基于MAS的制导雷达网络化抗干扰作战体系结构研究

通过对地空导弹网络化抗干扰作战的分析,结合DAI(Distributed Artificial Intelligence,分布式人工智能)技术中的MAS(Multi-Agent System,多智能体系统)技术,建立了基于MAS的制导雷达网络化抗干扰作战系统,并给出了该系统的Agent组成框架,定义并说明了各Agent的功能及相互关系.     

A roadmap for knowledge exchange and mobilization research in conservation and natural resource management

Scholars across all disciplines have long been interested in how knowledge moves within and beyond their community of peers. Rapid environmental changes and calls for sustainable management practices mean the best knowledge possible is needed to inform decisions, policies, and practices to protect biodiversity and sustainably manage vulnerable natural resources. Although the conservation literature on knowledge exchange (KE) and knowledge mobilization (KM) has grown in recent years, much of it is based on context‐specific case studies. This presents a challenge for learning cumulative lessons from KE and KM research and thus effectively using knowledge in conservation and natural resources management. Although continued research on the gap between knowledge and action is valuable, overarching conceptual frameworks are now needed to enable summaries and comparisons across diverse KE‐KM research. We propose a knowledge‐action framework that provides a conceptual roadmap for future research and practice in KE/KM with the aim of synthesizing lessons learned from contextual case studies and guiding the development and testing of hypotheses in this domain. Our knowledge‐action framework has 3 elements that occur at multiple levels and scales: knowledge production (e.g., academia and government), knowledge mediation (e.g., knowledge networks, actors, relational dimension, and contextual dimension), and knowledge‐based action (e.g., instrumental, symbolic, and conceptual). The framework integrates concepts from the sociology of science in particular, and serves as a guide to further comprehensive understanding of knowledge exchange and mobilization in conservation and sustainable natural resource management.     

A Monitoring Network Design Procedure for Three-Dimensional (3D) Groundwater Contaminant Source Identification

Finding the location and concentration of contaminant sources is an important step in groundwater remediation and management. This discovery typically requires the solution of an inverse problem. This inverse problem can be formulated as an optimization problem where the objective function is the sum of the square of the errors between the observed and predicted values of contaminant concentration at the observation wells. Studies show that the source identification accuracy is dependent on the observation locations (i.e., network geometry) and frequency of sampling; thus, finding a set of optimal monitoring well locations is very important for characterizing the source. The objective of this study is to propose a sensitivity-based method for optimal placement of monitoring wells by incorporating two uncertainties: the source location and hydraulic conductivity. An optimality metric called D-optimality in combination with a distance metric, which tends to make monitoring locations as far apart from each other as possible, is developed for finding optimal monitoring well locations for source identification. To address uncertainty in hydraulic conductivity, an integration method of multiple well designs is proposed based on multiple hydraulic conductivity realizations. Genetic algorithm is used as a search technique for this discrete combinatorial optimization problem. This procedure was applied to a hypothetical problem based on the well-known Borden Site data in Canada. The results show that the criterion-based selection proposed in this paper provides improved source identification performance when compared to uniformly distributed placement of wells.     

基于人工神经网络和遗传算法研究锰过氧化酶脱色甲基橙

基于锰过氧化物酶(MnP)氧化脱色偶氮类染料的原理,实验研究MnP对甲基橙的脱色工艺,采用人工神经网络(ANN)和遗传算法(GA)建立脱色模型并优化工艺。建立的ANN模型的误差、相关系数、均方根误差和绝对平均偏差分别为0.0009、0.9971、1.21和6.82,模型有效且能够用于预测和工艺优化。采用GA对ANN模型进行数值寻优,得到的最佳工艺条件为酶液量0.6 mL,Mn2+浓度4 mmol/L,H2O2浓度0.49 mmol/L。该条件下脱色率达到(90.74±0.59)%。ANN耦合GA有效地建立了锰过氧化物酶脱色甲基橙的模型,并优化了工艺参数,为甲基橙脱色的研究提供一定参考。     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

有热源巷道风流温度分布非稳定模型及数值计算

针对矿井有热源巷道中风流温度分布非稳定计算问题，通过通风巷道存在放热源-对流-扩散-热交换的非稳定温度传播方程，建立一维有限元数值模拟求解方法的数学模型，讨论按放热强度（2类热流量条件）的热源项处理算法，给出有热源通风巷道风流初始温度均匀分布与非均匀分布的定解条件，并编写计算机仿真程序进行求解计算。结果表明:在起点27 ℃-热源300 kW和起点32℃-热源100 kW的不同边界条件和热源强度下，有热源通风巷道中温度分布的非稳定传播过程；数值解收敛准则用Peclet数来衡量，取Pe<2作为收敛条件，程序中根据Peclet数自动加以判断，算例中剖分单元长度1.5 m，模拟时间步长取2 s不发生振荡。实现了巷道中风流各点的温度和温度分布随时间的变化过程的描述，为矿井系统多设备热源散热计算及热害防治设计提供基础算法。     

辽河流域水环境监测网络优化技术研究

以辽河流域2009—2011年监测数据为基础,针对北方季节性河流,在制定了水环境监测网络优化原则的前提下,分别运用最优分割法、变异系数与水质类别相结合方法、连续3年未检出判断法进行了辽河流域水环境质量监测断面、监测频次和监测项目的优化。结果表明,辽河流域优化后的监测断面为18个,比优化前减少8个。在监测频次上,干旱少雨的月份中,1、5、10月应保持每个月2次的监测频次,2—4月、11—12月每月监测1次;湿润多雨的6—9月只监测1~2次即可,全年监测频次分布更合理。监测项目分为必测项目和选测项目,必测项目为12~20项,选测项目为3~11项。     

Canada's Model Forests 20 years on: towards forest and community sustainability?

We review how Canadian Model Forests pursued forest and community sustainability over the course of two decades (1992–2012). Given its roots in the forest industry and forest science, Model Forest programming initially faced some challenges in pursuing the socio‐economic dimensions of sustainable forest management (SFM) in order to fulfil mandated community sustainability objectives. This was due, in part, to how objectives, stakeholders, and expertise were brought together to develop SFM. The programme helped to define sustainability and the SFM paradigm, advance forest science and social research, and bring together a mix of usually adversarial partners in the name of innovation. Ultimately, the termination of federal programming was linked to high‐level policy shifts, yet difficulty in delivering on the socio‐economic dimensions of SFM during a period of forest sector and community crisis was also a factor.     

酚类化合物臭氧氧化速率的神经网络研究

酚类化合物(BP)是重要的工业原料或中间体,但工业废水含有的酚类化合物会对环境造成污染。为建立酚类化合物臭氧氧化速率的QSPR(quantitative structure-property relationship)预测模型,分析了23种酚的分子结构与臭氧氧化速率之间的相关关系,计算了这些酚的分子连接性指数和分子形状指数,优化筛选了连接性指数的1χ和2χ、分子形状指数的K1和K2共4种参数,将其作为BP神经网络的输入层变量,臭氧氧化速率作为输出层变量,采用4:2:1的网络结构,获得了令人满意的QSPR神经网络预测模型,模型总相关系数r为0.976,计算得到的臭氧氧化速率的预测值与实验值较为吻合,平均残差仅为0.05;为检验结构参数建立模型的普适性,同样方法建立对酚类化合物的辛醇-水分配系数的预测模型,模型总相关系数r达到0.993,辛醇-水分配系数的预测值与实验值吻合度较为理想,结果表明,本法建构的神经网络模型具有良好的稳健性和预测能力。