The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance chemical (AKMD-t-SNE) is proposed to realize multimodal monitoring. First, matrix strategy utilized ensure sample contains autoco...

Abstract Objectives This study presents a method combining one-class classifier and laser-induced breakdown spectrometry (LIBS) to quickly identify healthy Tegillarca granosa (T. granosa). Materials Methods The sum of ranking differences (SRD) was used fuse multiple anomaly detection metrics build the classifier, which only trained with T. granosa. can exclude non-healthy proposed calculated st...

Detection of damages caused by natural disasters is a delicate and difficult task due to the time constraints imposed by emergency situations. Therefore, an automatic Change Detection (CD) algorithm, with less user interaction, is always very interesting and helpful. So far, there is no existing CD approach that is optimal and applicable in the case of (a) labeled samples not existing in the st...

kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for face classification and other applications. However, the standard KCCA algorithm may suffer from computational problem as the training set increase. To overcome the drawback, we propose a threestage method to improve...

We propose a completely unsupervised pixel-wise anomaly detection (AD) method for hyperspectral images (HSIs). The proposed consists of three steps called data preparation, reconstruction, and detection. In the preparation step, we apply background purification to train deep network in an manner. reconstruction use different autoencoding adversarial (AEAN) models including 1-D-AEAN, 2-D-AEAN, 3...

While the statistical properties of images are vital in forestry engineering, usefulness these various tasks may vary, and certain image might not be enough to adequately describe a particular tree species. To address this problem, we propose novel method comprehensively analyze relationship between different species, determine subset features that best each individual In study, employed quanti...

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum the recent years among potential users. Connected Autonomous Electric Vehicle (CAEV) technologies are fascinating automakers inducing them to manufacture connected autonomous with self-driving features such autopilot self-parking. Therefore, Traffic Flow ...

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance sy...