Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

Regression models of Pearson correlation coefficient

We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of interest.Likelihood-based inference is established to estimate the regression coefficients,upon which bootstrap-based method is used to test the significance of covariates of interest.Simulation studies show the effectiveness of the method in terms of type-I error control,power performance in moderate sample size and robustness with respect to model mis-specification.We illustrate the application of the proposed method to some real data concerning health measurements.

Prediction model for cost data of a power transmission and transformation project based on Pearson correlation coefficient-IPSO-ELM

In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.

Basic Characteristics,Spatial Disparity and Its Major Influencing Factors of Service Industry in China

Based on the analysis of its basic characteristics, this article investigated the disparities of Chinese service industry among the three regions （the eastern China, the western China and the middle China） and inter-provincial disparities of that in the three regions by Theil coefficient and cluster analysis. Then, major factors influencing its spatial disparity were explored by correlation analysis and regression analysis. The conclusions could be drawn as follows. 1） The development of Chinese service industry experienced three phases since the 1980s： rapid growth period, slow growth period, and recovery period. From the proportion of value-added and employment, its development was obviously on the low level. From the composition of industrial structure, traditional service sectors were dominant, but modem service sectors were lagged. Moreover, its spatial disparity was distinct. 2） The level of Chinese service industry was divided into five basic regional ranks： well-developed, developed, relatively-developed, underdeveloped and undeveloped regions, As a whole, the overall structure of spatial disparity was steady in 1990-2005. But there was notable gradient disparity in the interior structure of service industry among different provinces. Furthermore, the overall disparity expanded rapidly in 1990-2005. The inter-provincial disparity of service industry in the three regions, especially in the eastern China, was bigger than the disparity among the three regions. And 3） the level of economic development, the level of urban development, the scale of market capacity, the level of transportation and telecommunication, and the abundance of human resources were major factors influencing the development of Chinese service industry.

Fracture property identification method based on shrinkage factor particle swarm optimization

In the multi-wave and multi-component seismic exploration,shear-wave will be split into fast wave and slow wave,when it propagates in anisotropic media. Then the authors can predict polarization direction and density of crack and detect the development status of cracks underground according to shear-wave splitting phenomenon. The technology plays an important role and shows great potential in crack reservoir detection. In this study,the improved particle swarm optimization algorithm based on shrinkage factor is combined with the Pearson correlation coefficient method to obtain the fracture azimuth angle and density. The experimental results show that the modified method can improve the convergence rate,accuracy,anti-noise performance and computational efficiency.

Sources of Particular Pollutants inAmbient Air at a Petrochemical Enterprise

The study on source apportionment of particular pollutants in ambient air at a petrochemical enterprise is the ba-sis of the control over air pollution. Through analyzing particular pollutants in the samples collected from one petrochemi- cal enterprise in northwestern China, the sources of particular pollutants were discussed. The test results showed that con- centrations of particular pollutants in different sites were remarkably different. Results showed that the sampling sites with higher concentrations of particular pollutants, including toluene, xylenes, NH3 and H2S, were located at the boundary of the petrochemical enterprise. Instead, the concentrations of NMHC in the ambient air sampling sites were higher than those at the boundary of the petrochemical enterprise. The sampling sites with higher concentrations of particular pollutants were located in the area that was close to the petrochemical enterprise. The results obtained from the Pearson correlation co- efficients analyses, the factor analyses, and x^2-tests of the particular pollutants had revealed that NH3, H2S, toluene and xylenes at all sampling sites came from the same source, while NMHC might come from some other sources besides the petrochemical enterprise.

Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm

Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem.

Metaphor Analysis Method Based on Latent Semantic Analysis

Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.

Fuzzy Logic Implementation of Vulnerability Assessment in a Coastal Aquifer of Northern Sicily

Aquifers can be defined as complex ecological systems. Their description is closely influenced by geometrical and geological parameters, which portray the hydrogeological behaviour of underground systems. This paper reports a contribution to assessing groundwater contamination risk in a particular Sicily sector, where deterministic approaches have methodically assessed and mapped vulnerability and quality of groundwater. In detail, in the coastal area of Acquedolci (Northern Sicily), already intensely surveyed in the frame of interdisciplinary projects on geological risk, implementing models and systems have been experimented, also considering fuzzy logic. Cartography issues are here presented and compared, with particular regard to the effect of stochastic hydrogeological elements (i.e. “depth to water”), locally characterized by variability for simultaneous climate, overdraft, irrigation and sea encroachment. The authors show how fuzzy logic, applied to vulnerability settings, contributes to a better comprehension of the passive scenery offered by aquifers in Acquedolci Sicily area.

Parameter value selection strategy for complete coverage path planning based on the Lüsystem to perform specific types of missions

We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.

An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security

Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

Intrusion Detection System Using Voting-Based Neural Network

Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(DDoS),botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP'99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network(DNN), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), and Gated Recurrent Unit(GRU).

Investigation on the relationship between hydraulic loss and vortex evolution in pump mode of a pump-turbine

Hydraulic loss and vorticity are two most common methods in analyzing the flow characteristics in hydro-machine,i.e.,centrifugal pump,Francis turbine,etc.While the relationship and correlation between hydraulic loss and vortex evolution are not uncovered yet.In this study,hydraulic loss is regarded as the combination of dissipation effect and transportation effect in flow domains.Meanwhile,vorticityωcan be decomposed into two parts,namely the Liutex partω_(R),the shear partωs,of whichω_(R)is regarded as the third-generation vortex identification method for its precise and rigorous definition of local rigid rotation part of fluid.Based on the dimensional analysis,two new physical quantities related to vorticity(ω,ω_(R)andωS),namely enstrophyΩ,vorticity transport intensity T are adopted to express the energy characteristic in vortex evolution process.Finally,operating points at pump mode of an ultra-high head reversible pump-turbine are selected as the research object and the numerical results calculated using SST k-ωturbulence model are consistent well with the experimental data.Pearson correlation coefficient is adopted to evaluate the correlation between hydraulic loss and vortex evolution in main flow regions.Results show that apart from the spiral casing domain,the enstrophy of shear partΩs has very strong correlation with dissipation effect and Liutex transport intensity TR has stronger correlation with transportation effect when compared with other forms of vorticity.The correlation between Liutex transport intensity TR and transportation effect is strong in stay/guide vanes(SGVs)while reduce to medium level in runner and draft tube domains.In spiral casing domain,all forms of vorticity show weak or very weak correlation with transportation effect.Based on the proposed method,we believe that the relationship and correlation between hydraulic loss and vortex evolution in other hydraulic machineries can also be clearly investigated.

The effect of salinity on waste activated sludge alkaline fermentation and kinetic analysis

The effect of salinity on sludge alkaline fermentation at low temperature（20°C） was investigated, and a kinetic analysis was performed. Different doses of sodium chloride（Na Cl, 0–25 g/L） were added into the fermentation system. The batch-mode results showed that the soluble chemical oxygen demand（SCOD） increased with salinity. The hydrolysate（soluble protein, polysaccharide） and the acidification products（short chain fatty acids（SCFAs）, NH＋4–N, and PO_4^（3-）–P） increased with salinity initially, but slightly declined respectively at higher level salinity（20 g/L or 20–25 g/L）. However, the hydrolytic acidification performance increased in the presence of salt compared to that without salt.Furthermore, the results of Haldane inhibition kinetics analysis showed that the salt enhanced the hydrolysis rate of particulate organic matter from sludge particulate and the specific utilization of hydrolysate, and decreased the specific utilization of SCFAs. Pearson correlation coefficient analysis indicated that the importance of polysaccharide on the accumulation of SCFAs was reduced with salt addition, but the importance of protein and NH＋4–N on SCFA accumulation was increased.

Soil particle size range correction for improved calibration relationship between the laser-diffraction method and sieve-pipette method

Particle size fraction(clay, silt, and sand) is an important characteristic that influences several soil functions. The laser-diffraction method(LDM) provides a fast and cost-effective measurement of particle size distribution, but the results usually differ from those obtained by the traditional sieve-pipette method(SPM). This difference can persist even when calibration is applied between the two methods. This partly relates to the different size ranges of particles measured by the two methods as a result of different operational principles, i.e., particle sedimentation according to Stokes’ Law vs. Mie theory for laser beam scattering. The objective of this study was to identify particle size ranges of LDM equivalent to those measured by SPM and evaluate whether new calibration models based on size range correction can be used to improve LDM-estimated particle size fractions, using 51 soil samples with various texture collected from five soil orders in New Zealand. Particle size distribution was determined using both LDM and SPM. Compared with SPM, original data from LDM underestimated the clay fraction(< 2 μm), overestimated the silt fraction(2–53 μm), but provided a good estimation of the sand fraction(53–2 000 μm).Results from three statistical indices, including Pearson’s correlation coefficient, slope, and Lin’s concordance correlation coefficient, showed that the size ranges of < 2 and 2–53 μm defined by SPM corresponded with the < 5 and 5–53 μm size ranges by LDM, respectively. Compared with the traditional calibration(based on the same particle size ranges), new calibration models(based on the corrected size ranges of these two methods) improved the estimation of clay and silt contents by LDM. Compared with soil-specific models(i.e., different models were developed for different soils), a universal model may be more parsimonious for estimating particle size fractions if the samples to be assessed represent multiple soil orders.