Edge Masking Research Articles

The role of masking and aperture size for accurate measurement of performance parameters of DSSCs

DSSC efficiency is often reported with very little or no consideration of the cell area for which they are reported. Area masking of cells is almost always used for the measurement of cell performance parameters (Jsc, Voc, and FF). Although edge masking is required to exclude edge effects, a variety of masking including masking a major portion of the cells are frequently used to boost the apparent efficiency of the cell. In this work, it will be shown that the reported efficiency of a cell is strongly dependent on the exposed area. Two different kinds of cells have been considered in this work to elucidate the area effect: first is a large active area cell whose performance metrics are obtained by masking various fraction of the active area. Second, cells were fabricated with active areas ranging from small to large. A comparison of these two kinds of cells demonstrates that the cells with smaller areas show area-dependent performance parameters. A cell with a 4 mm2 exposed area will show a higher efficiency of 10.47 % while the efficiency will drop to ∼ 3 % as the area increases to 60 mm2 or higher. Such area dependence can be avoided if the cell area/exposed area selected is 60 mm2 or above. The reason for the area dependence has been identified to be due to the presence of trap states and not due to scattering which is sometimes used in the literature to explain the area dependence.

Inclusion Detection in Injection-Molded Parts with the Use of Edge Masking.

The algorithm and prototype presented in the article are part of a quality control system for plastic objects coming from injection-molding machines. Some objects contain a flaw called inclusion, which is usually observed as a local discoloration and disqualifies the object. The objects have complex, irregular geometry with many edges. This makes inclusion detection difficult, because local changes in the image at inclusions are much less significant than grayscale changes at the edges. In order to exclude edges from calculations, the presented method first classifies the object and then matches it with the corresponding mask of edges, which is prepared off-line and stored in the database. Inclusions are detected based on the analysis of local variations in the surface grayscale in the unmasked part of the image under inspection. Experiments were performed on real objects rejected from production by human quality controllers. The proposed approach allows tuning the algorithm to achieve very high sensitivity without false detections at edges. Based on input from the controllers, the algorithm was tuned to detect all the inclusions. At 100% recall, 87% precision was achieved, which is acceptable for industrial applications.

A Spatial-Temporal Video Quality Assessment Method via Comprehensive HVS Simulation.

The quality of videos is the primary concern of video service providers. Built upon deep neural networks, video quality assessment (VQA) has rapidly progressed. Although existing works have introduced the knowledge of the human visual system (HVS) into VQA, there are still some limitations that hinder the full exploitation of HVS, including incomplete modeling with few HVS characteristics and insufficient connection among these characteristics. In this article, we present a novel spatial-temporal VQA method termed HVS-5M, wherein we design five modules to simulate five characteristics of HVS and create a bioinspired connection among these modules in a cooperative manner. Specifically, on the side of the spatial domain, the visual saliency module first extracts a saliency map. Then, the content-dependency and the edge masking modules extract the content and edge features, respectively, which are both weighted by the saliency map to highlight those regions that human beings may be interested in. On the other side of the temporal domain, the motion perception module extracts the dynamic temporal features. Besides, the temporal hysteresis module simulates the memory mechanism of human beings and comprehensively evaluates the video quality according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module toward the proposed method. The source code is available at https://github.com/GZHU-DVL/HVS-5M.

Node and edge dual-masked self-supervised graph representation

Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in these data environments. Currently, masking and reconstruction-based (MR-based) methods lead the state-of-the-art records in the self-supervised graph representation field. However, existing MR-based methods did not fully consider both the deep-level node and structure information which might decrease the final performance of the graph representation. To this end, this paper proposes a node and edge dual-masked self-supervised graph representation model to consider both node and structure information. First, a dual masking model is proposed to perform node masking and edge masking on the original graph at the same time to generate two masking graphs. Second, a graph encoder is designed to encode the two generated masking graphs. Then, two reconstruction decoders are designed to reconstruct the nodes and edges according to the masking graphs. At last, the reconstructed nodes and edges are compared with the original nodes and edges to calculate the loss values without using the labeled information. The proposed method is validated on a total of 14 datasets for graph node classification tasks and graph classification tasks. The experimental results show that the method is effective in self-supervised graph representation. The code is available at: https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask.

Cross-view graph contrastive learning with hypergraph

Graph contrastive learning (GCL) provides a new perspective to alleviate the reliance on labeled data for graph representation learning. Recent efforts on GCL leverage various graph augmentation strategies, i.e., node dropping and edge masking, to create augmented views of the original graph, and then contrast the representations in these augmented views to learn expressive graph embeddings. Nevertheless, the contrasting is still conducted between small variations of the original graph, where limited information can be extracted. In this work, for the first time, we propose to use hypergraph to establish a new view for graph contrastive learning. Specifically, for each graph, we construct its corresponding hypergraph, and then contrast the graph representations learned in the hypergraph view and the original graph view, by which the high-order information of a graph can be captured to produce graph representations of higher quality. Furthermore, to bridge the potential gap between the graph and hypergraph representations, we utilize the diffusion model to exchange the information contained in these two views, enabling better graph contrastive learning. We evaluate our proposal with a collection of experiments, and the empirical results validate that the proposed model can improve node and graph classification performance.

Homogeneous cooling control technology for high-strength low-alloy hot-rolled strip

ABSTRACT Heterogeneities of transverse temperature during laminar cooling of high-strength low-alloy hot-rolled strip can cause heterogeneous distribution of microstructure, mechanical property and internal stress. Edge masking is a control technology that can be used to improve laminar cooling heterogeneities. In this paper, different edge masking schemes were set up based on the transverse transformation characteristics of the strip. The results show that a slow cooling of 7.1 s during transformation could reduce the heterogeneity of grain size, internal stress and microhardness at the edge and middle of the strip. In addition, the relationship model between grain size and six variables such as transformation start temperature was established by PCA. It provided a new idea for the construction of a microstructure prediction model under nonlinear cooling.

Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation

Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous applications and systems, necessitating explanations of their predictions, especially when making critical decisions. However, explaining GNNs is challenging due to the complexity of graph data and model execution. Post-hoc explanation approaches have gained popularity due to their versatility, despite their additional computational costs. Although intrinsically interpretable models can provide instant explanations, they are usually model-specific and can only explain particular GNNs. To address these challenges, we propose a novel, general, and fast GNN explanation framework named SCALE. SCALE trains multiple specialty learners to explain GNNs, as creating a single powerful explainer for examining the attributions of interactions in input graphs is complicated. In training, a black-box GNN model guides learners based on an online knowledge distillation paradigm. During the explanation phase, explanations of predictions are generated by multiple explainers corresponding to trained learners. Edge masking and random walk with restart procedures are used to provide structural explanations for graph-level and node-level predictions, respectively. A feature attribution module provides overall summaries and instance-level feature contributions. We compare SCALE with state-of-the-art baselines through quantitative and qualitative experiments to demonstrate its explanation correctness and execution performance. Furthermore, we conduct a user study and a series of ablation studies to understand the strengths and weaknesses of the proposed framework.

Edge imaging characteristics of aberrated coherent optical systems by edge masking of circular apertures

In this paper, attempts have been made to study the joint effects of apodization and aperture masking on the diffraction images of coherently illuminated straight edge. The Edge-Ringing, Edge-Shift and Edge-Eradient of the edge images have been evaluated for different values of apo-dization using edge masking of circular apertures. We have considered rotationally symmetric, ab-errated coherent optical system. These investigations have lead to the use of certain pupil functions in conjunction with optimal apodizers to assess the quality of edge images. Any obstruction placed in the light path of an optical system prevents waves from a portion of the wavefront in reaching the focal zone. This results in the change in the light flux at every point of the diffraction pattern. This is in turn, depends on the shape and size of the obstruction.

RETRACTED ARTICLE: Effect of cooling process on reducing residual stress of hot rolled high strength strip

ABSTRACT In order to analyse the influence of different laminar cooling technology on residual stress of steel strip, a finite-element model of strip was established with ABAQUS and FORTRAN named basic model. The evolution of temperature, phase transformation and stress of the strip during cooling process were expressed with coupling calculation. Other four finite-element models were established based on the basic model by changing its initial condition and boundary condition. The new models represented four cooling technology named edge masking, sparse cooling, posterior cooling and initial transverse temperature difference controlling. The results show that the stress distribution on strips tends to be high compressive stress on edge and tensile stress on middle section. The effect of the four cooling technologies on the residual stress reduction is ranked as initial transverse temperature difference controlling, sparse cooling, edge masking and posterior cooling.

Advanced image steganography based on exploiting modification direction and neutrosophic set

A new variant of image steganography based on exploiting modification directions (EMD) named advanced EMD (AEMD) is introduced. In this method the secret digits in mn -ary notional systems are embedded into a group of n pixels of the cover image. To increase data hiding capacity, edge masking characteristics of human visual system is exploited to embed more bits at image edge pixels than non-edge pixels. To do this, a neutrosophic set edge detector, named as MNSED is also introduced to classify cover image into 2 × 2 non-overlapping edge and non-edge blocks without any overhead information. Hence the secret digits in two different bases are embedded into the edge and non-edge blocks. Experimental results show that the proposed method provides higher embedding capacity and better quality compared to the existing schemes, while its resistance to steganographic attack is still higher.

Visual Attention Guided Pixel-Wise Just Noticeable Difference Model

The just noticeable difference (JND) models in pixel domain are generally composed of luminance adaptation (LA) and contrast masking (CM), which takes edge masking (EM) and texture masking (TM) into consideration. However, in existing pixel-wise JND models, CM is not evaluated appropriately since they overestimate the masking effect of regular oriented texture regions and neglect the visual attention characteristic of human eyes for the real image. In this work, a novel JND model in pixel domain is proposed, where orderly texture masking (OTM) for regular texture areas (also called orderly texture regions) and disorderly texture masking (DTM) for complex texture areas (also called disorderly texture regions) are presented based on the orientation complexity. Meanwhile, the visual saliency is set as the weighting factor and is incorporated into CM evaluation to enhance JND thresholds. Experimental results indicate that compared with existing relevant JND profiles, the proposed JND model tolerates more distortion in the same perceptual quality, and brings better visual perception in the same level of the injected JND-noise energy.

Reduction of Residual Stress for High‐Strength Low‐Alloy Steel Strip Based on Finite Element Analysis

Intensive cooling technology is widely utilized in the production of high‐strength hot‐rolled steel strip. However, intensive cooling at high cooling rate may cause stress heterogeneity on a steel strip, which further generates great residual stress and influences steel strip shape. In this study, a three‐dimensional finite element (FE) model of high‐strength low‐alloy steel strip on the run‐out table coupled with heat transfer, phase transformation, and strain/stress is developed by ABAQUS software. To enhance modeling precision, several experiments are conducted, such as uniaxial tensile test at multiple temperatures, dynamic continuous cooling transformation, and scanning electron microscopy, to determine the material properties and boundary conditions of the FE model. Four new models are established based on this model to reduce the residual stress of strip by modifying the initial and boundary conditions. Results show that reducing the initial transverse temperature difference is the most effective in reducing residual stress, followed by sparse cooling, edge masking, and posterior cooling.

RAPID BLEEDING REGION DETECTION IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Wireless Capsule Endoscopy (WCC) is a medical imaging technique used to examine parts of the gastrointestinal tract. Computer aided detection is used to increase the speed of detection, better performance and reduce the time. Before finding the bleeding regions the edge regions are first detected and removed. Both the edge and the bleeding regions will share the same Hue value and the luminance should be same for the bleeding and the non -bleeding regions .We use a canny edge detector operator for detecting the edge regions in L channel. Canny edge detector is used to detect more edge pixels and preserve more bleeding pixels based up on canny edge algorithm. This method in edge removal algorithm includes edge detection, edge dilation and edge masking. After the removal of edges, those regions are made in to segment through super-pixel segmentation and regions are classified using Artificial Neural Network by Radial Bias Function (RBF).Â

Effect of Edge Masking on Residual Stress of Hot-Rolled Strip on Run-Out Table

A numerical model including thermal and microstructural model is established for precisely prediction of residual stress of hot-rolled strip on run-out table. Temperature and phase transformation fraction are coupled and formulated for the calculation of thermal and transformation expansion that could induce internal stress. Residual stress for hot-rolled strip before and after coiling are calculated using C++ program. Influence of edge masking on residual stress is analyzed. It is concluded that edge masking makes the strip flatness better after coiling.

Development of Software for Calculating Temperature Field by FEM Simulation to Hot Strip Mill

The temperature field of hot strip during rolling was simulated by FEM. The temperature drop curve and temperature field at any time were solved Furthermore, a temperature field simulation software based on the practical production conditions was developed and applied to simulate the temperature field of a combination of multiple grades of steel, rolling, diversified equipment and cooling steps. In the end, simulation results are analyzed and edge masking controlling in width in LFC is discussed.

Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions

In just noticeable difference (JND) models, evaluation of contrast masking (CM) is a crucial step. More specifically, CM due to edge masking (EM) and texture masking (TM) needs to be distinguished due to the entropy masking property of the human visual system. However, TM is not estimated accurately in the existing JND models since they fail to distinguish TM from EM. In this letter, we propose an enhanced pixel domain JND model with a new algorithm for CM estimation. In our model, total-variation based image decomposition is used to decompose an image into structural image (i.e., cartoon like, piecewise smooth regions with sharp edges) and textural image for estimation of EM and TM, respectively. Compared with the existing models, the proposed one shows its advantages brought by the better EM and TM estimation. It has been also applied to noise shaping and visual distortion gauge, and favorable results are demonstrated by experiments on different images.

Hot strip flatness optimization by means of edge masking in the ROT

The control of temperature distribution across the strip is essential for the hot strip flatness improvement. Hot strips of Arcelor in Aviles may display wavy edge defects that are generated after the finisher and are attributed to residual stresses introduced by the accelerated cooling on the run-out-table. A first approach has established that the thermal drop on the run-out-table has a significant effect on flatness, at the edges in particular. Among several systems that may control temperature at the edges, the edge masking technology has been selected, considering the previous implementation on CSP mills. Wavy edges are considerably removed or even suppressed on the operator side, while they are reduced but not eliminated on the drive side.

Advanced revamp concepts and technologies for hot strip production

For today's investments, quick pay-back is becoming more important than ever before. The market increasingly demands the shortest possible delivery times and correspondingly greater flexibility of production. Minimal shutdown times as well as a fast start-up curve during start-up or restarting of the plant have become an absolute necessity today. A new plant concept is illustrated by the extension project of an existing Steckel mill with three finishing stands in tandem arrangement to increase its annual production. Furthermore, the quality of the products will be clearly enhanced thanks to the modified process and the selected technology. In order to implement all this with the highest possible commercial benefit, a revolutionary revamping concept, which reduces the mill shutdown period to a minimum, is introduced to the market. The so-called “add on products” have been developed to generate the highest possible benefit in existing facilities with limited expense and effort. Highlights include the differential tension looper and the tensiometer looper, the compact cooling system, edge masking as well as the on-line polisher.

Mechanism and Prevention of Edge Over Coating in Continuous Hot-Dip Galvanizing.

In order to clarify the mechanism of edge over coating (EOC) for continuous hot-dip galvanizing, a visualization test of the gas flow on strip and a cold model test to measure the profile of the coating thickness at the strip edge were carried out. Outward deflected gas flow was observed at the strip edge and EOC developed in the absence of gas wiping. With gas wiping, EOC developing below the wiping position is reduced by the impinging pressure of the gas wiping jet, and the film thickness becomes approximately uniform at the gas wiping position. However, upward of the gas wiping position, EOC increases again and the outward deflected gas flow on the strip edge sweeps the liquid film to the strip edge. EOC is considered to develop at the location where the dynamic pressure of the outward deflected gas flow balances with the surface tension.For the prevention of EOC, edge masking was devised and the effects which reduce EOC were measured in the cold model test and on a commercial line test. The edge mask which can be kept farther away from the strip edge is more effective for preventing EOC than the edge plates. The optimum dimension of the edge mask is 30 mm in width and 75–100 mm in depth, and installing it at 4–10 mm away from the strip edge is most effective. It was confirmed by the commercial line test that the edge mask can reduce EOC from 45% to less than 10%.

Interlaboratory Comparison of the ASTM Standard Test Methods for Water Vapor Transmission of Materials (E 96-95)

AbstractAn interlaboratory comparison was initiated by the ASTM C16 Committee on Thermal Insulation to determine the precision of the test method prescribed by ASTM Standard Test Methods for Water Vapor Transmission of Materials (E 96-95). Expanded polystyrene board was chosen as the test material. Nine laboratories completed the measurements according to the dry cup and the wet cup procedures prescribed by the standard. Statistical analyses of the data resulted in the following precision statement for the water vapor permeability:Dry cup method:Permeability of material = 3.1E-12 kg m−1 s−1 Pa−1Repeatability standard deviation (within a laboratory) = 1.6E-1.3 kg m−1 s−1 Pa−1Reproducibility standard deviation (between laboratories) = 4.7E-13 kg m−1 s−1 Pa−1Wet cup method:Permeability of material = 3.4E-12 kg m−1 s−1 Pa−1Repeatability standard deviation (within a laboratory) = 1.7E-13 kg m−1 s−1 Pa−1Reproducibility standard deviation (between laboratories) = 4.1E-13 kg m−1 s−1 Pa−1A specific procedure was uniformly used to analyze the data and to calculate the permeability of the test material from the raw data provided by the participating laboratories. Corrections were made for the resistances offered by the still air layer inside the cup and by the specimen surfaces and for the edge masking details.