Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量，分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L，豆饼粉25．638g／L和K。HPO。3．265g／L时，预测发酵活菌最大生物量为7．079×10^8CFU／mL，实测量为7．194×10^8CFU／mL；人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L时，预测最大值为7．199×10^8CFU／mL，实测量为7．244×10。CFU／mL；最终确定培养基配方：玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L，CaCO3为3．000g／L，MgSO4·7H2O和NaCl均为0．200g／L；优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明，在阿特拉津降解菌DNS32发酵培养基组分优化方面，响应面法和基于人工神经网络的遗传算法都是可行的，基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

遗传投影寻踪回归在城市环境质量评价中的应用

引入投影降维的思想,将遗传投影寻踪与回归分析技术运用到城市环境质量评价中。将此技术与神经网络方法进行实例比较,投影寻踪回归方法不但可以合理地作出环境质量的综合评价,而且消除了神经网络方法中类别判断不够精确的影响。     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

WEP的安全技术分析及对策

在对无线局域网WLAN标准IEEE80 2 .11的WEP描述的基础上 ,笔者深入研究了WEP协议的设计上和实际应用中存在的密钥序列重复使用、消息认证和完整性等严重安全漏洞 ;详细分析了标准无线局域网中WEP协议解决方案 ;提出了WEP的改进算法WEP2 ,WEP2将IV大小增加到 12 8bit,以增强抗攻击能力 ;分析了AES解决方案的可行性 ;在密钥再生与分配方面 ,提出在MAC层实现经认证的密钥交换 ,用来改善WEP的安全脆弱性等解决措施。最后 ,还对今后的研究方向作出进一步的展望     

安徽省农业经济发展水平综合评价研究

根据2005年《安徽统计年鉴》的统计资料，运用TOPSIS和聚类分析方法分别对安徽省17个地级市农业经济发展水平进行了综合评价和分类，分析了各地级市在安徽农业经济发展中的地位，旨在为其它地区进行农业经济综合评价提供参考。     

深水平台施工过程自然风险定量评估

在分析现有风险理论的基础上,结合海洋施工实际,提出了一种对海洋施工自然风险进行定量评估的模型。根据目标系统各风险事件与影响因素之间存在高度非线性复杂映射的特点,提出了利用BP神经网络拟合方法,对主要风险事件发生概率进行量化的模型。针对传统的梯度下降优化算法收敛速度慢的特点,采用可避免计算Hesse矩阵的Levenberg-Marquardt算法来训练神经网络。这一模型可以模拟专家评价,并准确地按照专家的评定法则进行估算,具有一定的通用性。     

交通系统智能决策的算法研究

遗传算法是近年来迅速发展起来的一种全新的随机搜索与优化算法，但由于其自身固有的缺陷，通常优化过程的收敛速度较慢，局部搜索能力不足，而且算法稳定性较差。而蚁群算法广泛应用在旅行商问题计算中，目前是较好的求解最短路由问题的算法之一。就其自身来说有很多优点，如正反馈性、鲁棒性和智能性，但是在寻优过程中容易陷入局部最佳的缺点。针对上述情况，将遗产算法与蚁群算法相结合，用于实际交通系统寻找最优路径的问题中，并定义了目标函数，以路径可靠性和路径长度为优化目标，寻找最合适的救灾路线。最后通过实际计算结果的对比验证，说明了该方法的有效性。