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Chinese Journal of Aeronautics 2022年第5期35卷 114-128页

作者： Bingbing GAO Wenmin LI Gaoge HU Yongmin ZHONG Xinhe ZHU School of Automation Northwestern Polytechnical UniversityXi’an 710072China School of Engineering RMIT UniversityBundooraVIC 3083Australia

Inertial Navigation System/Celestial Navigation System(INS/CNS)integration,especially for the tightly-coupled mode,provides a promising autonomous tactics for Hypersonic Vehicle(HV)in military demands.However,INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity,non-additive noise and dynamic complexity.This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter(CKF)to handle the strong INS error model nonlinearity caused by HV's high dynamics.It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements.Subsequently,a technique for the detection of dynamic model uncertainty is developed,and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism.Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements,leading to improved performance for HV navigation with tightly-coupled INS/CNS integration.

关键词： Autonomous integration Fading factor Hypersonic vehicle Inertial navigation systems Kalman filters Mahalanobis distance

An Improved ICCP Gravity Matching Algorithm Based on Mahalanobis distance

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An Improved ICCP Gravity Matching Algorithm Based on Mahalan...

Feature subset selection based on Mahalanobis distance: a statistical rough set method

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作者： Yu Wang Zhihong Deng Wenzhe Zhang Shengwu Zhao Integrated Navigation and Intelligent Navigation Laboratory Beijing Institute of Technology

Consider the problems of low accuracy and poor real-time performance in traditional ICCP gravity matching algorithm,this paper proposes an improved ICCP gravity matching algorithm based on Mahalanobis distance.Mahalanobis distance,instead of Euclidean distance,is applied to improve the objective function of rigid transformation iteration in traditional ICCP algorithm,which can eliminate the interference of variable correlation.At the same time,a new searching strategy based on strip search area is proposed.With the current sampling point as the center,we use the gravimeter measurement error variance to determine the width of the strip region along the normal direction of the contour,and use the Mahalanobis distance objective function value obtained in the iterative process as the length of the strip area along the direction of the contour.Therefore,this searching strategy can gradually narrow the search range of the closest contour point in the ICCP algorithm,and reduce the calculation amount of the algorithm.The simulation results show that:Compared with the traditional ICCP algorithm,the mean value of the position error of the improved algorithm is reduced by 26.89%,and the standard deviation is reduced by 58.53%,which greatly improves the accuracy of the gravity matching algorithm.And the average running time of the improved algorithm is also 42.1% shorter than the traditional ICCP algorithm,so the real-time performance is improved.The improved ICCP gravity matching algorithm based on Mahalanobis distance reduces the running time of the algorithm,improves real-time performance and positioning accuracy,and further improves the practicability of gravity matching for underwater vehicles.

关键词： ICCP matching algorithm Mahalanobis distance Strip-search area

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Journal of Pharmaceutical Analysis 2008年第1期20卷 14-18页

作者：孙亮韩崇昭 School of Electronic and Information Engineering Xi'an Jiaotong University

In order to select effective feature subsets for pattern classification, a novel statistics rough set method is presented based on generalized attribute reduction. Unlike classical reduction approaches, the objects in universe of discourse are signs of training sample sets and values of attributes are taken as statistical parameters. The binary relation and discernibility matrix for the reduction are induced by distance function. Furthermore, based on the monotony of the distance function defined by Mahalanobis distance, the effective feature subsets are obtained as generalized attribute reducts. Experiment result shows that the classification performance can be improved by using the selected feature subsets.

关键词： feature subset selection rough set attribute reduction Mahalanobis distance

Method of Monitoring Wearing and Breakage States of Cutting Tools Based on Mahalanobis distance Features

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厦门大学学报（自然科学版） 2002年第S1期41卷 25-26页

作者： JI Shi-ming, ZHANG Lin-bin, YUAN Ju-long, WAN Yue-hua, ZHANG Xian, ZHANG Li, BAO Guan-jun (Institute of Mechatronics Engineering, Zhejiang University of Technology, Hangzhou 310032, China)

The Mahalanobis distance features proposed by P.C.mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a new method of obtaining Mahalanobis distance features from a tool image is proposed. The key of calculating Mahalanobis distance is appropriately dividing the object into several component sets. Firstly, a technique is proposed that can automatically divide the component groups for calculating Mahalanobis distance based on the gray level of wearing or breakage regions in a tool image. The wearing region can be divided into high gray level component group and the tool-blade into low one. Then, the relation between Mahalanobis distance features of component groups and tool conditions is investigated. The results indicate that the high brightness region on the flank surface of the turning tool will change with its abrasion change and if the tool is heavily abraded, the area of high brightness will increase apparently. The Mahalanobis distance features of high gray level component group are related with wearing state of tool and low gray level component group correlated with breakage of tool. The experimental results show that the abrasion of the tool’s flank surface affected the Mahalanobis distances of high brightness component of the tool and the pixels of high brightness component set. Compared with the changes of them, we found that the Mahalanobis distance of high brightness component of the tool was more sensitive to the abrasion of cutting tool than the area of high brightness component set of the tool. Here we found that the relative changing rate of the area of high brightness component set was not quite obvious and it was ranging from 2% to 15%, while the relative changing rate of the Mahalanobis distance in table 1 ranges from 13.9% to 47%. It is 3 times higher than the changing rate of the area.

关键词： Mahalanobis distance tool condition monitoring image processing

Exploratory Data Analysis Applied in Mapping Multi-element Soil Geochemical Anomalies for Drill Target Definition:A Case Study from the Unpha Layered Non-magmatic Hydrothermal Pb-Zn Deposit,DPR Korea

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Acta Geologica Sinica(English Edition) 2021年第4期95卷 1357-1365页

作者： JANG Gwang-Hyok WON Hyon-Chol HWANG Bo-Hyon CHOI Chol-Man Faculty of Geology Kim Il Sung UniversityPyongyangDemocratic People's Republic of Korea

A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralization,was selected for interpretation.The median+2 MAD(median absolute deviation)method of exploratory data analysis(EDA)and C-A(concentration-area)fractal modeling were then applied to the Mahalanobis distance,as defined by Zn,Cu and Pb from the factor analysis to set the thresholds for defining multi-element anomalies.As a result,the median+2 MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal model.The soil anomaly identified by the median+2 MAD method on the Mahalanobis distances defined by three principal elements(Zn,Cu and Pb)rather than thirteen elements(Co,Zn,Cu,V,Mo,Ni,Cr,Mn,Pb,Ba,Sr,Zr and Ti)was the more favorable reflection of the ore body.The identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for validation.The results showed that the median+2 MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization,which is therefore useful at the reconnaissance drilling stage.

关键词： factor analysis exploratory data analysis Mahalanobis distance multi-element Unpha

Data-driven Transient Stability Assessment Based on Kernel Regression and distance Metric Learning

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 27-36页

作者： Xianzhuang Liu Yong Min Lei Chen Xiaohua Zhang Changyou Feng the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China State Grid Jibei Electric Power Company Beijing 100053China State Grid Corporation of China Beijing 100031China

Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

关键词： Transient stability assessment(TSA) critical clearing time(CCT) conservativeness level distance metric learning Nadaraya-Watson kernel regression Mahalanobis distance nonparametric regression data-driven

ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

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Computers, Materials & Continua 2021年第11期69卷 1447-1462页

作者： Sundresan Perumal Mujahid Tabassum Ganthan Narayana Suresh Ponnan Chinmay Chakraborty Saju Mohanan Zeeshan Basit Mohammad Tabrez Quasim Faculty of Science and Technology Universiti Sains Islam MalaysiaNilai71800Malaysia Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology600062TamilnaduIndia Department Electronics&Communication Engineering Birla Institute of TechnologyMesraJharkhandIndia Faulty of Information Technology University of Technology and Applied SciencesMuscat133Oman College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.

关键词： AODV artificial neural network artificial intelligence Mahalanobis distance node failure throughput wireless sensor network

Fusion Fault Diagnosis Approach to Rolling Bearing with Vibrational and Acoustic Emission Signals

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Computer Modeling in Engineering & Sciences 2021年第11期129卷 1013-1027页

作者： Junyu Chen Yunwen Feng Cheng Lu Chengwei Fei School of Aeronautics Northwestern Polytechnical UniversityXi’an710072China Department of Aeronautics and Astronautics Fudan UniversityShanghai200433China

As the key component in aeroengine rotor systems,the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems.In order to monitor rolling bearing conditions,a fusion fault diagnosis method,namely empirical mode decomposition(EMD)-Mahalanobis distance(E2MD)and improved wavelet threshold(IWT)(E2MD-IWT)for vibrational signals and acoustic emission(AE)signals is developed to improve the diagnostic accuracy of rolling bearings.The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold.Moreover,it is shown to be effective through numerical simulation.EMD is utilized to process the original AE signals for rolling bearings so as to generate a set of components called intrinsic modes functions(IMFs).The Mahalanobis distance(MD)approach is introduced in order to determine the smallest MD between the original AE signal and IMF components.Then,the IWT approach is employed to select the IMF components with the largest MD.It is demonstrated that the proposed E2MD-IWT method for vibrational and AE signals can improve rolling bearing fault diagnosis,beyond its ability to effectively eliminate noise signals.This study offers a promising approach to fault diagnosis for rolling bearings in aeroengines with regard to vibration signals and AE signals.

关键词： Empirical mode decomposition Mahalanobis distance improved wavelet threshold rolling bearings

A Hardware Trojan Detection Method Based on the Electromagnetic Leakage

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China Communications 2019年第12期16卷 100-110页

作者： Lei Zhang Youheng Dong Jianxin Wang Chaoen Xiao Ding Ding Beijing Electronic Science and Technology Institute

Hardware Trojan(HT) refers to a special module intentionally implanted into a chip or an electronic system. The module can be exploited by the attacker to achieve destructive functions. Unfortunately the HT is difficult to detecte due to its minimal resource occupation. In order to achieve an accurate detection with high efficiency, a HT detection method based on the electromagnetic leakage of the chip is proposed in this paper. At first, the dimensionality reduction and the feature extraction of the electromagnetic leakage signals in each group(template chip, Trojan-free chip and target chip) were realized by principal component analysis(PCA). Then, the Mahalanobis distances between the template group and the other groups were calculated. Finally, the differences between the Mahalanobis distances and the threshold were compared to determine whether the HT had been implanted into the target chip. In addition, the concept of the HT Detection Quality(HTDQ) was proposed to analyze and compare the performance of different detection methods. Our experiment results indicate that the accuracy of this detection method is 91.93%, and the time consumption is 0.042s in average, which shows a high HTDQ compared with three other methods.

关键词： hardware trojan detection side channel analysis electromagnetic leakage principal component analysis Mahalanobis distance detection quality

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

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Geoscience Frontiers 2021年第1期12卷 425-439页

作者： Shuo Zheng Yu-Xin Zhu Dian-Qing Li Zi-Jun Cao Qin-Xuan Deng Kok-Kwang Phoon State Key Laboratory of Water Resources and Hydropower Engineering Science Institute of Engineering Risk and Disaster PreventionWuhan University299 Bayi RoadWuhan 430072China Department of Civil and Environmental Engineering National University of SingaporeBlk E1A#07-031 Engineering Drive 2Singapore 117576Singapore

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.

关键词： Outlier detection Site investigation Sparse multivariate data Mahalanobis distance Resampling by half-means Bayesian machine learning