Blending Scheme Research Articles

PFB-Diff: Progressive Feature Blending diffusion for text-driven image editing

Diffusion models have demonstrated their ability to generate diverse and high-quality images, sparking considerable interest in their potential for real image editing applications. However, existing diffusion-based approaches for local image editing often suffer from undesired artifacts due to the latent-level blending of the noised target images and diffusion latent variables, which lack the necessary semantics for maintaining image consistency. To address these issues, we propose PFB-Diff, a Progressive Feature Blending method for Diffusion-based image editing. Unlike previous methods, PFB-Diff seamlessly integrates text-guided generated content into the target image through multi-level feature blending. The rich semantics encoded in deep features and the progressive blending scheme from high to low levels ensure semantic coherence and high quality in edited images. Additionally, we introduce an attention masking mechanism in the cross-attention layers to confine the impact of specific words to desired regions, further improving the performance of background editing and multi-object replacement. PFB-Diff can effectively address various editing tasks, including object/background replacement and object attribute editing. Our method demonstrates its superior performance in terms of editing accuracy and image quality without the need for fine-tuning or training. Our implementation is available at https://github.com/CMACH508/PFB-Diff.

Blending of global and regional ensembles in cycling hybrid ensemble-variational data assimilation for the convection-permitting prediction of typhoon Merbok (2017)

Several blending approaches have been implemented in regional cycling data assimilation to introduce the large-scale weather features of the global model forecast or analysis effectively into regional model forecasts. While in previous studies the blending schemes for data assimilation have generally been applied in stand-alone variational data assimilation systems, here we incorporate the new large-scale ensemble blending schemes into the regional hybrid ensemble-variational (EnVar) data assimilation system. It uses a low-pass Raymond tangent implicit filter to introduce the large-scale background or analysis information from global ensembles. Five parallel cycling hybrid ensemble-variational data assimilation experiments for the convection-permitting prediction of typhoon Merbok in 2017 show that the large-scale ensemble information from Global Ensemble Forecast System (GEFS) can suppress cumulative error growth in data assimilation cycles. On the other hand, the large-scale part of the global ensemble improves the typhoon track and intensity forecasts, possibly due to a better description of the horizontal and vertical structure of the typhoon. In addition, the blending scheme using global ensemble analysis provides more accurate rainfall forecasts of typhoon than the other blending schemes.

Investigations of a functional version of a blending surface scheme for regular data interpolation

This paper describes an implementation and tests of a blending scheme for regularly sampled data interpolation, and in particular studies the order of approximation for the method. This particular implementation is a special case of an earlier scheme by Fang for fitting a parametric surface to interpolate the vertices of a closed polyhedron with n-sided faces, where a surface patch is constructed for each face of the polyhedron, and neighbouring faces can meet with a user specified order of continuity. The specialization described in this paper considers functions of the form z=f(x,y) with the patches meeting with C2 continuity. This restriction allows for investigation of order of approximation, and it is shown that the functional version of Fang's scheme has polynomial precision.

Sulfur migration during co-pyrolysis of high sulfur coking coal with fat coal and lean coal

Desulfurization remains a critical yet challenging aspect of producing high-quality metallurgical coke through pyrolysis. Previous studies have suggested that introducing hydrogen-rich gases may improve sulfur removal efficiency. To further explore this approach, we investigated the effects of volatile matter on sulfur migration during co-pyrolysis of high-sulfur coal (SC) with both fat coal (FC) and lean coal (LC). Our findings indicate that most sulfur is transferred into the coke during pyrolysis, with sulfide and pyrite being relatively easy to decompose, while thiophenes and sulfones tend to be thermally stable. Co-pyrolysis with FC significantly decreased sulfur content in the final coke product, while co-pyrolysis with LC resulted in higher sulfur content. The comparative analysis points to the beneficial influence of volatile matter in promoting desulfurization through enhanced sulfur transfer into the gas phase and reduced sulfur fixation with nascent coke. These findings offer practical guidance for coke-making industries to optimize coal blending scheme and to achieve efficient desulfurization during coke-making.

Relevance between various phenomena during coking coal carbonization. Part 4: Insight into the interaction of coal blending

Medium metamorphism strong bonding properties coal is usually used as the main coal in the coal blending scheme. A study of the matching effect of low metamorphism coal is conducive to expanding the utilisation of coal resources. Three kinds of coal: C1 (low metamorphism with strong bonding properties), C2 (low metamorphism with weak bonding properties), and C3 (middle metamorphism and strong bonding properties) were used as research samples. The thermogravimetry-plastometer-swelling pressure (TG-P-SP) apparatus was used to measure the release behaviour of volatiles, the evolution of quantity and performance of the plastic layer, and the change of swelling pressure and volume deformation by mixing C1 and C2 with C3 in different proportions. Blending C1 and C3, the release of volatiles and the evolution of the plastic layer shifted toward the low-temperature zone, and the semi-coke yield was lower than the theoretical expectation. C3 had a strong chemical effect on C1. Other proportions were investigated, although the plasticity was inhibited only when the proportion was equal. The final shrinkage was lower than the predicted result. Blending C2 and C3, the release rate of volatiles was inhibited, and the yield of semi-coke was consistent with the calculation results, and the plasticity of the plastic layer was inhibited. The release behaviour of volatiles and the plastic layer had the opposite effect on the shrinkage interaction. The final shrinkage was close to the ideal calculation value. The interaction was the combination result of coal composition, the correlation among various phenomena, and the multiple interactions of phenomena of coal blending.

Research on highly efficient quality improvement process and product blending scheme for fine coal

In this study, a highly efficient quality improvement process for fine coal was proposed and industrial experiment was conducted. The fine coal was classified with 3 mm flip-flow screen (FFS), and then separated to improve quality by dense medium cyclone (DMC). The vibration characteristics of FFS were analyzed and its capacity was optimized during the screening process. Separation performance toward each particle size of coal in DMC was investigated. According to the products structure of optimized process, a product blending scheme was proposed. The results indicated that the vibration intensity of flip-flow screen surface could reach 27.81. When the capacity was 278 t⋅h−1, the 3 mm screening performance was found to be the best. The separation performances toward each particle size of coal worsened with the decrease of particle size in DMC, and the overall probable error was 0.057 g⋅cm−3. The quality of fine coal after process optimization improved significantly compared to that before process optimization. This study indicates that by using this formulated product blending scheme, the product profit of the fine coal system is expected to increase by 52.85 million RMB annually.

An improved calculation scheme of electron flow in a propagator method for solving the Boltzmann equation

In order to accurately evaluate the electron acceleration process in the calculation of the time evolution of the electron velocity distribution function (EVDF) based on the Boltzmann equation, an improved scheme blending upwind and central differences is introduced into the propagator method (PM). While the previous PM based on the upwind scheme needs fine cells to obtain an accurate EVDF at low electric fields, the improved PM is robust against coarse cells, which allows the reduction of cell resolution. Calculations of the EVDF in Ar under RF electric fields demonstrated that the blending scheme can provide satisfactorily accurate results even with cells about tenfold larger than the upwind case at low reduced electric fields below 1 Td, which leads to much shorter computational time because the reduction in the number of cells satisfactorily compensates for the complexity of the blending scheme. This technique has been built into a new user-friendly PM software named BOSPROM.

Preserving the autocovariance of texture tilings using importance sampling

AbstractBy‐example aperiodic tilings are popular texture synthesis techniques that allow a fast, on‐the‐fly generation of unbounded and non‐periodic textures with an appearance matching an arbitrary input sample called the “exemplar”. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random.In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real‐time tiling and blending scheme that permits the generation of high‐quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.

Development of improved technology for the production of strong wines of marsala type on the basis of enzymatic catalysis

In the assortment of wines produced in the Republic of Azerbaijan, strong wines occupy a significant place. To improve their technology, a number of technological modes and parameters aimed at improving the quality of finished products and the use of modern technology were proposed and tested. As a result of the study, the effectiveness of the use of the mixed-action enzyme preparation Vinozim or Ultrazyme in the production of strong vintage wine of the Marsala type according to the blending scheme using the first must and press fractions was determined. It has been established that the use of this preparation in the process of fermentation of pomace provides a rational supply of phenolic substances for blends of marsala, and in the process of infusion of pomace of nitrogenous components and pentosis. The fundamental possibility of obtaining high-quality wine materials for vintage marsala under the conditions of processing grapes of the variety Bayan-Shirei and Rkasiteli "in the red way" has been studied. Rational modes for obtaining blended wine materials for Marsala, based on the technology of fermentation or long-term infusion of pomace, have been developed. The effect of various enzyme preparations on the quality of wine materials for Marsala has been studied; a rational variant of blending and aging of wine materials for Marsala was experimentally substantiated. It has been established that the rational option for obtaining vintage marsala is a blend of dry-fortified wine material and mistel, or dry-fortified wine material and vacuum must. Analysis of the dynamics of maturation of blended wine materials and data on changes in their chemical composition confirmed the choice of a rational scheme for their production and made it possible to establish the expediency of aging blends for marsala for 2.5 years. To improve the quality of ordinary marsala wine, a new technique for producing vacuum must was devised

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO2, TiO2, Al2O3, P, Mn, Na2O, K2O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line.

Performance analysis and reconstruction of interface capturing schemes for multiphase flows

This article aims to provide a roadmap for analysis of the interface capturing schemes and their possible modification with recommended design principles. Blending the suitable high resolution (HR) scheme with a bounded downwind (BD) scheme is the core of the development of any interface capturing scheme. However, the concrete guidelines for choice of HR and BD schemes are not clear. For this purpose, the HR and BD constituents of a popular scheme known as “Switching Technique for Advection and Capturing of Surfaces (STACS)” are analyzed. Subsequently, it is observed that the scheme fails to satisfy some critical design principles. This leads to a reconstruction of the scheme by introducing a newly compliant bounded downwind scheme. The novel re-constructed scheme is then accessed through typical advection problems, including the shear flow on structured and unstructured meshes. The scheme convincingly outperforms its parent scheme in all test cases irrespective of the Courant number. Moreover, it generates superior or at least at par results while compared with a few well-established schemes like Compressive Interface Capturing Scheme for Arbitrary Meshes (CICSAM) and Cubic Upwind Interpolation based Blending Scheme (CUIBS).

Study on interaction and mechanisms of direct coal liquefaction residue and gas coal in coal blending during coking

Crucible cokes were prepared by blending direct coal liquefaction residue (DCLR) with five bituminous coals (JM, FM, 1/3JM, QM, SM). The cold strength and thermal strength of cokes were analyzed. Coal petrology was used to guide and optimize coal blending for coking which was aimed at finding out the mechanism of DCLR and QM partially replacing coking coal (FM or JM) during coking. Results showed that QM and DCLR could partially replace FM or JM when the ratio of high caking coal was under 70%, and the DCLR ratio cannot exceed 6% to guarantee the coke strength. Based on blending scheme JT20, partially replacing JM with DCLR and QM (termed as DCLR-QM-JM system), the optimal DCLR ratio was 3% as M13 increased by 1.20%, M3 declined by 1.21% and CSR* increased by 0.79%. Partially replacing FM with DCLR and QM (termed as DCLR-QM-FM system), the optimal DCLR ratio was 2% as M13 increased by 2.14%, M3 declined by 2.13% and CSR* increased by 1.97%. There existed obvious interactions between raw materials in coal blending and the function mechanism of DCLR and QM could be concluded as follows: With the addition of DCLR, temperature range of the interaction between metaplast and gas in coking system extended to the low temperature region by 50 °C. The continuous gas driving force provided by QM drove much metaplast into the pores of coal particles and bound them to more inert components, which led to decreasing reaction surface and increasing thermal strength of coke.

Study on the Conventional Performance and Microscopic Properties of PPA/SBS-Modified Bio-Mixed Asphalt.

To promote the construction of environmentally friendly, sustainable pavements and solve the impact of the scarcity of asphalt resources on highway development, bio-mixed asphalt (BMA) modified by SBS and polyphosphoric acid (PPA) was prepared, and the influence of the ratio of bio-asphalt (BA) replacing petroleum asphalt on different PPA/SBS blending schemes was explored through conventional property tests. According to each PPA/SBS blending scheme, the optimal replacement ratio of bio-asphalt was optimized, and the microstructure and distribution morphology of different PPA/SBS-modified BMA were evaluated. Conventional property test results show that with the same PPA/SBS content, the replacement ratio of bio-asphalt has a significant impact on the conventional performance of composite-modified asphalt, but the appropriate replacement ratio of bio-asphalt can improve the storage stability and conventional performance of composite-modified asphalt; in micromorphological analysis, it was found that the number of bee-like structures on the surface of the modified BMA decreased significantly, which indicated that the molecular heterogeneity of various components in the asphalt was reduced. In addition, bio-asphalt changed the particle morphology and improved the dispersity of SBS in asphalt. The composite-modified BMA had a lower SBS content, but its conventional performance was still excellent—so it has significant application prospects in road engineering.

Remixing functionally graded structures: data-driven topology optimization with multiclass shape blending

To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversity-based freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.

Sintering and Smelting Property Investigations of Ludwigite

In this paper, orthogonal experiments are designed to study the sintering and smelting characteristics of the ludwigite ore. The predominant influencing factors of the optimal ratio, basicity and carbon content on different single sintering indexes, including the vertical sintering speed, yield rate, drum strength and low-temperature reduction pulverization index, are firstly explored by the range analysis method, and the main influencing factors on comprehensive indexes are obtained by a weighted scoring method based on different single index investigation. Considering the sintering characteristics, the primary and secondary influencing factors are: ordinary ore ratio, carbon content and basicity, and the optimal ore blending scheme is: basicity 1.7, ordinary ore blending ratio 60% and carbon content 5%. In terms of the smelting characteristics, the research obtains the order of the influencing factors on the softening start temperature, softening end temperature, softening zone, smelting start temperature, dripping temperature, smelting-dripping zone, maximum pressure difference and gas permeability index of the ludwigite sinters by simply considering various single smelting indexes. On this basis, considering the comprehensive softening-melting-dripping characteristics, the primary and secondary influencing factors are: carbon content, ordinary ore ratio and basicity, and the optimal ore blending scheme is: basicity 1.9, ordinary ore blending ratio 60% and a carbon content of 5.5%. Comprehensively, considering the sintering and smelting property of the ludwigite ore, the primary and secondary influencing factors are: carbon content, ordinary ore ratio and basicity, and the optimal ore blending scheme is: basicity 1.9, ordinary ore blending ratio 60% and a carbon content of 5.5%.

Imitation and mirror systems in robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.

Multi-Objective Optimization Model of Industrial Lubricants Based on Integer Nonlinear Programming

Based on actual lubricating oil production data and the base oil performance indexes of an enterprise, two nonlinear blending schemes corresponding to viscosity and freezing point and four linear blending schemes corresponding to acid value, flash point, oxidation stability, and carbon residue are given in this paper. On the premise that the error of each index is less than 5%, a linear weighted multi-objective optimization model based on integer nonlinear programming considering cost and performance is established, and the lubricating oil blending scheme is obtained. The results show that the blending formula is simple in form and convenient in calculation, and that the overall consistency between the calculated value and the measured value is good. At the same time, the relative error of each performance index, except residual carbon, of the scheme with weight value of (0.5, 0.5) is far less than 5%. Although the performance index is slightly inferior to that of the scheme with a weight value of (0, 1), it is far higher than that of the scheme with a weight value of (1, 0). The linear weighted multi-objective optimization model based on integer nonlinear programming proposed in this paper can well-optimize the blending scheme of industrial lubricating oil, and can re-select different weight combinations according to the actual situation, providing good prospects for application.

High-performance adaptive texture streaming and rendering of large 3D cities

We propose a high-performance texture streaming system for real-time rendering of large 3D cities with millions of textures. Our main contribution is a texture streaming system that automatically adjusts the streaming workload at runtime based on measured frame latencies, specifically addressing the high memory binding costs of hardware virtual texturing which causes frame rate stuttering. Our system streams textures in parallel with prioritization based on GPU computed mesh perceptibility, and these textures are cached in a sparse partially resident image at runtime without the need for a texture preprocessing step. In addition, we improve rendering quality by minimizing texture pop-in artifacts using a color blending scheme based on mipmap levels. We evaluate our texture streaming system using three structurally distinct datasets with many textures and compared it to a baseline, a game engine, and our prior method. Results show an 8X improvement in rendering performance and 7X improvement in rendering quality compared to the baseline.

An improved watermarking scheme for color image using alpha blending

This paper proposes a robust and secure watermarking method for a color image in Y CbCr color space. In this study, watermarking is performed using Lifting Wavelet Transform (LWT). Here, edge entropy and information entropy is used to find the block to embed watermark. In this work alpha blending scheme is used for embedding and extraction of watermarks in the LWT domain. The use of LWT makes the proposed scheme faster and more efficient. The Arnold Cat Map (ACM) is used to enhance watermark security. Numerous tests are presented to illustrate the feasibility of the proposed scheme. The experimental results obtained are compared to state-of-the-art schemes, which demonstrate the superiority of the proposed scheme.

Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States

This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval's use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.