Fairing wireframes in industrial surface design

Wireframe is a modeling tool widely used in industrial geometric design. The term wireframe refers to two sets of curves, with the property that each curve from one set intersects with each curve from the other set. Akin to the mu-, v-isocurves in a tensor-product surface, the two sets of curves in a wireframe span an underlying surface. In many industrial design activities, wireframes are usually set up and adjusted by the designers before the whole surfaces are reconstructed. For adjustment, the fairness of wireframe has a direct influence on the quality of the underlying surface. Wireframe fairing is significantly different from fairing individual curves in that intersections should be preserved and kept in the same order. In this paper, we first present a technique for wireframe fairing by fixing the parameters during fairing. The limitation of fixed parameters is further released by an iterative gradient descent optimization method with step-size control. Experimental results show that our solution is efficient, and produces reasonably fairing results of the wireframes

Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.Comment: 12 pages, 8 figure

Note on industrial applications of Hu's surface extension algorithm

An important surface modeling problem in CAD is to connect two disjoint B-spline patches with the second-order geometric continuity. In this paper we present a study to solve this problem based on the surface extension algorithm [Computer-Aided Design 2002; 34:415–419]. Nice properties of this extension algorithm are exploited in depth and thus make our solution very simple and efficient. Various practical examples are presented to demonstrate the usefulness and efficiency of our presented solution

Exploring channel properties to improve singing voice detection with convolutional neural networks

Singing voice detection is still a challenging task because the voice can be obscured by instruments having the same frequency band, and even the same timbre, produced by mimicking the mechanism of human singing. Because of the poor adaptability and complexity of feature engineering, there is a recent trend towards feature learning in which deep neural networks play the roles of feature extraction and classification. In this paper, we present two methods to explore the channel properties in the convolution neural network to improve the performance of singing voice detection by feature learning. First, channel attention learning is presented to measure the importance of a feature, in which two attention mechanisms are exploited, i.e., the scaled dot-product and squeeze-and-excitation. This method focuses on learning the importance of the feature map so that the neurons can place more attention on the more important feature maps. Second, the multi-scale representations are fed to the input channels, aiming at adding more information in terms of scale. Generally, different songs need different scales of a spectrogram to be represented, and multi-scale representations ensure the network can choose the best one for the task. In the experimental stage, we proved the effectiveness of the two methods based on three public datasets, with the accuracy performance increasing by up to 2.13 percent compared to its already high initial level.</p

Study on Asymmetric Failure and Control Measures of Lining in Deep Large Section Chamber

Lining is often used as the last line of defense in deep large section chamber. Under the asymmetric load, it is easy to damage, resulting in the overall repair of the chamber. Aiming at this problem, taking the pump house in Wanfu Coal Mine under construction in China as an engineering example, we analyzed the asymmetric failure of pump house lining caused by construction disturbance, established the lining mechanical model and quantitative evaluation indexes, such as bending moment change rate, bending moment balance rate, displacement change rate and displacement balance rate, studied the influence mechanism of asymmetrical coefficient, section size and lining thickness on the mechanical behavior of lining, and proposed the control measures of deep large section chamber with the core of “strengthening asymmetric support, reducing section size and improving lining strength”. The field monitoring shows that the asymmetric deformation of the pump house is effectively controlled, and the maximum displacement is only 7.3 mm, which ensures the long-term stability of the chamber

Component-based SHGC determination of BIPV glazing for product comparison

Publisher Copyright: © 2024 The Author(s)Building-integrated photovoltaic (BIPV) systems are intrinsically designed to generate electricity and to provide at least one building-related function. When BIPV modules act as glazing products in windows, skylights or curtain walls, their ability to control the transmission of solar energy into the building must be characterised by a Solar Heat Gain Coefficient (SHGC) or g value (also known as Total Solar Energy Transmittance – TSET – or “solar factor”). For the comparison of BIPV glazing products consisting of one PV laminate and possibly further, conventional glazing layers separated by gas-filled cavities, the procedures documented in international standards for architectural glazing (e.g. ISO 9050 and EN 410) form a suitable starting point. Easily implemented modifications to these procedures are proposed to take both optical inhomogeneity (if relevant) and extraction of electricity from BIPV glazing units into account. Geometrically complex glazing and shading devices, and light-scattering glazing layers, are outside the scope of the proposed methodology; SHGC determination for obliquely incident solar radiation is also excluded. For these cases, the experimental calorimetric approach documented in [ISO 19467:2017; ISO 19467-2:2021] is recommended. The paper also presents results and conclusions from an implementation exercise and sensitivity study carried out by participants of the IEA-PVPS Task 15 on BIPV. The cell coverage ratio in the PV laminate, the thermal resistance offered by the glazing configuration, the choice of boundary conditions and the effect of extracting electricity were all identified as parameters which significantly affect the SHGC value determined for a given type of BIPV glazing. A practicable approach to accommodate the great variety of dimensions typical for BIPV glazing is also proposed. These findings should pave the way for modifying the existing component-based standards for architectural glazing to take the specific features of BIPV glazing into account.Peer reviewe

Distinct histopathological phenotypes of severe alcoholic hepatitis suggest different mechanisms driving liver injury and failure

Intrahepatic neutrophil infiltration has been implicated in severe alcoholic hepatitis (SAH) pathogenesis; however, the mechanism underlying neutrophil-induced injury in SAH remains obscure. This translational study aims to describe the patterns of intrahepatic neutrophil infiltration and its involvement in SAH pathogenesis. Immunohistochemistry analyses of explanted livers identified two SAH phenotypes despite a similar clinical presentation, one with high intrahepatic neutrophils (Neuhi), but low levels of CD8+ T cells, and vice versa. RNA-Seq analyses demonstrated that neutrophil cytosolic factor 1 (NCF1), a key factor in controlling neutrophilic ROS production, was upregulated and correlated with hepatic inflammation and disease progression. To study specifically the mechanisms related to Neuhi in AH patients and liver injury, we used the mouse model of chronic-plus-binge ethanol feeding and found that myeloid-specific deletion of the Ncf1 gene abolished ethanol-induced hepatic inflammation and steatosis. RNA-Seq analysis and the data from experimental models revealed that neutrophilic NCF1-dependent ROS promoted alcoholic hepatitis (AH) by inhibiting AMP-activated protein kinase (a key regulator of lipid metabolism) and microRNA-223 (a key antiinflammatory and antifibrotic microRNA). In conclusion, two distinct histopathological phenotypes based on liver immune phenotyping are observed in SAH patients, suggesting a separate mechanism driving liver injury and/or failure in these patients