回归分析在葡萄叶面积测算中的应用

调查5个葡萄品种的叶片形状特征值及叶面积,利用相关和回归分析的方法,研究葡萄品种叶片的叶形特征值与叶面积的相关关系及回归方程。相关分析结果表明,红提品种单叶面积与主脉长×叶宽以及第2侧脉长的相关性最大;摩尔多瓦、魏可品种单叶面积与主脉长×叶宽以及叶宽的相关性最大;维多利亚品种单叶面积与第2侧脉长、主脉长的相关性最大;巨玫瑰品种单叶面积与主脉长×叶宽以及第2侧脉长的相关性最大;回归分析结果表明,主脉长×叶宽与叶面积的回归方程最优,利用其回归方程测定葡萄叶面积与实测叶面积误差很小,均不超过1%,该回归分析适于测量其叶面积的生长动态,是一种简单有效的叶面积测定方法。

高速公路PPP项目可行性缺口补助测算方法研究

为防控财政风险,确保社会投资人取得合理回报,科学评估PPP项目建设影响及各年度项目收支成为政府和社会投资方共同关注的重点。可行性缺口补助是介于政府付费与使用者付费之间的一种折衷形式,财金[2015]21号文中给出相应计算方法,通过实际操作发现,该公式存在概念模糊、界定不清等问题。为此结合实际项目,在充分分析项目收入和支出组成结构的基础上,通过理清基本交通量、盈亏平衡点交通量和实际交通量三个变量与项目收支之间的内在关系,设定年度折现率、合理利润率、年度收支等关键参数,通过分析三类交通量之间的关系,以收支平衡的理念计算可行性缺口补助,通过比较分析,得出本计算方法更加合理的结论。

基于概率统计的数据挖掘预测算

近年来概率与数理统计的不断发展使之在各个领域得到了广泛的应用,尤其是基于概率统计的数据挖掘测算就是一个很热门的行业,正因为这个行业需要精准的测算以及测量,所以,学好这个领域更显得极为重要,本文主要讲的就是概率统计的数据挖掘测算存在的问题以及应用。

浅析水运工程营改增计价调整办法与应用

本文简要介绍了增值税与营业税的计算方法及其区别,对比分析了水运工程与其他行业计价依据调整办法的区别,并对水运工程营改增前后造价水平进行了测算与分析,总结了营改增对水运工程建设行业的影响。

Prospecting for coal in China with remote sensing

With the rapid development of China＇s economy, coal resources are increasingly in great demand. As a result, the remaining coal reserves diminish gradually with large-scale exploitation of coal resources. Easily-found mines which used to be identified from outcrops or were buried under shallow overburden are decreasing, especially in the prosperous eastern regions of China, which experience coal shortages. Currently the main targets of coal prospecting are concealed and unidentified underground coal bodies, making it more and more difficult for coal prospecting. It is therefore important to explore coal prospecting by taking advantage of modern remote sensing and geographic information system technologies. Given a theoretical basis for coal prospecting by remote sensing, we demonstrate the methodologies and existing problems systematically by summarizing past practices of coal prospecting with remote sensing. We propose a new theory of coal prospecting with remote sensing. In uncovered areas, coal resources can be prospected for by direct interpretation. In coal beating strata of developed areas covered by thin Quaternary strata or vegetation, prospecting for coal can be carried out by indirect interpretation of geomorphology and vegetation. For deeply buried underground deposits, coal prospecting can rely on tectonic structures, interpretation and analysis of new tectonic clues and regularity of coal formation and preservation controlled by tectonic structures. By applying newly hyper-spectral, multi-polarization, multi-angle, multi-temporal and multi-resolution remote sensing data and carrying out integrated analysis of geographic attributes, ground attributes, geophysical exploration results, geochemical exploration results, geological drilling results and remote sensing data by GIS tools, coal geology resources and mineralogical regularities can be explored and coal resource information can be acquired with some confidence.

Predictive value of ^(18)F-fluorodeoxyglucose PET/CT for transarterial chemolipiodolization of hepatocellular carcinoma

AIM： To investigate the correlation of 18F-fluorodeoxy- glucose （18F-FDG） positron emission tomography （PET） with clinical features and the prediction of treatment response. METHODS： A total of 83 hepatocellular carcinoma （HCC） patients undergoing 18F-FDG PET before transar- terial chemolipiodolization with systemic chemo-infusion between October, 2006 and May, 2009 were retrospec-tively enrolled. The patients included 68 men and 15 women （mean age, 60 ~ 10.7 years）. The effect of 18F- FDG-monitored PET uptake on clinical features and on the evaluated treatment response was ascertained with modified Response Evaluation Criteria in Solid Tumors. The PET parameters of maximal standardized uptake value of the tumor （Tsuvmax）, the ratio of the tumor maximal standardized uptake value （SUV） to the liver maximal SUV （Tsuvmax/Lsuwax） and the ratio of tumor maximal SUV to the liver mean SUV （msuvmax/LSUVrnean） were tested as predictive factors. RESULTS： Among the 3 SUV parameters, the TSUV- =maxdLsuvmean ratio （cutoff value of 1.90） was significantly associated with tumor burden including tumor size, tu- mor number, α-fetoprotein levels and tumor stage （P 〈 0.001, P = 0.008, P = 0.011, P 〈 0.001, respectively）. The objective response rates in patients with a high SUV ratio （≥ 1.90） were significantly better than those with a low SUV ratio （〈 1.90） （P = 0.020）. The overall survival rates of patients exhibiting a low Tsuvmax/Lsu- Vmean ratio （〈 1.90） and those with a high SUV ratio （≥1.90） was 38.2 and 10.3 mo, respectively （P 〈 0.01）. However, the time to progression showed no significant difference between the groups （P = 0.15）. CONCLUSION： 18F-FDG PET can be an important predictor of HCC treatment. In particular, the Tsuvmax/ Lsuwean ratio （cutoff value of 1.90） can provide useful information in treatment prognosis for HCC patients treated with Iocoregional therapy.

NOVEL ADAPTIVE MULTIUSER DETECTIONALGORITHM BASED ON WAVELET TRANSFORM

The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses LMS algorithm to implement the adaptive multiuser detection. The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence. White noise can be wiped off under the wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analyses and simulations demonstrate that the algorithm converges faster than the conventional adaptive multiuser detection algorithm, and has the better performance. Simulation results reveal that the algorithm convergence relates to the wavelet base, and show that the algorithm convergence gets better with the increasing of regularity for the same series of the wavelet base. Finally the algorithm shows that it can be easily implemented.

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

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.

Application of the Third Theory of Quantification in Screening Sensitive Geological Factors Influencing Coal and Gas Outburst

The principles of the third theory of quantification were discussed. The concept and calculation method of reaction degree were put forward, which have extended the applying range and scientificalness of the primary reaction. Taking the Zhongmacun mine as an example, the geological factors affecting coal and gas outburst were researched. Eight sensitive factors for the outburst of coal and gas were screened out from 11 geological factors using the method of unit classification and the third theory of quantification. On the basis of this, the Zhongmacun coal mine was classified into several divisions. The practice shows that it is feasible to apply the third theory of quantification to gas geology, which offers a new thought to screen the sensitive geological factors of gas outburst forecast.

An Algorithm for Detecting Low-frequency Seismic Events and Preliminary Applications

An algorithm for detecting low-frequency seismic events is developed and applied to the detection of low-frequency events before the 2008 Wenchuan and the 2013 Lushan earthquakes. Continuous vertical-component waveforms of some broadband stations in the few months before the Wenchuan and Lushan earthquakes are processed by applying a bandpass filter in 2- 8Hz,and then converted to envelopes with a smoothing time of 10 s window and a median filter with a 20 min window. As a result,teleseismic,long-period noise and local small earthquakes are removed,the filtered amplitude is obviously larger than that of the noise and lasts for a dozen minutes to several hours during a few days in a few stations before the Wenchuan and Lushan earthquakes,respectively. The waveform and envelope are similar to that of a non-volcanic tremor( NVT). There are suspected NVT before the two earthquakes. Preliminary application demonstrates that this algorithm is potentially useful for extracting NVT signals from continuous seismic waveforms.

Application of computational intelligence platform in coal and gas outburst prediction

The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.

Application of Snapback Chronometry Method in Calculation of Regulation

Work study is an area of construction production rationalization in which with scientific, logical, holistic and system analysis methods of the process we gain optimum in way of work and time of work. Chronometry method is one of the work study methods which is appropriate for recording shorter cyclic processes and is based on statistical sampling theory. Determination of cyclic times and work performances of standard cyclic construction machines （SCCM） is one of key assumptions of dynamic planning of machine work on every construction site. Calculation methods of SCCM work performances arc one of basic research objects in the field of construction organization and technology. Study shows applied chronometry method in work of standard cyclic construction machine. Goal of this study is to accomplish regulation through measured time cycle and compare measured effects with effects obtained with standard methodology of calculating hydraulic excavator practical achievement for gaining regulation in order to determine main reasons which effect work performance on site.

Advances of Bioinformatics Tools Applied in Virus Epitopes Prediction

In recent years,the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein,a general overview of different tools currently available,including T cell and B cell epitopes prediction tools,is presented. And the principles of different prediction algorithms are reviewed briefly. Finally,several examples are present to illustrate the application of the prediction tools.

The Study of Otsu Algorithm Applied in the Measuring of Ash Proportion

To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.

Application of Genetic Algorithms to Optimize Neural Networks for Selected Tribological Tests

This paper presents a method of determining the friction coefficient in metal forming using multilayer artificial neural networks based on experimental data obtained from strip drawing test. The number of input variables of the artificial neural network has been optimized using genetic algorithm. This process is based on surface parameters of the sheet and dies, sheet material parameters and clamping force as input parameters to train the neural network. In addition to demonstrating the fact that regression statistics model using genetic selection and intelligent problem solver are better than models without preprocessing of input data, the sensitivity analysis of the input variables has been conducted. This avoids the time-consuming testing of neurons in finding the best network architecture. The obtained results from this study have also pointed out that genetic algorithm can successfully be applied to optimize the training set and the outputs agree with experimental results. This allows reduction or elimination of expensive experimental tests to determine friction coefficient value.

Application of Signal Detection Theory to Ascertain Driving Risk and Mitigation in a Flooded Urban Area

Signal Detection Theory （SDT） offers an unparalleled deterministic set of decision variables necessary to formulate applied risks in transportation. SDT has distinct advantages over basic prediction models since the latter may not represent an entirely accurate analysis. Thresholds based on elements of stimulus （signal and noise） and response for： a Type I discrimination of response variable where decision outcomes and rates are computed for metacognition to discriminate a Type II of decision outcomes was set. We also adapted the classical Dijkstra＇s shortest path algorithm within a GIS environment using Avenue programming. Contours derived from LiDARwere used to set flood levels while satellite imagery corresponding to Red River of the North inundated （signal） areas were acquired amongst other spatial datasets. The signal information was further dichotomized using a binary yes-no model. Origin and destination points constrained within Fargo-Morehead were generated using a random point generator. From these points, trips were generated with some connected segments traversing through flooded areas. By analyzing False Alarm Rate （FAR） and Corrected Rejection （CRR） computation, we found out that, when Hit Rate （HR） and FAR are both low then there was an increased corresponding sensitivity. At 30-35 ft flood level, the values for FAR and HR was 0.97 and 0.91 respectively.When FAR〉HR, lower set flood levels offered numerous route choices. Corresponding routes with associated impedance can be classified for risk-averse drivers or risk-takers While the risk-averse avoid risky and unfavorable routes, the risk-taker optimizes at an adjustment factor of ω = 0.1 or ω = 0.2. An idealistic stage is achieved for a conservative, co, equal to 0.4 or 0.5, which indicates maximum achievement in terms of time gain and safety simultaneously. At ω = 0.0 the prevailing conditions can be considered unrealistic since they incorporate areas considered impassable with absolute resistance like segments with a ＂Road Closed＂ or ＂Detour＂ sign. The applicability of our approach can be used to design multi-level and multi-modal transportation systems involving risk.

Automatic GUI Test by Using SIFT Matching

In software development process, the last step is usually the Graphic User In- terface（GUI） test, which is part of the final user experience （UE） test. Traditionally, there exist some GUI test tools in the market, such as Abbot Java GUI Test Framework and Pounder, in which testers pre-configure in the script all desired actions and instructions for the computer, nonetheless requiring too much of invariance of GUI environment; and they require reconfiguration in case of GUI changes, therefore still to be done mostly manually and hard for non-programmer testers to. Consequently, we proposed GUI tests by image recognition to automate the last process; we managed to innovate upon current algorithms such as SIFT and Random Fern, from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm. Computers then apply the algorithm, to search for target patterns themselves and take subsequent actions such as manual mouse, keyboard and screen I/O automatically to test the GUI without any manual instructions. Test results showed that the proposed approach can accelerate GU! test largely compared to current benchmarks.

Improved Blind Multiuser Detection Algorithm Based on Minimum Output Energy

Based on minimum output energy,an improved blind multiuser detection algorithm is proposed by the use of Hopfield neural network.Compared with traditional algorithms,the proposed algorithm does not need the circuit for constraints.The resources are greatly saved and the complexity is reduced as well.The simulation results show that the performance of the improved algorithm is similar to that of the optimal multiuser detection algorithm which is not suitable for the mobile station.Compared with the traditional gradient blind multiuser detection algorithm,the convergence speed of the improved algorithm is quickened.

Evaluating the accuracy performance of Lucas-Kanade algorithm in the circumstance of PIV application

Lucas-Kanade(LK) algorithm, usually used in optical flow filed, has recently received increasing attention from PIV community due to its advanced calculation efficiency by GPU acceleration. Although applications of this algorithm are continuously emerging,a systematic performance evaluation is still lacking. This forms the primary aim of the present work. Three warping schemes in the family of LK algorithm: forward/inverse/symmetric warping, are evaluated in a prototype flow of a hierarchy of multiple two-dimensional vortices. Second-order Newton descent is also considered here. The accuracy & efficiency of all these LK variants are investigated under a large domain of various influential parameters. It is found that the constant displacement constraint, which is a necessary building block for GPU acceleration, is the most critical issue in affecting LK algorithm's accuracy, which can be somehow ameliorated by using second-order Newton descent. Moreover, symmetric warping outbids the other two warping schemes in accuracy level, robustness to noise, convergence speed and tolerance to displacement gradient, and might be the first choice when applying LK algorithm to PIV measurement.