Condition-based maintenance (CBM) of critical high-valued machines is a typical Internet of Thing... more Condition-based maintenance (CBM) of critical high-valued machines is a typical Internet of Things (IoT) scenario. Key to CBM is monitoring important machine health parameters, such that maintenance can be based on the perceived condition of the machine, rather than performing preventive maintenance at intervals, where the likelihood of failures between repairs is very small, or performing corrective maintenance by running the machine until it fails and repairing it. Vibration data analysis is a key tool for understanding the internal condition of a machine, often when it is running continuously. This paper illustrates how to use the new digital filters, time-frequency analysis tools, machine learning algorithms available in SAS Visual Data Mining and Machine Learning, and SAS Event Stream Processing to monitor the real-time condition of a “live” variable-speed rotating machine by analyzing vibration sensor measurements obtained at sampling rates higher than 10,000 Hz.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
This paper presents a method for hyperspectral image classification that uses support vector data... more This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. This paper proposes a new automatic, unsupervised Gaussian kernel bandwidth selection approach which is used with a multiclass SVDD classification scheme. The performance of the multiclass SVDD classification scheme is evaluated on three frequently used hyperspectral data sets, and preliminary results show that the proposed method can achieve better performance than published results on these data sets.
IEEE Transactions on Geoscience and Remote Sensing, 2007
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a groundpenetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data.
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.
IEEE Transactions on Geoscience and Remote Sensing, 2007
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a groundpenetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data.
The mammalian visual system is still the gold standard for recognition accuracy, flexibility, eff... more The mammalian visual system is still the gold standard for recognition accuracy, flexibility, efficiency, and speed. Ongoing advances in our understanding of function and mechanisms in the visual system can now be leveraged to pursue the design of computer vision architectures that will revolutionize the state of the art in computer vision.
This paper describes a novel approach for the detection and classification of man-made objects us... more This paper describes a novel approach for the detection and classification of man-made objects using discriminating features derived from higher-order spectra (HOS), defined in terms of higher-order moments of hyperspectral-signals. Many existing hyperspectral analysis techniques are based on linearity assumptions. However, recent research suggests that significant nonlinearity arises due to multipath scatter, as well as spatially varying atmospheric water vapor concentrations. Higher-order spectra characterize subtle complex nonlinear dependencies in spectral phenomenology of objects in hyperspectral data and are insensitive to additive Gaussian noise. By exploiting these HOS properties, we have devised a robust method for classifying man-made objects from hyerspectral signatures despite the presence of strong background noise, confusers with spectrally similar signatures and variable signal-to-noise ratios. We tested classification performance hyperspectral imagery collected from several different sensor platforms and compared our algorithm with conventional classifiers based on linear models. Our experimental results demonstrate that our HOS algorithm produces significant reductions in false alarms. Furthermore, when HOS-based features were combined with standard features derived from spectral properties, the overall classification accuracy is substantially improved.
Condition-based maintenance (CBM) of critical high-valued machines is a typical Internet of Thing... more Condition-based maintenance (CBM) of critical high-valued machines is a typical Internet of Things (IoT) scenario. Key to CBM is monitoring important machine health parameters, such that maintenance can be based on the perceived condition of the machine, rather than performing preventive maintenance at intervals, where the likelihood of failures between repairs is very small, or performing corrective maintenance by running the machine until it fails and repairing it. Vibration data analysis is a key tool for understanding the internal condition of a machine, often when it is running continuously. This paper illustrates how to use the new digital filters, time-frequency analysis tools, machine learning algorithms available in SAS Visual Data Mining and Machine Learning, and SAS Event Stream Processing to monitor the real-time condition of a “live” variable-speed rotating machine by analyzing vibration sensor measurements obtained at sampling rates higher than 10,000 Hz.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
This paper presents a method for hyperspectral image classification that uses support vector data... more This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. This paper proposes a new automatic, unsupervised Gaussian kernel bandwidth selection approach which is used with a multiclass SVDD classification scheme. The performance of the multiclass SVDD classification scheme is evaluated on three frequently used hyperspectral data sets, and preliminary results show that the proposed method can achieve better performance than published results on these data sets.
IEEE Transactions on Geoscience and Remote Sensing, 2007
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a groundpenetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data.
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.
IEEE Transactions on Geoscience and Remote Sensing, 2007
Numerous detection algorithms, using various sensor modalities, have been developed for the detec... more Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a groundpenetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data.
The mammalian visual system is still the gold standard for recognition accuracy, flexibility, eff... more The mammalian visual system is still the gold standard for recognition accuracy, flexibility, efficiency, and speed. Ongoing advances in our understanding of function and mechanisms in the visual system can now be leveraged to pursue the design of computer vision architectures that will revolutionize the state of the art in computer vision.
This paper describes a novel approach for the detection and classification of man-made objects us... more This paper describes a novel approach for the detection and classification of man-made objects using discriminating features derived from higher-order spectra (HOS), defined in terms of higher-order moments of hyperspectral-signals. Many existing hyperspectral analysis techniques are based on linearity assumptions. However, recent research suggests that significant nonlinearity arises due to multipath scatter, as well as spatially varying atmospheric water vapor concentrations. Higher-order spectra characterize subtle complex nonlinear dependencies in spectral phenomenology of objects in hyperspectral data and are insensitive to additive Gaussian noise. By exploiting these HOS properties, we have devised a robust method for classifying man-made objects from hyerspectral signatures despite the presence of strong background noise, confusers with spectrally similar signatures and variable signal-to-noise ratios. We tested classification performance hyperspectral imagery collected from several different sensor platforms and compared our algorithm with conventional classifiers based on linear models. Our experimental results demonstrate that our HOS algorithm produces significant reductions in false alarms. Furthermore, when HOS-based features were combined with standard features derived from spectral properties, the overall classification accuracy is substantially improved.
Uploads
Papers by Yuwei Liao