Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

开滦矿区近距离煤层群上行开采可行性研究

为了对近距离煤层群上行开采的可行性进行判别,以开滦矿区近20年来上行开采实例为基础,确定了上行开采可行性的评价指标并对其进行了定量化,其中将下煤层开采后对上煤层的破坏程度分为5个等级,破坏等级越高,则上行开采越困难。在此基础上利用多元回归分析方法,以直接顶初次垮落步距、时间间隔、采动影响系数为自变量,以上煤层破坏程度为因变量,建立了近距离煤层群上行开采可行性判据。结果表明,采动影响系数越大、上下煤层开采间隔的时间越长,对上部煤层的开采就越有利。

农村高脂血症患者辛伐他汀治疗后肌酸激酶的变化及其影响因素

目的:了解农村高脂血症患者服用辛伐他汀后血清肌酸激酶(Creatine Kinase,CK)的变化及其主要影响因素。方法:安徽农村社区高脂血症患者,在饮食控制治疗4周后,血清胆固醇水平仍≥5.72 mmol/L者,给予辛伐他汀20 mg每晚顿服,分别于治疗前及治疗后4周、8周检测血脂、肝功能和血清CK等。结果:(1)服药4周后,患者CK均值明显高于服药前(P<0.01),平均升高约为88.5%。(2)多元逐步回归分析显示,影响患者服药后CK升高的主要因素为服药前的CK水平和体力劳动强度。(3)分层分析显示体力劳动强度越大,CK升高越明显(P>0.01)。结论:农村高脂血症患者服用辛伐他汀会引起血清CK短暂性升高,在服药期间体力劳动强度将会影响CK的升高。

基于地震多属性的孔隙度预测——以川东A气田为例

利用基于地震多属性的孔隙度预测方法,可综合权衡各属性参数,更客观、有效地反映孔隙度的变化。建立测井孔隙度同地震属性联系,运用多元回归、误差分析、交叉验证等技术来确定最优的属性类型及数量;结合人工神经网络方法建立这些属性与测井孔隙度之间的映射关系,预测孔隙度在平面、垂向上的分布特征。首次将地震多属性孔隙度预测方法运用于川东A气田超致密砂岩储集层孔隙度的预测研究,取得了良好的效果。

基于QAR数据的飞机巡航段燃油流量回归模型

深入分析了波音737-700飞机的QAR数据,确定了影响飞机燃油流量的因素;采用多元线性回归分析方法,建立了该机型巡航段燃油流量模型,利用该模型对飞机巡航段燃油流量进行了验证。对验证精度的评估表明所建立的模型具有较好的验证效果,为航空公司提高燃油消耗监控效率及控制燃油成本提供了参考。

探讨配矿数学模型 提高铁精矿品位

运用多元回归分析法,分析铁矿石采出品位、碎矿抛岩品位和铁精矿品位三者之间的相互关系,并以此为依据,确立配矿数学模型,提出配矿方案。

论偏最小二乘校正方法的稳定性

研究了多元校正方法——偏最小二乘（ＰＬＳ）在波长色散Ｘ射线荧光光谱分析中的应用，并将该法与传统的经验系数法（ＥＣＭ）进行了比较．其考察对象为一组转炉渣文献数据、一组不锈钢样和一组锌精矿样．结果表明，ＰＬＳ比ＥＣＭ准确、稳定．经分析探讨，认为其主要原因是ＰＬＳ能滤除噪音．文中建立了描述浓度和强度本质的关系式．

建筑能源利用率预测与节能评价模型研究

针对目前的建筑能源系统效率低下、浪费严重的情况,本文针对建筑能源消耗的现状和特点提出了一种建筑能源利用率预测与节能评价模型,从经济变量数据出发,利用协整理论基于多元素回归法的能源预测模型,然后在对建筑能源利用率进行预测的基础上,再采用多级模糊AHP综合评价方法将评价指标体系分成递阶层次结构,对建筑节能情况进行评价。算法仿真试验结果表明,本文提出的建筑能源利用率预测与节能评价模型切实有效,并且准确性较高。

民用飞机航线平均燃油消耗评估模型研究

定量评估民用飞机的航线平均油耗水平是优化飞机型号设计、制定碳排放收费政策的前提。论文通过统计分析国内常见民用客机的航线生产运营数据,运用灰色关联分析法对影响民用飞机航线平均耗油水平的主要因素进行分析。为定量评估民用飞机航线平均油耗水平,本文建立了以航距和最大起飞重量为解释变量的非线性统计回归模型,模型的拟合优度达到0.9945。实证分析结果表明,该模型可靠性较高。

Non-isothermal Decomposition Mechanism and Kinetics of LiClO_4 in Nitrogen

The non-isothermal decomposition kinetics of LiClO4 in flow N2 atmosphere was studied. TG-DTA curves show that the decomposition proceeded through two well-defined steps below 900℃, and the mass loss was in agreement with the theoretical value. XRD profile demonstrates that the product of the thermal decomposition at 500℃ is LiCI. For the decomposition kinetics study, the activation energies calculated with the Friedman method were considered as the initial values for non-linear regression and were used for verifying the correctness of the fired models. The decomposition process was fitted by a two-step consecutive reaction： extended Prout-Tompkins equation[Bna, f（α） is （1-α）^nα^α] followed by a lth order reaction（F1）. The activation energies were （215.6±0.2） and （251.6±3.6） kJ/mol, respectively. The exponentials n and a for Bna reaction were （0.25±0.05） and （0.795±0.005）, respectively. The reaction types and activation energies were in agreement with those obtained from the isothermal method, but the exponentials were optimized for better firing and prediction.

Intelligent process expert database of double pulse MIG welding of AI-Si alloy

Based on double pulse welding process characteristics, expert database structure and work flow are designed. Further, multiple outstanding specifications of 1.0 ram-diameter wire are obtained through a large number of experiments. By making non-linear regression analysis on these groups of standards, the relationship between average welding current and other pulse parameters can be found out. Polynomial regression equation is set up for further realization of＂ parameter estimation function of the expert database. Finally, the preliminary developed expert database is tested. The result indicates that the unified adjusting and parameters estimation of the expert database leads to stable welding process and good weld appearance.

Temperature change along elevation and its effect on the alpine timberline tree growth in the southeast of the Tibetan Plateau

Smith fir （Abies georgei var. smithii）, which is the timberline constructive tree species in the cool slope of Mt. Sygera in the southeast ofTibet, plays a very important role in maintaining the timberline completeness and indicating global climate change. This study uses theinstrumental recorded meteorological data along the altitude from 3600 to 4400 m at every 200 m in the growing season, investigates the smithfir growth biomass from 2006 to 2010 in the same timberline ecotone, and makes a non-linear regression analysis to determine the relationshipbetween the alpine tree growth biomass and its in-situ environment condition. The results showed that the cool and warm slope share different airtemperature lapse rates, which were
0.48 C （100 m）
1 in the warm slope and
0.54 C （100 m）
1 in the cool slope, respectively. However, thedominant timberline tree species in the warm slope was Sabina saltuaria, and it can reach as high as 4570 m, which is approximately 170 mhigher than that in the cool slope. Moreover, the smith fir in the cool slope was only distributed in the range of elevation from approximately3600 to 4400 m. The altitude of approximately 3800 m was the appropriate altitude for the growing smith fir, where the mean air temperature inthe growing season was about 9.0 C, and the young smith fir tree can form more biomass. The results suggested that alpine forest chose asuitable environment where trees can grow more in the prolonged succession, but not in the warmer or cooler condition, it could be seen as abiological evidence for climate change.

A regression model-based method for indoor positioning with compound location fingerprints

This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.

Mathematical model for abrasive suspension jet cutting based on orthogonal test design

This paper describes the application of orthogonal test design coupled with non-linear regression analysis to optimize abrasive suspension jet （AS J） cutting process and construct its cutting model. Orthogonal test design is applied to cutting stainless steel. Through range analysis on experiment results, the optimal process conditions for the cutting depth and the kerr ratio of the bottom width to the top width can be determined. In addition, the analysis of ranges and variances are all employed to identify various factors： traverse rate, working pressure, nozzle diameter, standoff distance which denote the importance order of the cutting parameters affecting cutting depth and the kerf ratio of the bottom width to the top width. ~rthermore, non-linear regression analysis is used to establish the mathematical models of the cutting parameters based on the cutting depth and the kerr ratio. Finally, the verification experiments of cutting parameters＇ effect on cutting performance, which show that optimized cutting parameters and cutting model can significantly improve the prediction of the cutting ability and quality of ASJ.

考虑温度-负荷相关性的调温负荷曲线拟合方法研究

根据调温负荷与温度的相关性,分别采用一元二次函数、一元三次函数进行温度-调温负荷曲线拟合,并采用二元二次函数拟合调温曲线以分析考虑温度累积效应后采暖电负荷受气温影响的情况。最后提出了温度-调温负荷灵敏度量化温度对调温负荷的影响程度。以贵州省多年历史负荷数据及气温数据为样本,计算调温负荷曲线,拟合该地区温度-调温负荷曲线,为电网规划及调度运行提供负荷特性、负荷预测计算工程实用方法。

TiO_(2)微粒和纳米颗粒制备K_(2)Ti_(2)O_(5)的动力学研究

The formation mechanism of K2Ti2O5 was investigated with Ti O2 microparticles and nanoparticles as precursors by the thermogravimetric(TG) technique. A method of direct multivariate non-linear regression was applied for simultaneous calculation of solid-state reaction kinetic parameters from TG curves. TG results show more regular decrease from initial reaction temperature with Ti O2 nanoparticles as raw material compared with Ti O2 microparticles, while mass losses finish at similar temperatures under the experimental conditions. From the mechanism and kinetic parameters, the reactions with the two materials are complex consecutive processes, and reaction rate constants increase with temperature and decrease with conversion. The reaction proceedings could be significantly hindered when the diffusion process of reactant species becomes rate-limiting in the later stage of reaction process. The reaction active sites on initial Ti O2 particles and formation of product layers may be responsible to the changes of reaction rate constant. The calculated results are in good agreement with experimental ones.

Mathematical approach for understanding deagglomeration behaviour of drug powder in formulations with coarse carrier

Deagglomeration of cohesive particles in combination with coarse carrier is a key requirement for inhaled formulations.The aim of the project was to propose a mathematical approach to understand aerosolization behaviour of micronized particles alone and in formulation with carriers.Salbutamol sulphate and salmeterol xinafoate were blended separately with fine lactose(ratio 1:4)and fine and coarse lactose(1:4:63.5).Laser diffraction was employed to characterize the powder median particle size.The deagglomeration of micronized materials followed an asymptotic monoexponential relationship.When the coarse lactose was added,the relationship fitted a bi-exponential equation showing an easily and a poorly dispersed fraction.Using model hydrophobic and hydrophilic APIs,this study has demonstrated the utility of an analytical approach that can parameterize deagglomeration behaviour of carrier-free and carrier-based inhalation formulations.The analytical approach provides the ability to systematically study the effect of material,formulation and processing factors on deagglomeration behaviour.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

Adaptability and Stability Analysis of Soybean Genotypes Using Toler and Centroid Methods

Soybean (Glycine max L. Merr.) adaptation to new environments has been hard to predict based on maturity group. The aim of this study was to evaluate the performance of 14 soybean genotypes, from the Soybean Breeding Program of the Federal University of Uberlandia, in their adaptive capacity and seed yield stability at 3 locations and 2 growing seasons. For the adaptability and stability analysis the Toler and Centroid methods were used;5 genotypic groups were identified in the first whereas 4 groups were identified in the latter. By the Toler method group A was composed by 4 genotypes, UFU-001, UFU-003, UFU-0010, and UFU-001. They showed a convex pattern of adaptability and stability. In contrast, the genotypes UFU-008 and UFU-0013 were classified in Group E with a concave pattern of adaptability and stability. Regarding results from the Centroid method, the Genotype UFU-002, with higher seed yield than average, was the only genotype in Ideotype VI with moderate adaptability to favorable environments. In contrast, 10 genotypes were included in the Ideotype V, of medium general adaptability. The genotypes UFU-001, UFU-002, UFU-006, UFU-0010, and UFU-0011 were recommended for use in the Brazilian Cerrado growing region. These genotypes had high seed yield potential in high quality environments.

Support vector machine regression(SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization(RRN)

Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.