Refinement Algorithm Research Articles

GDUI: Guided Diffusion Model for Unlabeled Images

The diffusion model has made progress in the field of image synthesis, especially in the area of conditional image synthesis. However, this improvement is highly dependent on large annotated datasets. To tackle this challenge, we present the Guided Diffusion model for Unlabeled Images (GDUI) framework in this article. It utilizes the inherent feature similarity and semantic differences in the data, as well as the downstream transferability of Contrastive Language-Image Pretraining (CLIP), to guide the diffusion model in generating high-quality images. We design two semantic-aware algorithms, namely, the pseudo-label-matching algorithm and label-matching refinement algorithm, to match the clustering results with the true semantic information and provide more accurate guidance for the diffusion model. First, GDUI encodes the image into a semantically meaningful latent vector through clustering. Then, pseudo-label matching is used to complete the matching of the true semantic information of the image. Finally, the label-matching refinement algorithm is used to adjust the irrelevant semantic information in the data, thereby improving the quality of the guided diffusion model image generation. Our experiments on labeled datasets show that GDUI outperforms diffusion models without any guidance and significantly reduces the gap between it and models guided by ground-truth labels.

Research of ESPI Stripe Skeleton Line Extraction Based on Improved Fast Parallel Algorithm

Stripe skeleton line method is one of the commonly used methods to extract the phase information of ESPI stripe maps, and the accuracy of the skeleton line extraction determines the accuracy of the stripe map phase information. In this paper, a fast parallel refinement algorithm for ESPI streak image is proposed for the commonly used ZS and OPTA fast parallel refinement algorithms, which improves the deletion template and retention template. It is experimentally verified that the algorithm can effectively reduce the burr bifurcation and fracture phenomenon of the refined image in ESPI skeleton line extraction, which has good practical value.

Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms.

Corrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze five-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring the layer and overall board thickness, flute height, and center lines of each layer. Through the integration of image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method only faced limitations with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement in order to enhance robustness and accuracy.

An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling

Objective. In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems. Approach. We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method’s accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a ‘silver-standard’ solution found by subsequent 4:1 global refinement of the adaptively-refined model. Main results. Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models. Significance. This error has especially important implications for TES dosing prediction—where the stimulation strength plays a central role—and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Transforming Academic Research: A Business Model for Chemical-Computational Laboratory

The Business Model Canvas (BMC) is a transformative tool in business strategy, streamlining the creation of dynamic models for organizational success. In the evolving field of academic research, computational chemistry undergoes significant changes due to increased computational capabilities and algorithm refinement. Despite these advancements, the challenge remains in effectively translating computational insights into commercial outcomes. This study addresses the task of formulating a business model for a computational simulation research lab and integrating a strategic Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Also, to identify predominant trends within computational chemistry research articles through literature review and keyword frequency analysis. A nine-block business model for a computational chemistry research lab was crafted using the BMC template. SWOT analysis was applied to each block of the business model. Trends in computational chemistry appear to be in drug discovery, following material design. Business model designing, meticulous market analysis like literature review trends, and strategic planning doing SWOT analysis are essential pillars supporting the successful execution of research projects.

A cut-cell method for the numerical simulation of 3D multiphase flows with strong interfacial effects

We present a numerical model for the approximation of multiphase flows with free surfaces and strong interfacial effects. The model relies on the multiphase incompressible Navier-Stokes equations, and includes surface tension effects on the interfaces between phases, and contact angles. The volume-of-fluid approach is used to track the interfaces and the free surfaces between the various phases and the ambient air. The numerical method relies on an operator splitting strategy. The space discretization relies on a two-grid approach that uses an unstructured finite element mesh for diffusion phenomena and a structured Cartesian grid for advection phenomena. An adaptive mesh refinement algorithm is incorporated to better track the interfaces and free surfaces on the finite element mesh, and approximate more accurately surface forces. The model is validated through numerical experiments, in particular for emulsion problems.

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretization of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

Energy-dependent self-adaptive mesh refinement algorithms are developed for a continuous Bubnov-Galerkin spatial discretization of the multi-group neutron diffusion equation using NURBS-based isogeometric analysis (IGA). The spatially self-adaptive algorithms employ both mesh (h) and polynomial degree (p) refinement. Constraint-based equations are established across irregular interfaces with hanging-nodes; they are based upon master-slave relationships and the conservative interpolation between surface meshes. A similar Galerkin projection is employed in the conservative interpolation between volume meshes to evaluate group-to-group source terms over energy-dependent meshes; and to evaluate interpolation-based error measures. Enforcing continuity over an irregular mesh does introduce discretization errors. However, local mesh refinement allows for a better allocation of computational resources; and thus, more accuracy per degree of freedom. Two a posteriori interpolation-based error measures are proposed. The first heuristically minimizes local contributions to the discretization error, which becomes competitive for global quantities of interest (QoIs). However, for localized QoIs, over energy-dependent meshes, certain multi-group components may become under-resolved. The second employs duality arguments to minimize important error contributions, which consistently and reliably reduces the error in the QoI.

Mixed Integer Programming based Placement Refinement by RSMT Model with Movable Pins

Placement is a critical step in the physical design for digital application specific integrated circuits (ASICs), as it can directly affect the design qualities such as wirelength and timing. For many domain specific designs, the demands for high performance parallel computing result in repetitive hardware instances, such as the processing elements in the neural network accelerators. As these instances can dominate the area of the designs, the runtime of the complete design’s placement can be traded for optimizing and reusing one instance’s placement to achieve higher quality. Therefore, this work proposes a mixed integer programming (MIP)-based placement refinement algorithm for the repetitive instances. By efficiently modeling the rectilinear steiner tree wirelength, the placement can be precisely refined for better quality. Besides, the MIP formulations for timing-driven placement are proposed. A theoretical proof is then provided to show the correctness of the proposed wirelength model. For the instances in various popular fields, the experiments show that given the placement from the commercial placers, the proposed algorithm can perform further placement refinement to reduce 3.76%/3.64% detailed routing wirelength and 1.68%/2.42% critical path delay under wirelength/timing-driven mode, respectively, and also outperforms the state-of-the-art previous work.

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

A flow control strategy for a near-wall square cylinder using porous media: A direct numerical simulation study

The topic of flows around a near-wall square cylinder has garnered increasing attention in recent decades. However, there are a few publications that have focused on mitigating the occurrence of a substantial negative lift in near-wall flows. In light of this, the present study has developed a novel flow control strategy that covers porous media at inward corners of a near-wall square cylinder to address this problem. We achieve such a control strategy with the aid of a high-fidelity computational framework at Re = 1000. Direct numerical simulations are employed to account for accurate flow behaviors, and the Cartesian cut-cell method as well as an adaptive mesh refinement algorithm are advocated to simplify grid generation and reduce computational costs. Additionally, a quasi-microscopic flow model is introduced to model the porous medium pore structure, providing an intuitive and accurate description of internal flows within the porous medium. Six porous medium layouts are first designed, and their influences and mechanisms on flow control are assessed using the presented computational framework to identify an optimal strategy. The optimal strategy yields a notable reduction of 52.472% in the lift coefficient. The identified strategy is then applied to a case involving a near-wall square cylinder with a substantial negative lift, where a gap ratio of 0.6 is determined via parameterization. The capacity of the presented strategy in flow control of the near-wall square cylinder is fully explored and demonstrated via the consideration of different porosities. The results indicate that the most effective flow control is achieved when the porosity exceeds 90%, leading to a near-zero lift coefficient. Furthermore, the underlying mechanism contributing to the variations in flow control effectiveness due to different porosities is analyzed.

The Application of the “Banchenko's Mnemonic Dream Synchronization Method” for the Joint Synchronization of Dream Elements (Including Lucid Dreams), within the Framework of “Blokhin's Dreaming Cell Concept,” and the Analysis of the Obtained Results Using the “Kapustin's AI Dream Matching Model.”

Purpose: The research is aimed at studying the potential for dream synchronization, including lucid dreams, among a group of participants using the concepts of "memes" and "tag-memes" within the framework of the "Banchenko Algorithm" and the "Mnemonic Dream Synchronization Technique of Banchenko." Additionally, it aims to assess the effectiveness of a new artificial intelligence model built and trained by Mykhailo Kapustin for identifying correlations in the dreams of the participants [1]. Methods: In the study, eight participants from different countries kept dream journals for a month. Afterward, the artificial intelligence model developed by Kapustin was trained to analyze these dreams. Following this, over the course of 16 days, synchronization among the group of participants was conducted using the "Mnemonic Dream Synchronization Technique of Banchenko," which included activities like collectively watching the TV series "The OA. Synchronization took into account the difference in time zones. Additionally, synchronous discussions of the viewing were carried out on the Telegram messenger, along with additional discussions related to key concepts and "tag-memes." Data analysis covered 30 days before synchronization and 30 days after the start of synchronization, using Kapustin's model with subsequent manual verification [2].Results: The data obtained for two periods confirm the influence of synchronization during wakefulness on dreams and highlight the emergence of "memes" and "tag-memes" in dream content, as well as objects and scenes unrelated to the elements of discussion or collective viewings. In-depth discussions during wakefulness contribute to the development of unique "tag-memes" within the group. The novelty of the research is based on the fact that it's suggested that electromagnetic fields may serve as mediators in intercellular communication, facilitating the exchange of information between cells. The systematic keeping of dream journals may influence the content of dreams. The frequency of lucid dreams remained stable, which may indicate the limited effectiveness of isolated methods of joint tuning without additional methods for inducing lucid dreams. Conclusions: The research highlights the impact of wakeful-state synchronization on dream content. "Memes" and "tag-memes" play their roles, but systematic dream journaling is also important for improving dream recall. The frequency of lucid dreams suggests the need for further investigation into isolated practices. A computational model excels at identifying common dream objects, but human evaluation remains integral during algorithm refinement.

An errors-unidentified hand-eye calibration method via robot relocalization and iterative combinatorial refinement

The accuracy of hand-eye calibration is easily affected by robot kinematic error, measurement error, random error, etc., and can be to a certain extent enhanced by the complicated error identification. To address this problem, we propose a novel errors-unidentified hand-eye calibration method. A relocalization-based hand-eye calibration is implemented to overcome the challenge from robot kinematics at first owing to the high robot relocalization accuracy. The tool center point (TCP) coordinates are then obtained based on an iterative reweighted least squares (IRLS) spherical fitting algorithm. An iterative combinatorial refinement algorithm is finally presented to search the solutions in the form of permutation and combination when solving the rigid transformation matrix. Experiments on criterion sphere and blade demonstrate that by converting the hand-eye matrix solving problem into a point set matching problem, the accuracy of the proposed method is enhanced by 60% on average compared with three state-of-the-art hand-eye calibration methods.

3D Simulations of Nucleate Boiling with Sharp Interface Vof and Localized Adaptive Mesh Refinement in Ansys-Fluent

Abstract The present work demonstrates the use of customized Ansys-Fluent in performing 3D numerical simulations of nucleate boiling with a sharp interface and adaptive mesh refinement. The developed simulation approach is a reliable and effective tool to investigate 3D boiling phenomena by accurately capturing the thermal and fluid dynamic interfacial vapor-liquid interaction and reducing computational time. These methods account for 3D sharp interface and thermal conditions of saturation temperature refining the mesh around the bubble edge. User-Defined-Functions (UDFs) were developed to customize the software Ansys-Fluent to preserve the interface sharpness, maintain saturation temperature conditions, and perform effective adaptive mesh refinement in a localized region around the interface. Adaptive mesh refinement is accomplished by a UDF that identifies the cells near the contact line and the liquid-vapor interface and applies the adaptive mesh refinement algorithms only at the identified cells. Validating the approach considered spherical bubble growth with an observed acceptable difference between theoretical and simulation bubble growth rates of 10%. Bubble growth simulations with water reveal an influence region of 2.7 times the departure bubble diameter, and average heat transfer coefficient of 15000 W/m2-K. In addition, the results indicate a reduced computational time of 75 hours using adaptive mesh compared to uniform mesh.

A PWDG method for the Maxwell system in anisotropic media with piecewise constant coefficient matrix

In this paper we are concerned with plane wave discontinuous Galerkin (PWDG) methods for time-harmonic Maxwell equations in three-dimensional anisotropic media, for which the coefficients of the equations are piecewise constant symmetric matrices, where each constant symmetric matrix is defined on a medium (subdomain). By using suitable scaling transformations and coordinate (complex) transformations on every subdomain, the original Maxwell equation in anisotropic media is transformed into a Maxwell equation in isotropic media occupying a union domain of specific subdomains of complex Euclidean space. Based on these transformations, we define anisotropic plane wave basis functions and discretize the considered Maxwell equations by PWDG method with the proposed plane wave basis functions. We derive error estimates of the resulting approximate solutions, and further introduce a practically feasible local hp-refinement algorithm, which substantially improves accuracies of the approximate solutions. Numerical results indicate that the approximate solutions generated by the proposed PWDG methods possess high accuracy for the case of strong discontinuity media.

Multi-task Adaptive Resolution Network for Lymph Node Metastasis Diagnosis from Whole Slide Images of Colorectal Cancer.

Automated detection of lymph node metastasis (LNM) holds great potential to alleviate the workload of doctors and reduce misinterpretations. Despite the practical successes achieved, effectively addressing the highly complex and heterogeneous tumor microenvironment remains an open and challenging problem, especially when tumor subtypes intermingle and are difficult to delineate. In this paper, we propose a multi-task adaptive resolution network, named MAR-Net, for LNM detection and subtyping in complex mixed-type cancers. Specifically, we construct a resolution-aware module to mine heterogeneous diagnostic information, which exploits the multi-scale pyramid information and adaptively combines multi-resolution structured features for comprehensive representation. Additionally, we adopt a multi-task learning approach that simultaneously addresses LNM detection and subtyping, reducing model instability during optimization and improving performance across both tasks. More importantly, to rectify the potential misclassification of tumor subtypes, we elaborately design a hierarchical subtying refinement (HSR) algorithm that leverages a generic segmentation model informed by pathologists' prior knowledge. Evaluations have been conducted on three private and one public cancer datasets (554 WSIs, 4.8 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods, achieving 0.5% to 3.2% higher AUC in LNM detection and 3.8% to 4.4% higher AUC in LNM subtyping.

Automated Reconstruction and Conforming Mesh Generation for Polycrystalline Microstructures from Imaging Data

A new algorithm named PolyCISAMR is introduced to automatically generate high-fidelity conforming finite element (FE) meshes for two-dimensional polycrystalline microstructures. PolyCISAMR extends the capabilities of the Conforming to Interface Structured Adaptive Mesh Refinement (CISAMR) algorithm, which transforms a structured grid overlaid on the domain geometry into a high-quality conforming mesh. The PolyCISAMR approach uses a segregated meshing strategy, where CISAMR is used to discretize each grain independently and the resulting matching meshes are merged to form the final FE model. In addition, this article presents a set of integrated algorithms for processing low-resolution images of a polycrystal, reconstructed using DREAM.3D software (Version 6.5.121), to generate NURBS characterizations for each grain prior to mesh generation. Example problems demonstrate the effectiveness of PolyCISAMR in creating high-quality meshes for various polycrystalline metallic microstructures along with corresponding crystal plasticity finite element (CPFE) simulations.

An Effective Routing Refinement Algorithm Based on Incremental Replacement and Rerouting

An Effective Routing Refinement Algorithm Based on Incremental Replacement and Rerouting

An Efficient Goal-Oriented Adaptive Finite Element Method for Accurate Simulation of Complex Electromagnetic Radiation Problems

A goal-oriented adaptive frequency domain finite element method for solving electromagnetic radiation problems including complex structures is presented in this paper. Compared with the traditional adaptive finite element method, the goal-oriented method can flexibly control the refined regions according to the parameters of interest; therefore it has better convergence and has made significant progress in scattering problems and eigenvalue problems. To simulate complex antennas, this paper proposes an error indicator with high accuracy and low computational cost, and it uses the adjoint problem to weight element residuals without additional degrees of freedom. Moreover, high-quality mesh refinement algorithms adapted to this indicator are developed using a suitable point insertion strategy for multiscale structures. By simulating two practical antennas, comparisons with the traditional goal-oriented FEM and the well-developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</i> -adaptive finite element method in commercial software demonstrate the accuracy and efficiency of the proposed method.

Takes Two to Tango: Digital Twins and AI Revolutionize Sports Science and Medicine

Purpose: Technological advancements are transforming the field of sports science and medicine, leading to a new era of performance improvement and injury prevention. Digital twins and artificial intelligence (AI) are at the forefront of these innovations, working together to redefine athletic training and monitoring. This editorial offers a comprehensive overview of the integration of digital twins and AI in sports science, with a focus on their potential applications, challenges, and future developments. By utilizing sensor data, AI algorithms, and biometrics, digital twins create virtual replicas of athletes, enabling precise performance monitoring and personalized training programs. Conclusions: Large datasets generated by AI can be used to predict and prevent injuries, as well as to enhance communication among stakeholders. Despite the promises, challenges such as privacy concerns and data accuracy need to be addressed. Future advancements will concentrate on sensor accuracy, AI algorithm refinement, and broader applications. The editorial highlights exciting research opportunities, including predictive injury models, real-time performance monitoring, and longitudinal health studies. Ultimately, the collaboration between digital twins and AI represents a paradigm shift in sports science, with the potential to revolutionize athlete well-being and performance optimization.

The software for improvement of image quality after enlargement

In the paper digital images of various formats were investigated. The different vector image formats have different color rendering capabilities. The main task was to achieve a result of refinement of the random low resolution color raster image without quality and resolution loss. The biggest advantage of using specific vector or compressed raster formats is the ability of scaling without quality loss and comparatively small file size. This eases vector images transfer through networks. In the article a specific algorithm of raster images refinement was investigated, particularly the method of raster images refinement based on combination of interpolation algorithms with and without square root of the color values. The key point of the method is comparison and combination of vertical, horizontal and diagonal interpolation that allows to achieve better precision on color depth calculation. This exact method was never used in commercial of scientific software though there are different variation of combined interpolation methods similar to current one. In this paper two different approaches to image matrix re-calculation during image refinement were tested, in order to research how root squaring the value of color depth would affect the target color value. The result shows that this approach allows to keep more details in shadows and save contours during interpolation though the images lose somewhat of color depth. The experiment shows that this interpolation method with square rooting color values allows to enlarge and refine color images with complex tone curve structure and keep details of the objects in place, though color depth is worsened especially in deepest shades and blacks. On the opposite the method of combined interpolation without root squaring gives significantly better result with color interpolation but loses details in the dark areas of the initial image. The suggested method can be used in a number of different areas