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...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosio...This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosion,which lead to increased loss of cold resources.The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction.This methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall performance.The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel.The mathematical model is constructed quantitatively using the improved Mahalanobis distance.Furthermore,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction.This model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature characteristics.This model facilitates the objective examination and prediction of the progression of equipment deterioration trends.The present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this research.These models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.展开更多
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 mult...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.展开更多
The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classifi...The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.展开更多
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 difficu...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.展开更多
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 mineralizatio...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.展开更多
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 th...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.展开更多
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...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.展开更多
We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a n...We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. Using data of 20 major stock markets, including G10 economies, we find spikes in our EMR index during the dotcom bubble, the global financial crisis, the European sovereign debt crisis, and the novel coronavirus pandemic. The result shows that the global financial crisis and the COVID-19 induced crisis record the historic highest spikes in financial market risk, suggesting stronger evidence of contagion in both periods.展开更多
In this short review paper, the significant and profound impacts of the Chou’s “invariance theorem” have been briefly presented with crystal clear convincingness.
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 ...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.展开更多
The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operati...The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operation process generally consists of four specialised stages,namely,Mahalanobis space(MS)construction,MS validation,MS optimisation,and diagnosis and prognosis.In recent years,a significant amount of research work has been conducted on the improvement of key technologies and these four processes.Numerous applications of MTS have also been studied in various fields.There are,of course,excellent reviews of theoretical research and applied research of MTS.However,there is no systematic review that covers both theoretical and applied research comprehensively.To fill this gap,this paper reviews MTS from key technolo-gies,four processes and application research,and provides researchers with a discussion of the current situation,upcoming challenges and possible future trends.展开更多
A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards.The main challenge in such an orchard is the collection of fruits and nuts lying on the floor.The manual collection of fruits a...A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards.The main challenge in such an orchard is the collection of fruits and nuts lying on the floor.The manual collection of fruits and nuts is both time consuming and labour intensive.The scenario begs for automated collection of fruits and nuts.There are methods developed in research for continuous crop fields,but none for discrete coverage.The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated.A novel Mahalanobis distance based partitioning approach for performing coverage is introduced.The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions.Optimization of the generated paths is achieved through a combination of local and global search techniques.This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator.A case study is formulated for the fruit picking operations in the orchards of Puducherry.The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate,convergence diversity and deviation ratio.The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively.The deviation ratio was 0.02 percent and 2.16 percent,with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes.The achieved solutions was on par with the global best solutions achieved so far for the test datasets.展开更多
To distinguish species or populations using morphometric data is generally processed through multivariate analyses,in particular the discriminant analysis.We explored another approach based on the maximum likelihood m...To distinguish species or populations using morphometric data is generally processed through multivariate analyses,in particular the discriminant analysis.We explored another approach based on the maximum likelihood method.Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data(or sample) in a given reference group.When data are described by more than one variable,the maximum likelihood(MLi) approach allows to combine these chances to find the best fit for the data.Such approach assumes independence between variables.The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics,but improvements may be obtained after some mathematical transformations.Provided there is strict anatomical correspondence of variables between unknown and reference data,the MLi classification produces consistent classification.We explored this approach using various input data,and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification.The simplicity of the method,its fast computation,performance and versatility,make it an interesting complement to other classification techniques.展开更多
Aim:This paper addresses the assessment of the composition of a general wound,in terms of all identifiable categories of tissue and pigmentation in an attempt to improve accuracy in assessing and monitoring wound heal...Aim:This paper addresses the assessment of the composition of a general wound,in terms of all identifiable categories of tissue and pigmentation in an attempt to improve accuracy in assessing and monitoring wound health.Materials and Methods:A knowledgebase of clusters was built into the hue,saturation and intensity(HSI)color space and then used for assessing wound composition.Based on the observation that the clusters are fairly distinct,two different algorithms,that is,Mahalanobis distance(MD)based and the rotated coordinate system(RCS)method,were used for classification.These methods exploit the shape,spread,and orientation of each cluster.Results:The clusters in the HSI color space,built from about 9,000(calibrated)pixels from 48 images of various wound beds,showed 8 fairly distinct regions.The inter-cluster distances were consistent with the visual appearance.The efficacy of the MD and RCS based methods in 120 experiments taken from a set of 15 test images(in terms of average percent-match)was found to be 91.55 and 93.71,respectively.Conclusion:Our investigations established eight categories of tissue and pigmentation in wound beds.These findings help to determine the stage of wound healing more accurately and comprehensively than typically permitted through use of the 4-color model reported in the literature for addressing specific wound types.展开更多
基金This work was supported by the National Basic Research Program of China(No.2001CB309403)
文摘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.
文摘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.
基金co-supported by the National Natural Science Foundation of China(Nos.41904028,42004021)the Natural Science Basic Research Plan in Shaanxi Province of China(Nos.2020JQ-150,2020JQ-234)the Soft Science Project of Xi’an Science and Technology Plan(No.XA2020RKXYJ-0150)。
文摘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.
基金supported by the National Natural Science Foundation of China (51906133).
文摘This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosion,which lead to increased loss of cold resources.The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction.This methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall performance.The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel.The mathematical model is constructed quantitatively using the improved Mahalanobis distance.Furthermore,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction.This model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature characteristics.This model facilitates the objective examination and prediction of the progression of equipment deterioration trends.The present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this research.These models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.
基金supported by the National Key R&D Program of China(Project No.2016YFC0800200)the NRF-NSFC 3rd Joint Research Grant(Earth Science)(Project No.41861144022)+2 种基金the National Natural Science Foundation of China(Project Nos.51679174,and 51779189)the Shenzhen Key Technology R&D Program(Project No.20170324)The financial support is grateful acknowledged。
文摘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.
基金supported by the Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.JB190501)Science and Technology Innovation Team of Shaanxi Province(No.2019TD-002)National Natural Science Foundation of China(No.11774277)。
文摘The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.
基金supported by the Special Funds for Basic Scientific Research Business Expenses of Central Universities No. 2014GCYY0the Beijing Natural Science Foundation No. 4163076the Fundamental Research Funds for the Central Universities No. 328201801
文摘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.
文摘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.
文摘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.
基金This paper is supported by the National Natural Science Foundation of China(Grant No.51875465)the Civil Aircraft Scientific Research Project.The authors would like to thank them.
文摘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.
文摘We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. Using data of 20 major stock markets, including G10 economies, we find spikes in our EMR index during the dotcom bubble, the global financial crisis, the European sovereign debt crisis, and the novel coronavirus pandemic. The result shows that the global financial crisis and the COVID-19 induced crisis record the historic highest spikes in financial market risk, suggesting stronger evidence of contagion in both periods.
文摘In this short review paper, the significant and profound impacts of the Chou’s “invariance theorem” have been briefly presented with crystal clear convincingness.
基金supported by National Key R&D Program of China (No.2018YFB0904500)State Grid Corporation of China。
文摘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.
文摘The Mahalanobis-Taguchi system(MTS)is a relatively new multi-dimensional pattern recogni-tion technology that combines Mahalanobis distance(MD)with Taguchi’s robust engineering for diagnosis and prognosis.MTS operation process generally consists of four specialised stages,namely,Mahalanobis space(MS)construction,MS validation,MS optimisation,and diagnosis and prognosis.In recent years,a significant amount of research work has been conducted on the improvement of key technologies and these four processes.Numerous applications of MTS have also been studied in various fields.There are,of course,excellent reviews of theoretical research and applied research of MTS.However,there is no systematic review that covers both theoretical and applied research comprehensively.To fill this gap,this paper reviews MTS from key technolo-gies,four processes and application research,and provides researchers with a discussion of the current situation,upcoming challenges and possible future trends.
基金The work was supported by University Grants Commission,India under the scheme NFOBC with grant no.F./201718/NF O201718OBCPON51035.The authors would like to thank the people of Namalavar Cashew Farmers Association,Puducherry for their inputs.
文摘A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards.The main challenge in such an orchard is the collection of fruits and nuts lying on the floor.The manual collection of fruits and nuts is both time consuming and labour intensive.The scenario begs for automated collection of fruits and nuts.There are methods developed in research for continuous crop fields,but none for discrete coverage.The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated.A novel Mahalanobis distance based partitioning approach for performing coverage is introduced.The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions.Optimization of the generated paths is achieved through a combination of local and global search techniques.This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator.A case study is formulated for the fruit picking operations in the orchards of Puducherry.The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate,convergence diversity and deviation ratio.The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively.The deviation ratio was 0.02 percent and 2.16 percent,with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes.The achieved solutions was on par with the global best solutions achieved so far for the test datasets.
基金financed by the Chaires Merieux foundation(Paris,France)Pontifical Catholic University of Ecuador(M 13480)
文摘To distinguish species or populations using morphometric data is generally processed through multivariate analyses,in particular the discriminant analysis.We explored another approach based on the maximum likelihood method.Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data(or sample) in a given reference group.When data are described by more than one variable,the maximum likelihood(MLi) approach allows to combine these chances to find the best fit for the data.Such approach assumes independence between variables.The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics,but improvements may be obtained after some mathematical transformations.Provided there is strict anatomical correspondence of variables between unknown and reference data,the MLi classification produces consistent classification.We explored this approach using various input data,and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification.The simplicity of the method,its fast computation,performance and versatility,make it an interesting complement to other classification techniques.
文摘Aim:This paper addresses the assessment of the composition of a general wound,in terms of all identifiable categories of tissue and pigmentation in an attempt to improve accuracy in assessing and monitoring wound health.Materials and Methods:A knowledgebase of clusters was built into the hue,saturation and intensity(HSI)color space and then used for assessing wound composition.Based on the observation that the clusters are fairly distinct,two different algorithms,that is,Mahalanobis distance(MD)based and the rotated coordinate system(RCS)method,were used for classification.These methods exploit the shape,spread,and orientation of each cluster.Results:The clusters in the HSI color space,built from about 9,000(calibrated)pixels from 48 images of various wound beds,showed 8 fairly distinct regions.The inter-cluster distances were consistent with the visual appearance.The efficacy of the MD and RCS based methods in 120 experiments taken from a set of 15 test images(in terms of average percent-match)was found to be 91.55 and 93.71,respectively.Conclusion:Our investigations established eight categories of tissue and pigmentation in wound beds.These findings help to determine the stage of wound healing more accurately and comprehensively than typically permitted through use of the 4-color model reported in the literature for addressing specific wound types.
