Distance Discriminant Analysis Research Articles

Influencing factors of hotel reservation and prediction

Hotel reservation has been a concern for hotel managers. They can manage their rooms well and deal with emergencies through the booking status, so it is important to predict whether the customer will cancel the reservation or not. Different from the articles data set based on evaluation and satisfaction of the hotel, this article is going to use limited and basic information based on objective factors to analysis the most influential factor and do the prediction. According to the correlation coefficient, the pairwise relationship of lead_time and booking status have a moderate and positive relationship, which. Next, through the test of goodness of fit, the number of children has a significant effect on the booking status. Last, the distance discriminant analysis model has a good predictive ability. The meaning of the analysis is to find new correlations of the pairwise variables, which people naturally ignore, and build a relatively accurate prediction model to serve the hotel managers to develop better.

Study on the Prediction of Low-Index Coal and Gas Outburst Based on PSO-SVM

Low-index coal and gas outburst (LI-CGO) is difficult to predict, which seriously threatens the efficient mining of coal. To predict the LI-CGO, the Support Vector Machine (SVM) algorithm was used in this study. The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the SVM algorithm. The results show that based on the training sets and test set in this study, the prediction accuracy of SVM is higher than that of Back Propagation Neural Network and Distance Discriminant Analysis. The prediction accuracy of the SVM model trained by the training set T2 with LI-CGO cases is higher than that of the SVM model trained by the training set T1 without LI-CGO cases. The prediction accuracy gets better when the SVM model is trained by the training set T3, made by adding the data of the other two coal mines (EH and SH) to the training set T2, that only contains the data of XP and PJ. Furthermore, the PSO-SVM model achieves a better predictive effect than the SVM model, with an accuracy rate of 90%. The research results can provide a method reference for the prediction of LI-CGO.

Identification of ancient glass categories based on distance discriminant analysis

It is crucial for archaeological investigations to identify the category of cultural relics by analyzing their chemical composition. This study analyzed the chemical composition distribution of glass cultural relics and applied distance discriminant analysis methods to classify them into two categories. Through stepwise regression, four key feature factors ({SiO}_{2}, {K}_{2}O, PbO, and the presence of weathering on the artifact's surface) were selected from a total of 15 features, including surface weathering. Aside from using columnar table analysis to determine weathering on the surface of the artifact and correlations between categories, and using Spearman correlation coefficients to select key feature factors such as {SiO}_{2}, {K}_{2}O, PbO, BaO, and ,SrO from 14 total feature factors (excluding weathering on the surface), we established a Mahalanobis distance discriminant model to differentiate unknown glass artifacts. Results indicate that Spearman-Mahalanobis distance discrimination outperformed stepwise regression-Mahalanobis distance discrimination, with an overall accuracy of 99.10% for the former and 98.69% for the latter in identifying high-potassium glass or lead-barium glass.

Research on Optimization of Deep Excavation Support Scheme Based on Distance Discriminant Analysis Method

With the rapid growth of China's national economy and urban construction, various factors make deep excavation engineering a challenging, high-risk and difficult geotechnical engineering technical subject. It plays an important role in concrete construction and has very high requirements for technology. In deep excavation engineering, in order to achieve the same supporting purpose, there are many alternative schemes, among which each scheme has its own characteristics. The choice of deep excavation support scheme is a complex multi-factor decision-making problem. Based on the idea of distance discriminant analysis theory, considering the principle of distance discriminant analysis method and the specific characteristics of deep excavation support scheme, this article tries to apply discriminant analysis method to the optimization research of deep excavation support scheme, so as to discuss the optimization design of deep excavation support project. It is hoped that the research in this article can further ensure the construction effect of building projects and provide some reference for similar projects.

Evaluation modeling of highway collapse hazard based on rough set and support vector machine

The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses in Yingxiu-Wolong highway of Hanchuan County measured by the authors in the field as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter C is 8 and the kernel parameter sigma is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.

Micrometeoroid and Orbital Debris Impact Detection and Location Based on FBG Sensor Network Using Combined Artificial Neural Network and Mahalanobis Distance Method

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.

Research on the classification model of coal’s bursting liability based on database with large samples

In order to evaluate the intensity of the coal’s bursting liability scientifically and accurately, the index relevance problems existing in the current classification methods were focused on, and the Mahalanobis distance discriminant analysis (DDA) method was introduced on the basis of establishing a measured database with the large sample of the coal’s bursting liability. Besides, the DDA model of coal’s bursting liability classification was also established. Then, with the duration of dynamic fracture, elastic strain energy index, bursting energy index, and uniaxial compressive strength selected as evaluation indicators, three grade-discrimination functions of the bursting liability were established through training the data of 95 groups of bursting liability of different coal seams that were collected extensively. After training, the accuracy rate of the DDA model reached 96%. The results of the application of DDA model to the classification of coal samples from 10 coal mines exhibited remarkable agreement with the actual situation, which also solved the difficult problem that the fuzzy comprehensive evaluation method could not distinguish 8 kinds of samples. Its application to the engineering project shows that the classification result of coal’s bursting liability based on the DDA method is both accurate and easy to calculate, and the DDA model has good engineering application prospect.

Hyper-spherical distance discrimination: A novel data description method for aero-engine rolling bearing fault detection

Abstract A novel method called hyper-spherical distance discrimination (HDD) is proposed in order to meet the requirement of aero-engine rolling bearing on-line monitoring. In proposed method, original multi-dimensional features extracted from vibration acceleration signal are transformed to the same dimensional reconstructed features by de-correlation and normalization while the distribution of feature vectors is transformed from hyper-ellipsoid to hyper-sphere. Then, a simple model built up by distance discriminant analysis is used for rolling bearing fault detection and degradation assessment. HDD is compared with the support vector data description (SVDD) and the self-organizing map (SOM) in rolling bearing fault simulation experiments. The results show that the HDD method is superior to the SVDD and SOM in terms of recognition rate. Besides, HDD is applied to a run-to-failure test of aero-engine rolling bearing. It proves that the evaluating indicator obtained by HDD method is able to reflect the degradation tendency of rolling bearing, and it is also more sensitive to initial fault than the root mean square (RMS) of vibration acceleration signal. With the advantages of low computational complexity and no need to tuning parameters, HDD method can be applied to practical engineering effectively.

Recognition model of groundwater inrush source of coal mine: a case study on Jiaozuo coal mine in China

The mine water source discrimination plays an important role in guiding mine water prevention in the water prevention work. Improving especially the discrimination accuracy of mine water sources which will cause water inrush is the important foundation of avoiding such accident and thus ensuring personnel and property being in safety. Based on Fisher discriminant analysis theory (FDA) and gray correlation analysis theory (GCA), groundwater chemical components (Ca2+, Mg2+, K++Na+, Cl−, SO4 2−, and HCO3 −) data at main water inrush aquifers in a typical coal mine through experiments, FDA-GCA recognition model of water inrush sources was established and then verified. Results indicated that the FDA-GCA recognition model of water inrush sources was characterized by high discrimination precision, and false determination rate obtained by back-substitution estimation method was zero. Thus, it had strong discrimination ability of water inrush sources. The significance ranking of input variables in the water source FDA-GCA recognition model was Ca2+ > Mg2+ > HCO3 − > SO4 2− > K+ + Na+ > Cl−. The data in this paper were discriminated by the distance discriminant method and BP neural network discriminant analysis method, the correct rate of which is 90%, 80%, slightly lower than or equal to the accuracy of 90% by the FDA-GCA recognition model.

Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering). We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

A Gaussian-based kernel Fisher discriminant analysis for electronic nose data and applications in spirit and vinegar classification

Fisher discriminant analysis (FDA) is a very useful pattern recognition technique widely used in electronic nose system (e-nose). However, due to its linear characteristic, the classification problems of multi-class and high-dimensional e-nose data cannot be handled effectively. Therefore, a Gaussian-based kernel FDA (KFDA) method is proposed to solve multi-class and high-dimensional classification problems of complex samples such as food classification using e-nose. The key point of the method is how to determine the Gaussian kernel parameter. Firstly, according to distance discriminant analysis viewpoint, a desired kernel matrix adapted to Gaussian kernel function can be given successfully. Secondly, an evaluation function based on Euclidean distance is established for measuring the degree of approximation between actual kernel matrix containing an unknown Gaussian kernel parameter and the desired kernel matrix so as to get an optimal solution of the parameter, and then the actual kernel matrix can be definitely determined. Finally, the principal component analysis (PCA) for the actual kernel matrix is carried out. Meanwhile, FDA for the principal component matrix generated by PCA is also implemented in succession, and the KFDA is completed. Six kinds of Chinese spirit and six kinds of Chinese vinegar samples as two classification applications were respectively carried out accurately with the KFDA method; and the KFDA method is tested to be very simple and effective. The KFDA method may be promising for complex samples classification dataset of e-nose.

Stator winding single-phase grounding faults protective scheme based on discriminant analysis for Powerformers with selectivity

Abstract In order to improve the stator winding single-phase grounding faults protective scheme for Powerformers, a protective scheme based on Mahal distance discriminant theory is proposed in this paper. By the proposed scheme, the different kinds of fault signals can be integrated. Also, a discriminant analysis model for stator winding single-phase grounding faults is established. In the proposed discriminant analysis model, four parameters including the magnitude of leakage current, the direction of leakage current, the magnitude of zero-sequence current in Powerformer terminal, and the direction of zero-sequence current in Powerformer terminal are selected as the discrimination factor. The method of back substitution estimation is introduced to verify the stability of the distance discriminant analysis model. Twenty-five groups of detected samples were analyzed based on the proposed model. The results are consistent with the measured data and show that the distance discriminant analysis model is with higher accuracy and lower mistake-discrimination ratio.

Discriminant-Analysis-Based Single-Phase Earth Fault Protection Using Improved PCA in Distribution Systems

This paper presents a new approach for single phase-earth fault protection in distribution systems. Traditional protection schemes are analyzed and compared with one another for effective fault feature extraction. The maximum-likelihood method is also carried out on several copula functions to find the optimal copula to fit the fault feature data. Then, copula rank correlation is calculated by the optimal copula, and the principal component analysis technique is improved to preprocess and reduce the dimension of the fault data by decomposing the copula rank correlation matrix instead of computing the eigenvalues and their eigenvectors of the covariance matrix. Finally, the distance discriminant function is defined and operation criterion is proposed, and distance discriminant analysis is used to discriminate the faulty feeder. Simulation results of a practical 10-kV distribution system show that the proposed approach of fault protection is able to achieve better identification accuracy than the hierarchical clustering algorithm and Fuzzy c-means algorithms-based approaches.

Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data.

DNA methylation is essential not only in cellular differentiation but also in diseases. Identification of differentially methylated patterns between case and control groups is important in understanding the mechanism and possible functionality of complex diseases. We propose a method to find possible functionally methylated regions which not only are differentially methylated but also have an effect on gene expression. It integrates methylation and gene expression data and is based on distance discriminant analysis (DDA). In the procedure of identifying differentially methylated regions (DMRs), we do not need to cluster methylation sites or partition the genome in advance. Therefore, the identified DMRs have a larger coverage than those of bump hunting and Ong's methods. Furthermore, through incorporating gene expression data as a complementary source, whether these DMRs are functional is determined through estimating the difference of the corresponding genes. Through a comparison of our approach with bump hunting and Ong's methods for simulation data, it is shown that our method is more powerful in identifying DMRs which have a larger distance in the genome, or only consist of a few sites and have higher sensitivity and specificity. Also, our method is more robust to heterogeneity of data. Applied to different real datasets, we find that most of the functional DMRs are hyper-methylated and located at CpG rich regions (e.g. islands, TSS200 and TSS1500), consistent with the fact that the methylation levels of CpG islands are higher in tumors than normal. Through comparing and analyzing the results of different datasets, we find that the change of methylation in some regions may be related to diseases through changing expression of the corresponding genes, and show the effectiveness of our method.

基于R软件分析两组专家对五个葡萄酒样品的评分数据

本文利用R软件主要讨论了两组专家对五个葡萄酒样品的评分及专家评分的合理性问题。首先，利用两个正态总体均值的假设检验评判两组专家的评分之间是否存在显著差异，从检验结果发现两组专家的评分是基本相符的，从而评比结果有一定的公平性与合理性。其次，利用均值的多重检验考察专家们对不同样品的区分度。在0.05的显著性水平下，专家们能够区分样品1与样品2、样品3、样品5，样品2与样品4，样品3与样品4。对五个样品的等级从高到低排序之后发现，专家们基本上可以区分等级相差为1的样品。但是专家们没有有效地区分出样品1与样品4(等级相差1.5)，样品3与样品5(等级相差1)。然后，运用系统聚类的方法将五个样品分为优、良、差三类。最后，采用距离判别分析法，利用训练样本建立判别函数，将训练样本回代进行判别，得到专家的误判率和正确率，从而利用判别函数对新的样本进行分类。By using R software, we discuss the evaluations of five wine samples by two groups of specialists and the rationality of the evaluations. First of all, by using the hypothesis testing of two normal population means, we judge whether there are significant score differences between two groups of specialists. The test results show consistency of scores of two groups of specialists, and thus the evaluation result has certain fairness and rationality. Secondly, by using multiple t test of the mean, we can investigate the degree of differentiation of different samples by the specialists. Under the significance level of 0.05, the specialists can separate sample 1 from samples 2, 3, and 5, samples 2 and 4, samples 3 and 4. By ordering the levels of five samples from high to low, we find that the specialists can basically distinguish samples with levels with level difference by 1. But specialists do not effectively distinguish samples 1 and 4 (level difference 1.5), samples 3 and 5 (level difference 1). Then we use the hierarchical clustering method to classify five samples to three classes: excellent, good, and bad. Finally, by using the distance discriminant analysis method, the discriminant function is established based on the training sample, then by discrimination of the training sample, we get specialists’ misjudgment rate and accurate rate, and thus we can use the discriminant function to classify the new samples.

An Improved DFT-Based Channel Estimation Algorithm for OFDM System in Non-sample-Spaced Multipath Channels

It is well known that channel impairment caused by multipath reflections can deeply degrade the transmission efficiency in wireless communication systems. The conventional DFT-based channel estimation methods improve the performance by neglecting nonsignificant channel taps. However, in multipath channels with non-sample-spaced time delays, this will cause power leakage and result in an error floor. In order to overcome this problem, based on the property of Channel impulse response, we describe a utility of channel estimation technology for OFDM systems using discriminant analysis. Usually, significant channel taps are detected on the basis of a predetermined threshold, so the optimal threshold value becomes a crucial factor. But it is difficult to decide channel taps that are approximately equal to the threshold value. To solve this disadvantage, the proposed algorithm improves performance by using Mahalanobis distance discriminant analysis for channel taps. This approach can mitigate the aliasing effect in the DFT-based channel estimator and reduce the leakage power efficiently when there is non-sample-spaced path delay. Simulation results show that the proposed channel estimators can eliminate the error floor substantially and its performance is better than the LS and conventional DFT estimators.

FBG sensor network for pressure localization of spacecraft structure based on distance discriminant analysis

Structure used in aircrafts and spacecrafts often undergo high pressure loading that can produce various types of damage, thus prediction of existence and pressure position is important to damage localization. In this paper, the structure type investigated is a 50mm thick sandwich panel with aluminum face-sheets and aluminum honeycomb core. One pressure test was performed on the panels, which was instrumented with 9 FBG sensors. The signals recorded at the various sensor locations varied with regard to Bragg wavelength shift of FBGs. Using this information and combining it with a localization algorithm based on distance discriminant analysis, the pressure location could be successfully determined. A description of the FBG sensors network and the mathematical model to determine the pressure location is provided. The pressure tests on the spacecraft structure, the response of the FBG sensors network and the analysis performed to determine the pressure location are described.

Prediction Methods to Determine Stability of Dam If There is Piping

In the paper, mechanism for generation of piping in dam and key factors that affect the generation of piping are analyzed; eight measured indexes are selected as basis of prediction; such prediction methods as Distance Discriminant Analysis Model and SVM (Support Vector Machine) are established for piping in dam, meanwhile, contrastive analysis for the method has been carried out to neural network method. According to the study of twenty-three actual cases of piping projects in dam, it is showed that Distance Discriminant Method and SVM prediction model are with good performance. SVM that is based on neural network kernel function and Radial Basis Kernel Function has much higher prediction accuracy and SVM method is one effective method to solve issue of piping prediction in dam, which can be used in actual projects.

DFT-Based on Mahalanobis Distance Discriminant Analysis Method Channel Estimation Algorithm for OFDM Systems

An enhanced discrete Fourier transform DFT-based channel estimation for OFDM systems is proposed. Conventional DFT-based channel estimations improve the performance by suppressing time domain noise. However, they potentially require information on channel impulse responses and may also result in mean-square error (MSE) floor due to incorrect channel information such as channel delay spread. In order to overcome the disadvantage, our proposed channel estimation can improve the performance by deciding significant channel taps adaptively. Significant channel taps are detected on the basis of Mahalanobis distance discriminant analysis. Simulation results demonstrate that the proposed algorithm outperforms the conventional DFT-based estimation in terms of BER and MSE performance.

Instability Identification of Goaf Risky in Mines Based on Distance Discriminant Analysis Method

The instability identification of goaf risky is an important work in the mine engineering. Based on distance discriminant analysis, a method to identify the instability of goaf risky in mines was presented in this paper. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation (RQD), area ration of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goaf, were selected as discriminant indexes in the stability analysis of goaf. The actual data of 40 goafs were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was discriminanted by using this model and the identification result is identical with that of practical situation.