Statistics local fisher discriminant analysis for industrial process fault classification

In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods

Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

In recent years, the development of deep learning is noticeably influencing the progress of computational fluid dynamics. Numerous researchers have undertaken flow field predictions on a variety of grids, such as MAC grids, structured grids, unstructured meshes, and pixel-based grids which have been many works focused on. However, predicting unsteady flow fields on unstructured meshes remains challenging. When employing graph neural networks (GNNs) for these predictions, the message-passing mechanism can become inefficient, especially with denser unstructured meshes. Furthermore, unsteady flow field predictions often rely on autoregressive neural networks, which are susceptible to error accumulation during extended predictions. In this study, we integrate the traditional finite volume method to devise a spatial integration strategy that enables the formulation of a physically constrained loss function. This aims to counter the error accumulation that emerged in autoregressive neural networks during long-term predictions. Concurrently, we merge vertex-center and cell-center grids from the finite volume method, introducing a dual message-passing mechanism within a single GNN layer to enhance the message-passing efficiency. We benchmark our approach against MeshGraphnets for unsteady flow field predictions on unstructured meshes. Our findings indicate that the methodologies combined in this study significantly enhance the precision of flow field predictions while substantially minimizing the training time cost. We offer a comparative analysis of flow field predictions, focusing on cylindrical, airfoil, and square column obstacles in two-dimensional incompressible fluid dynamics scenarios. This analysis encompasses lift coefficient, drag coefficient, and pressure coefficient distribution comparison on the boundary layers

Special Cases in Cataract Surgery

During phacoemulsification for cataracts, the surgeon may encounter various challenges and should therefore be trained to handle them. This chapter will share an example of clinical cases encountered by the author in clinical practice, which mainly includes the successful implantation of a trifocal intraocular lens in the capsular bag after posterior capsular tear during posterior polar cataract surgery as well as cataract surgery design after corneal refractive surgery, shrinkage, and treatment of capsular opening in patients with retinitis pigmentosa after cataract surgery to provide a reference for clinicians

Incidence and Risk Factors for Berger's Space Development after Uneventful Cataract Surgery : Evidence from Swept-Source Optical Coherence Tomography

Background: This study investigates the incidence and risk factors for the development of Berger's space (BS) after uneventful phacoemulsification based on swept-source optical coherence tomography (SS-OCT). Methods: Cataractous eyes captured using qualified SS-OCT images before and after uneventful phacoemulsification cataract surgery were included. Six high-resolution cross-sectional anterior segment SS-OCT images at 30 degrees intervals were used for BS data measurements. BS width was measured at three points on each scanned meridian line: the central point line aligned with the cornea vertex and two point lines at the pupil's margins. Results: A total of 223 eyes that underwent uneventful cataract surgery were evaluated. Preoperatively, only two eyes (2/223, 0.9%) were observed to have consistent BS in all six scanning directions. BS was observed postoperatively in 44 eyes (44/223, 19.7%). A total of 13 eyes (13/223, 5.8%) with insufficient image quality, pupil dilation, or lack of preoperative image data were excluded from the study. A total of 31 postoperative eyes with BS and 31 matched eyes without BS were included in the final data analysis. The smallest postoperative BS width was in the upper quadrant of the vertical meridian line (90 degrees), with a mean value of 280 mu m. The largest BS width was observed in the opposite area of the main clear corneal incision, with a mean value >500 mu m. Conclusions: Uneven-width BS is observable after uneventful phacoemulsification. Locations with a much wider BS (indirect manifestation of Wieger zonular detachment) are predominantly located in the opposite direction to the main corneal incisions.Peer reviewe

UAV first view landmark localization with active reinforcement learning

We present an active reinforcement learning framework for unmanned aerial vehicle (UAV) first view landmark localization. We formulate the problem of landmark localization as that of a Markov decision process and introduce an active landmark-localization network (ALLNet) to address it. The aim of the ALLNet is to locate a bounding box that surrounds the landmark in a first view image sequence. To this end, it is trained in a reinforcement learning fashion. Specifically, it employs support vector machine (SVM) scores on the bounding box patches as rewards and learns the bounding box transformations as actions. Furthermore, each SVM score indicates whether or not the landmark is detected by the bounding box such that it enables the ALLNet to have the capability of judging whether the landmark leaves or re-enters a first view image. Therefore, the operation of the ALLNet is not only dominated by the reinforcement learning process but also supplemented by an active learning motivated manner. Once the landmark is considered to leave the first view image, the ALLNet stops operating until the SVM detects its re-entry to the view. The active reinforcement learning model enables training a robust ALLNet for landmark localization. The experimental results validate the effectiveness of the proposed model for UAV first view landmark localization