.3%,节能效果更好.     

New soft morphological parameterized symmetric logarithmic image processing filtering algorithm

Synthetic Aperture Radar (SAR) imaging has the inherent random speckle noise, which seriously affects the quality of the SAR image, and it is very important to study the suppression of the speckle noise. In view of the fact that the traditional spot noise suppression method could not remove speckle noise while protecting the image texture information, a novel filtering algorithm is proposed based on the soft morphological and parameterized symmetric logarithmic image processing model. The proposed algorithm combines the flexibility of the order-statistic soft morphological operations and the characteristic of the parameterized symmetric logarithmic image processing model which establishes a parameterized symmetric structure processing part of the image, which can suppress speckle noise while protecting the texture information on the image. In order to validate the despecking performance of the proposed algorithm, we compare the effect of the filter with some of the existing filtering algorithms, and use some of the image evaluation parameters of the suppress speckle noise image to evaluate the performance of the SMPSLIP filtering algorithm. Experimental results show the effectiveness of this algorithm.     

Radio frequency interference suppression algorithm in the spatial domian for high-frequency radars

Radio frequency interference suppression is a very important issue in applications of high-frequency surface wave radar. This paper proposes a new interference suppression algorithm which makes use of RF interference azimuth information on the basis of learning common RF interference suppression algorithms and spatial spectrum estimation. First, we apply the MUSIC algorithm to estimate the interference azimuth and obtains the interference's signal space after the first Fourier transform; next, we use space decomposition to decompose the radar echo and obtain interference components; last, we subtract radio frequency interference components from the original signals. This algorithm can do great amount of RF interference suppression in actual data and get good results.     

Novel Apriori-Based Multi-Label Learning Algorithm by Exploiting Coupled Label Relationship

It is a key challenge to exploit the label coupling relationship in multi-label classification (MLC) problems. Most previous work focused on label pairwise relations, in which generally only global statistical information is used to analyze the coupled label relationship. In this work, firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples, which combines global and local statistical information, and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels, which can exploit the label coupling relations more accurately and comprehensively. The experimental results on text, biology and audio datasets shown that, compared with the state-of-the-art algorithm, the proposed algorithm can obtain better performance on 5 common criteria.     

Mathematical Advancement of General Chlorine Bulk Decay Model

An accurate chlorine bulk decay model is needed to ensure that potable water meets the microbial and the chemical safeties at the treatment plant and throughout the distribution. Among the mathematical models available,the general second-order chlorine bulk decay model( GBDM) is the most fundamentally sound.Application of the GBDM,however,has been hindered by its numerous fictive parameters and lack of an analytical solution. This theoretical work removes the two obstacles. The GBDM is solved through transformation and integration. The analytical solution provides deep insights into the GBDM and facilitates the parameterization and sensitivity analysis. The background natural organic matter( NOM) is characterized with the probabilistic distribution of functional groups. It reveals that the mean of the function group distribution is correlated with the initial chlorine decay rate coefficient( κ_0). A simple formula is developed to determine κ_0 directly from the initial chlorine decay. The theoretical treatment reduces the fictive parameters to a minimum. For the common lognormal distribution,the GBDM needs only three parameters,well defined as initial chlorine demand X_0,median rate coefficient km,and heterogeneity index σ. For more complicated scenarios,composite distributions are constructed through superposition of individual distributions. A highlighted example is to predict chlorine decay in blends of different waters. With the theoretical and mathematical advancement here,the GBDM can be applied effectively to any reactive background matter in any reaction systems.