The flexible thin-wall bearing, characterized by different kinematic attributes and fault
characteristic frequency in comparison to rolling bearings, introduces a great challenge in …

This research paper presents the novel face recognition (FR) approach by introducing the
novel descriptor so-called directional binary pattern (DBP). The directional features are …

Deep clustering outperforms traditional methods by incorporating feature learning. However,
some existing deep clustering methods overlook the suitability of the learned features for …

Discriminative dictionary learning (DDL) has attracted significant attention in the field of
image classification. To enhance the classification performance, most existing discriminative …

Francis turbine is one of the most commonly used hydraulic machinery for hydroelectric
power generation, but its stability is frequently jeopardized by vortex rope. It is crucial to …

In this paper, a novel structured collaborative sparse dictionary learning approach is
proposed to improve the monitoring performance of discriminative dictionary learning for …

Among several local descriptors invented in literature, the local binary pattern (LBP) is the
prolific one. Despite its advantages like low computational complexity and monotonic gray …

In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of
bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with …

We propose a novel, lightweight supervised dictionary learning framework for text
classification based on data compression and representation. This two-phase algorithm …

Fault diagnosis of aero-engine inter-shaft bearing under variable operating conditions poses
a significant challenge in the industry. Existing sparse classification methods with shallow …