The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extr...The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.展开更多
Fingerprint is a very popular and an ancient biometric technology to uniquely identify a person. In this paper, a fingerprint matcher is proposed which uses the global and local adaptive binarization and global minuti...Fingerprint is a very popular and an ancient biometric technology to uniquely identify a person. In this paper, a fingerprint matcher is proposed which uses the global and local adaptive binarization and global minutia features. The fingerprint data is collected using three different authentication devices based on optical sensors. The experimental results are compared with the National Institute of Standards and Technology （NIST） Bozorth algorithm and various authentication fingerprint sensors. The accuracy of the proposed algorithm has been improved significantly compared with that of the NIST Bozorth algorithm.展开更多
De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing ...De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing the instances of multiple enrollments by the same person using the person＇s biometric data. An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions. This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner. De-duplication accuracy decreases because of the noise present in the data, which would cause improper slap fingerprint segmentation. In this paper, an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood. The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplica- tion accuracy. Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.展开更多
基金related to grant PM-31-22-626-414 from the Prime Minister's Research Fellows(PMRF)of the Indian Institute of Technology Roorkee。
文摘The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.
文摘Fingerprint is a very popular and an ancient biometric technology to uniquely identify a person. In this paper, a fingerprint matcher is proposed which uses the global and local adaptive binarization and global minutia features. The fingerprint data is collected using three different authentication devices based on optical sensors. The experimental results are compared with the National Institute of Standards and Technology （NIST） Bozorth algorithm and various authentication fingerprint sensors. The accuracy of the proposed algorithm has been improved significantly compared with that of the NIST Bozorth algorithm.
文摘De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population. De-duplication is the process of removing the instances of multiple enrollments by the same person using the person＇s biometric data. An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions. This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner. De-duplication accuracy decreases because of the noise present in the data, which would cause improper slap fingerprint segmentation. In this paper, an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood. The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplica- tion accuracy. Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.
