门限自回归模型在海洋冰情预测中的应用

给出了建立门限自回归模型（ＴＡＲ） 的一套实用方法, 用作者提出的改进遗传算法可同时优化门限值和自回归系数,成功地解决了ＴＡＲ建模过程所涉及的大量复杂寻优工作这一难题。实例计算的结果说明了, 这套方法在海洋冰情预测中是可行的和有效的, 在各种自然灾害非线性时序动态预测中具有重要的理论意义和广泛的实用价值。     

水环境质量评价的投影寻踪模型及其应用

将投影寻踪分类模型应用于水环境质量综合评价中,利用基于实码的加速遗传算法来优化投影方向,将多维数据指标转换成低维子空间,通过寻求最优投影方向来计算投影函数值,从而根据投影值的大小进行水环境质量评价。结合实例进行了应用,结果较令人满意,为水环境质量评价提供了一条新的方法和途径。     

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.     

变尺度混沌-遗传算法在复杂河流水质模型参数优化中的应用

将变尺度混沌-遗传算法(MSCGA)应用于复杂河流水质模型参数优化.采用湘江衡阳段水质监测资料,以二维河流水质数学模型反演结果的均方误差为适应度函数,估计横向扩散系数Dx、纵向弥散系数Dy和污染物衰减系数κ.数值实验结果表明,MSCGA寻优过程具有明显的分级特征,级级收敛;在同样的条件下,MSCGA的收敛速度较快,为遗传算法(GA)的1.36倍;同时,MSCGA克服了GA早熟收敛的问题,其最优适应度函数值为7.6×10-4,而GA的最优适应度函数值9.6×10-4.将MSCGA应用于研究河段,求得Dx、Dy分别为0.1335、0.0011,BOD5、As、Cr的衰减系数κ分别为0.0229、0.0100、0.0107.     

基因工程菌E.coli BL21-NiCoT吸附镍的性能研究

以表达Staphylococcus aureus ATCC6538镍钴转运酶NiCoT基因的基因工程菌E.coli BL21-NiCoT作为生物吸附剂处理含镍废水.结果表明:基因工程菌在pH为4～9时有比较好的吸附效果,30 min就达到了吸附平衡;基因工程菌对Ni2 的富集容量比原始宿主菌有很大的提高,最大平衡富集量从3.76 mg/g增加到11.33 mg/g,增幅达3倍多,溶液中Ni2 的最大去除率也从原来的35.62%增加到91.23%;Cu2 、Cr6 、Zn2 等的存在对吸附没有很大的影响.镍进入细胞内后与羟基和酰胺基团发生结合,蛋白类物质和含羟基类物质在细胞内富集镍的过程中起到重要作用;基因工程菌转接10、20、30、40、50次,重组质粒保持良好的结构稳定性,基因工程菌对镍的富集能力也具有较好的稳定性.     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.