Traditional Operators Research Articles

Bi-Constraints Diffusion: A Conditional Diffusion Model with Degradation Guidance for Metal Artifact Reduction.

In recent years, score-based diffusion models have emerged as effective tools for estimating score functions from empirical data distributions, particularly in integrating implicit priors with inverse problems like CT reconstruction. However, score-based diffusion models are rarely explored in challenging tasks such as metal artifact reduction (MAR). In this paper, we introduce the BiConstraints Diffusion Model for Metal Artifact Reduction (BCDMAR), an innovative approach that enhances iterative reconstruction with a conditional diffusion model for MAR. This method employs a metal artifact degradation operator in place of the traditional metal-excluded projection operator in the data-fidelity term, thereby preserving structure details around metal regions. However, scorebased diffusion models tend to be susceptible to grayscale shifts and unreliable structures, making it challenging to reach an optimal solution. To address this, we utilize a precorrected image as a prior constraint, guiding the generation of the score-based diffusion model. By iteratively applying the score-based diffusion model and the data-fidelity step in each sampling iteration, BCDMAR effectively maintains reliable tissue representation around metal regions and produces highly consistent structures in non-metal regions. Through extensive experiments focused on metal artifact reduction tasks, BCDMAR demonstrates superior performance over other state-of-the-art unsupervised and supervised methods, both quantitatively and in terms of visual results.

A fractional dual-phase-lag thermoelastic model for a solid half-space with changing thermophysical properties involving two-temperature and non-singular kernels

<abstract> <p>The thermal and mechanical properties of materials show differences depending on the temperature change, which necessitates consideration of the dependence of the properties of these materials on this change in the analysis of thermal stress and deformation of the material. As a result, in the present work, a mathematical framework for thermal conductivity was formulated to describe the behavior of non-simple elastic materials whose properties depend on temperature changes. This derived model includes generalized fractional differential operators with non-singular kernels and two-stage delay operators. The fractional derivative operators under consideration include both the Caputo-Fabrizio fractional derivative and the Atangana-Baleanu fractional derivative, in addition to the traditional fractional operator. Not only that, but the system of governing equations includes the concept of two temperatures. Based on the proposed model, the thermodynamic response of an unlimited, constrained thermoelastic medium subjected to laser pulses was considered. It was taken into account that the thermal elastic properties of the medium, such as the conductivity coefficient and specific heat, depend on the temperature. The governing equations of the problem were formulated and then solved using the Laplace transform method, followed by the numerical inverse. By presenting the numerical results in graphical form, a detailed analysis and discussion of the effects of fractional factors and the dependence of properties on temperature are presented. The results indicate that the fractional order coefficient, discrepancy index, and temperature-dependent properties significantly affect the behavior fluctuations of all physical domains under consideration.</p> </abstract>

Program Pelatihan Operasional Kapal Speedboat Fiberglass Untuk Operator Kapal Tradisional Sampang-Gili Mandangin Dalam Rangka Mendukung Perkembangan Kapal Berbahan Fiberglass Di Pulau Madura

Mandangin Island is one of the islands in Sampang Regency which has an area of 1650 km 2 with a population of around 3000 people. Most of the people on this island, the majority of whom work as fishermen, generally travel around 12.2 km to Sampang City to buy daily necessities by boarding traditional wooden boats at the Tanglok Sampang port. According to information conveyed by the Head of the Sampang Regency Transportation Service, most or almost all of the passenger ships that dock at Tanglok Port are traditional wooden ships whose actual function is to transport goods, not passengers, which are quite old. The Transportation Department considers the existing ships to be less safe and comfortable and therefore require refreshing. Besides that, wood as the main material in nature is decreasing in quantity so the price is increasingly expensive. The method implemented in this community service is to carry out a fiber glass boat operational training program for traditional boat operators. With this activity, it is hoped that the continued use of fiber glass boats can begin to be used and reduce the use of wooden boats which are relatively more expensive.

Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903g/cm3. The proposed method can improve MMD performance and has potential applications.

Walk in views: Multi-view path aggregation graph network for 3D shape analysis

The graph-based multi-view methods have achieved state-of-the-art results in 3D shape analysis tasks by taking advantage of graph convolutional networks (GCN) to process discrete data. However, the homogeneity of the traditional GCN aggregation operator leads to a problem in aggregating neighborhood information, i.e., if several views have the same neighbors, the same node embeddings will be generated, resulting in feature redundancy. To address this problem, we propose a Multi-view Path Aggregation Graph Network (MVPNet) for 3D shape analysis, which aims to extract a particular path from a graph composed of multiple views and aggregate it into an effective 3D shape descriptor. Specifically, we first extract a path in the graph through dynamic walking, and update the path status while searching for new nodes during the walking. Then we embed the position information of the nodes in the order of the nodes in the path. Finally, we propose to aggregate the features of a path employing a Path Transformer that is capable of handling ordered sequences. A path contains richer semantic and structural information than a traditional subgraph. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on three benchmark datasets, namely ModelNet, ShapeNetCore55 and MCB, and these experiments prove that the method outperforms the current methods in 3D shape classification and retrieval tasks.

Evaluation of Molten Steel Flow Behaviors in a Multistrand Tundish via Numerical Simulation and Grey Relational Analysis with a New Dimensionless Operator

Optimizing the tundish structure can effectively improve billet or solid product quality. The correlation between the structural parameters of the baffle and the flow characteristics of molten steel in a tundish is a prerequisite for tundish structural optimization. In this study, molten steel flow behaviors are stimulated in a five‐strand tundish using computational fluid dynamics. The behaviors are characterized by the dead‐zone ratio of the entire tundish and the flow consistency of five strands at the outlets. Further, the influences of different structural parameters on the characteristics are analyzed, and the degree of influence is evaluated using grey relational analysis (GRA). Four traditional nondimensionalization operators are used for GRA. However, the results obtained from different operators are inconsistent. Therefore, a benchmark deviation dimensionless operator is proposed to improve the accuracy of GRA. The improved grey correlation degrees are consistent with the results obtained through numerical simulation. The rank of the grey correlation degree indicates that the diameter and inclination angle of Orifice B, which is at the lower position of the baffle, are highly correlated with the dead‐zone ratio. The inclination angles should have a larger correlation degree for flow consistency than the orifice diameter.

Deep learning-based prediction of the remaining time and future distribution of pebble flow from real-scene images

Pebble flow dynamics is a crucial issue for designing and operating pebble bed reactors. The existing experimental or simulation methods are often associated with high time, resource, and effort costs. Therefore, image-based deep learning methods are explored to make real-time predictions of pebble flow dynamics directly from images. Real-scene images, captured by the high-speed camera during pebble flow experiments are used as the dataset. This paper proposes an RT-Net model based on Convolutional Neural Network (CNN) to predict the remaining time of pebble flow from experimental images. The core of the RT-Net model is the mergeable multi-branch convolutional component called ConvBlock, which effectively improves accuracy and reduces computational costs compared to traditional convolutional operators. Results show that this model is superior in both accuracy and efficiency metrics compared with typical convolutional neural networks (like AlexNet and VGGs). The proportion of test sets with prediction error within 0.05 s reaches 96.7 %, and the parameters count and inference time are 5.02 M and 1.99 ms respectively. Furthermore, to anticipate the pebble distribution at a given future time, a dual-input PreNet that combines CNN and Generative Adversarial Network (GAN) is designed, where the input includes the current pebble flow image and an arbitrarily chosen temporal displacement Δt. Targeted evaluation metrics, such as Target Similarity (TS) and Equivalent Target Similarity (TSeq) are proposed to quantitatively evaluate the model’s performance. Results indicate that the Pre-Net model can make quite satisfactory predictions of the future distribution of most data, while more efforts such as a task-specific loss function are encouraged to achieve better performance.

Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation

To address the complexity of small or unique reconstruction distances in digital holography, we propose an inverse cross-correlation-based algorithm for the digital holographic imaging of multiplanar objects with a large depth of field. In this method, a planar output mapping is closely around the objects, and it is established by calculating the image inverse cross-correlation matrix of the reconstructed image at similar reconstruction distances, whereby the object edges serve as the result guide. Combining the search for edge planes with the depth estimation operator, the depth of field of digital holography is improved, thus allowing for a digital holography that is capable of meeting the requirements of the holographic imaging of multiplanar objects. Compared with the traditional depth estimation operator method, the proposed method solves the reconstruction ambiguity problem in multiple planes with a simple optical path, and no additional optical or mechanical devices need to be added, thus greatly improving the reconstruction quality. The numerical calculation results and the experimental results with multiplanar samples validate the effectiveness of the proposed method.

A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter

Fabric inspection is critical in fabric manufacturing. Automatic detection of fabric defects in the textile industry has always been an important research field. Previously, manual visual inspection was commonly used; however, there were drawbacks such as high labor costs, slow detection speed, and high error rates. Recently, many defect detection methods based on deep learning have been proposed. However, problems need to be solved in the existing methods, such as detection accuracy and interference of complex background textures. In this paper, we propose an efficient segmentation algorithm that combines traditional operators with deep learning networks to alleviate the existing problems. Specifically, we introduce a Gabor filter into the model, which provides the unique advantage of extracting low-level texture features to solve the problem of texture interference and enable the algorithm to converge quickly in the early stages of training. Furthermore, we design a U-shaped architecture that is not completely symmetrical, making model training easier. Meanwhile, multi-stage result fusion is proposed for precise location of defects. The design of this framework significantly improves the detection accuracy and effectively breaks through the limitations of transformer-based models. Experimental results show that on a dataset with one class, a small amount of data, and complex sample background texture, our method achieved 90.03% and 33.70% in ACC and IoU, respectively, which is almost 10% higher than other previous state of the art models. Experimental results based on three different fabric datasets consistently show that the proposed model has excellent performance and great application potential in the industrial field.

A shallow physics-informed neural network for solving partial differential equations on static and evolving surfaces

In this paper, we introduce a shallow (one-hidden-layer) physics-informed neural network (PINN) for solving partial differential equations on static and evolving surfaces. For the static surface case, with the aid of a level set function, the surface normal and mean curvature used in the surface differential expressions can be computed easily. So, instead of imposing the normal extension constraints used in literature, we write the surface differential operators in the form of traditional Cartesian differential operators and use them in the loss function directly. We demonstrate a series of performance study for the present methodology by solving Laplace–Beltrami equations and surface diffusion equations on complex static surfaces. With just a moderate number of neurons used in the hidden layer, we are able to attain satisfactory prediction results. We then extend the present methodology to solve the advection–diffusion equation on an evolving surface with a given velocity. To track the deforming surface, we additionally introduce a network, in which a prescribed hidden layer is employed to enforce the topological structure of the surface and learn the homeomorphism between the surface and the prescribed topology. The proposed network structure is designed to track the surface and solve the equation simultaneously. Again, the numerical results show comparable accuracy as the static cases. As an application, we simulate surfactant transportation on a droplet surface under shear flow and obtain some physically plausible results.

A Sparse Local Binary Pattern extraction algorithm applied to event sensor data for object classification

Recently, new sensors with active pixels were brought to market. These sensors export local variations of light intensity in the form of asynchronous events with low latency. Since the data output format is a stream of addressable events and not a complete image of light intensities, new algorithms are required for known problems in the field of Computer Vision, such as segmentation, VO, SLAM, object, and scene recognition. There are some proposed methodologies for object recognition using conventional methods, convolutional neural networks, and third-generation neural networks based on spikes. However, convolutional neural networks and spike neural networks require specific hardware for processing, hard to miniaturize. Also, several traditional Computer Vision operators and feature descriptors have been neglected in the context of event sensors and could contribute to lighter methodologies in object recognition. This paper proposes an algorithm for local binary pattern extraction in sparse structures, typically found in this context. This paper also proposes two methodologies using local binary patterns to captures with event-based sensors for object recognition. The first methodology exploits the known motion performed by the sensor, while the second is motion agnostic. It is demonstrated experimentally that the LBP operator is a fast and light alternative that enables variable reduction using PCA in some cases. The experiments also show that it is possible to reduce the final feature vector for classification by up to 99,73% when compared to conventional methods considered state-of-the-art while maintaining comparable accuracy.

Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.

Simulator Feature Framework: Requirements to Support Training, Research, and Education

There is an expansion of nuclear power plant control room simulator use beyond traditional operator training. Modern simulator designs require more robust capabilities to serve the diverse needs of multiple user groups including researchers and educators. A common framework for evaluating simulator features to support training, research, and education is critical to ensure future simulators enable research for immediate and future plant modernization and advanced reactor deployment needs. An initial framework comprised of 8 feature categories was developed by reviewing published simulator-based research and analyzing simulator features against research objectives and results. Future research will focus on evaluating the suitability of this framework in characterizing and differentiating simulators across training, research, and education use cases.

Efficient Numerical Simulation of Biochemotaxis Phenomena in Fluid Environments.

A novel dimension splitting method is proposed for the efficient numerical simulation of a biochemotaxis model, which is a coupled system of chemotaxis-fluid equations and incompressible Navier-Stokes equations. A second-order pressure correction method is employed to decouple the velocity and pressure for the Navier-Stokes equations. Then, the alternating direction implicit scheme is used to solve the velocity equation, and the operator with dimension splitting effect is used instead of the traditional elliptic operator to solve the pressure equation. For the chemotactic equation, the operator splitting method and extrapolation technique are used to solve oxygen and cell density to achieve second-order time accuracy. The proposed dimension splitting method splits the two-dimensional problem into a one-dimensional problem by splitting the spatial derivative, which reduces the computation and storage costs. Finally, through interesting experiments, we show the evolution of the cell plume shape during the descent process. The effect of changing specific parameters on the velocity and plume shape during the descent process is also studied.

Complex-valued neural-operator-assisted soliton identification.

The numerical determination of solitary states is an important topic for such research areas as Bose-Einstein condensates, nonlinear optics, plasma physics, and so on. In this paper, we propose a data-driven approach for identifying solitons based on dynamical solutions of real-time differential equations. Our approach combines a machine-learning architecture called the complex-valued neural operator (CNO) with an energy-restricted gradient optimization. The CNO serves as a generalization of the traditional neural operator to the complex domain, and constructs a smooth mapping between the initial and final states; the energy-restricted optimization facilitates the search for solitons by constraining the energy space. We concretely demonstrate this approach on the quasi-one-dimensional Bose-Einstein condensate with homogeneous and inhomogeneous nonlinearities. Our work offers an idea for data-driven effective modeling and studies of solitary waves in nonlinear physical systems.

Edge Detection via Fusion Difference Convolution.

Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning.

Manifold Interpolation for Large-Scale Multiobjective Optimization via Generative Adversarial Networks.

Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on the manifold, thereby improving the optimization performance of evolutionary algorithms. We compare the proposed approach with several state-of-the-art algorithms on various large-scale multiobjective benchmark functions. The experimental results demonstrate that significant improvements have been achieved by the proposed framework in solving LSMOPs.

Boundary-optimized summation-by-parts operators for finite difference approximations of second derivatives with variable coefficients

Boundary-optimized summation-by-parts (SBP) finite difference operators for second derivatives with variable coefficients are presented. The operators achieve increased accuracy by utilizing non-equispaced grid points close to the boundaries of the grid. Using the optimized operators we formulate SBP schemes for the acoustic and elastic operators defined directly on curvilinear multiblock domains. Numerical studies of the acoustic and elastic wave equations demonstrate that, compared to traditional SBP difference operators, the new operators provide increased accuracy for surface waves as well as block interfaces in multiblock grids. For instance, simulations of Rayleigh waves demonstrate that the boundary-optimized operators more than halve the runtime required for a given error tolerance.

Picture fuzzy Choquet integral based Einstein operations and its application in selection of the best mobile apps for online education

Picture fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to attributes into one unique output. The Choquet integral operator has been proven more ideal than traditional aggregation operators in the modelling of interaction phenomena among the attributes in decision-making problems. Firstly, we propose the Choquet integral picture fuzzy Einstein geometric aggregation operator and Choquet integral picture fuzzy Einstein ordered geometric aggregation operator with certain properties of these operators being established. We validate the functioning of the operators with illustrative examples. The proposed operators clearly capture the comprehensive correlative relationships of attributes in a simpler manner. Furthermore, the algorithm for a multi attribute decision-making problem based on proposed operators is given. The application of the proposed operators was explored to deal with the selection of the best mobile apps for online education. Finally, comparisons are conducted to illustrate the discussion and advantages of the proposed operators.

The generalized nonlocal fractal calculus: an efficient tool for fractal circuit analysis

PurposeThe purpose of this paper is to propose a novel nonlocal fractal calculus scheme dedicated to the analysis of fractal electrical circuit, namely, the generalized nonlocal fractal calculus.Design/methodology/approachFor being generalized, an arbitrary kernel function has been adopted. The condition on order has been derived so that it is not related to the γ-dimension of the fractal set. The fractal Laplace transforms of our operators have been derived.FindingsUnlike the traditional power law kernel-based nonlocal fractal calculus operators, ours are generalized, consistent with the local fractal derivative and use higher degree of freedom. As intended, the proposed nonlocal fractal calculus is applicable to any kind of fractal electrical circuit. Thus, it has been found to be a more efficient tool for the fractal electrical circuit analysis than any previous fractal set dedicated calculus scheme.Originality/valueA fractal calculus scheme that is more efficient for the fractal electrical circuit analysis than any previous ones has been proposed in this work.