六株木测树法估测人工杉木林蓄积量的研究

以湖南省攸县黄丰桥林场杉木树种为研究对象,采用随机抽样方法布设样地110块,其中杉木有效观测样地97块.以距样地中心位置最近的6株木为对象,观测每株杉木的胸径、树高及最远杉木到样地中心的距离,以计算这6株杉木样圆的覆盖面积来估测样地蓄积量,并将估测结果与角规测树法进行对比分析.结果表明,在95%的可靠性下,平均胸径估测精度为90%,平均树高估测精度为88%,蓄积量估测精度达89%,估测结果与角规测树法所得结果十分接近,表明利用六株木法估测杉木蓄积量具有较好的效果.

应用于GreenLab模型构建的测树方法

GreenLab模型是目前研究最为深入的植物功能--结构模型,但传统的测树学方法难以满足GreenLab模型构建所需要的外观形态参数和生物量参数。因此,该文提出了一套能够满足GreenLab模型构建需求的测树方法。首先分析了GreenLab模型的参数需求,然后以欧美杨107为测量实例,详细探讨了树木的参数测量步骤以及测量过程中的关键问题,形成了一套科学而系统的参数测量方案。结果表明:该测量方案可以快捷有效地实现参数的测量,其测量结果可以满足GreenLab模型构建的需求。

谈角规调查应注意的几个问题

论述了角规操作中应注意的事项,对角规样地的布设,角规操作等影响调查质量的情况进行了说明。

Dendritic Cells Algorithm and Its Application to Nmap Portscan Detection

Dendritic Cells Algorithm （DCA） is a new development in Artificial Immune System （AIS）. It has various parameters, and as yet has not been ex- tensively tested. The general applicability of the al- gorithm to a variety of problems is d. The aim of this work is to demonstrate the feas^ility and ro- bustness of the algorithm, and the sensitivity to the change of various parameters in a series of experi- ments for Nmap portscan detection by using DCA. Experiment results show that the algorithm per- forms well on the task of detecting a ping based Nmap portscan. Sensitivity analysis is also per- formed. True positive rate is higher for the detec- tion of anomaly processes and false positive rate is lower for the detection of normal orocesses.

STUDY ON THE DETERMINATION OF TRACE BISMUTH(III) BY THIN-LAYER RESIN PHASE SPECTROPHOTOMETRY

In this paper, a new thin-layer ion-exchange resin phase analytical method is introduced. It is based on that, the bismuthous cation can associate with iodic anions, so as to formed an anion complex [BiI4-] in a strong acidic environments. This anion complex can also exchanges with a weaker anions on the surface active site of anion exchange resin, so that a [R+] [BiI4-] solid phase binary associational system is produced. Owing to the solid system is a great many dispersive particulates, it can be pressed to a thin-layer by press tools of the so called 搕hin-layer resin phase?or 搑esin phase? and using this solid association system spectrophotometry for the determination of trace metals. So it can increase the analytical sensitivity. This association system exhibits maximum absorbance at 460nm, and obeys Beer抯 law over the concentration range 0.01ug/ml^1.20ug/ml of bismuthous(III). It has a molar absorptivity of 7.1×105 [L/mol穋m]. It indicated the resin phase spectrophotometry is a sensitive analytical method for trace bismuthous. It is 18 times higher than routine aqueous spectrophotometry. The relative standard deviations is 1.82% (n=6) for the measurements of 0.5ug/ml Bi(III). The detection limit of Bismuthous(III) is 1.4×10-8mol/L. The method has applied to the analysis Bi(III) in environmental water samples.

DiagData： A Tool for Generation of Fuzzy Inference System

In this paper, it described the architecture of a tool called DiagData. This tool aims to use a large amount of data and information in the field of plant disease diagnostic to generate a disease predictive system. In this approach, techniques of data mining are used to extract knowledge from existing data. The data is extracted in the form of rules that are used in the development of a predictive intelligent system. Currently, the specification of these rules is built by an expert or data mining. When data mining on a large database is used, the number of generated rules is very complex too. The main goal of this work is minimize the rule generation time. The proposed tool, called DiagData, extracts knowledge automatically or semi-automatically from a database and uses it to build an intelligent system for disease prediction. In this work, the decision tree learning algorithm was used to generate the rules. A toolbox called Fuzzygen was used to generate a prediction system from rules generated by decision tree algorithm. The language used to implement this software was Java. The DiagData has been used in diseases prediction and diagnosis systems and in the validation of economic and environmental indicators in agricultural production systems. The validation process involved measurements and comparisons of the time spent to enter the rules by an expert with the time used to insert the same rules with the proposed tool. Thus, the tool was successfully validated, providing a reduction of time.

Trinomial tree model of the real options approach used in mining investment price forecast and analysis

In order to effectively avoid the defects of a traditional discounted cash flow method, a trinomial tree pricing model of the real option is improved and used to forecast the investment price of mining. Taking Molybdenum ore as an example, a theoretical model for the hurdle price under the optimal investment timing is constructed. Based on the example data, the op- tion price model is simulated. By the model, mine investment price can be computed and forecast effectively. According to the characteristics of mine investment, cut-off grade, reserve estimation and mine life in different price also can be quantified. The result shows that it is reliable and practical to enhance the accuracy for mining investment decision.

Unplanned Shutdown Frequency Prediction of FBR MONJU Using Fault Tree Analysis Method

In order to evaluate the operational reliability of Japanese FBR （fast breeder reactor） MONJU, frequencies of important intermediate events and equipment failures resulting during reactor automatic trip are predicted using FTA （fault tree analysis） technique for the plant system model. The targeted devices are the following： PHTS （primary heat transport system）, SHTS （secondary heat transport system）, WS （water and steam system）, PPS （plant protection system） and PCS （plant control system）. In this paper, the frequency of automatic reactor trips was estimated by extracting and analyzing the important intermediate events and equipment failures covering all the derived fault trees of these systems. The analyses predicted 1.2/RY （reactor year） the value of unplanned shut down frequency by the internal factor of the system. The largest contributed event was function failure of SHTS accounting for 42.6% of total events followed by PHTS with 40.1%. The contribution factor of WS was only 4.4%.

Influences of misprediction costs on solar flare prediction

The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm.Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region.These selected parameters are applied as the inputs of the solar flare prediction model.The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares.It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples,and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares.This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.