Diffusion Model Research Articles

Multi-period urban hydrogen refueling stations site selection and capacity planning with many-objective optimization for hydrogen supply chain

The location and quantity of hydrogen refueling stations (HRSs) play a crucial role in the development and promotion of hydrogen fuel cell vehicles (HFCVs). This study proposes a multi-period urban HRSs site selection and capacity planning with many-objective optimization framework for hydrogen supply chain (HSC). Firstly, the city's multi-period hydrogen requirement is predicted based on a generalized Bass diffusion model. Using publicly available data, including gas station network data, geographic information system (GIS) data, population data, and regional economic data, a spatially aggregated demand model is established to allocate hydrogen requirement at candidate sites in the city of the geographic grid model with a 1 km resolution. On this basis, four interrelated objective functions (total investment cost, hydrogen requirement coverage, risk coefficient, and environmental factors) are developed. The third-generation non-dominated sorting genetic algorithm (NSGA-III) and the technique for order preference by similarity to an ideal solution (TOPSIS) are employed to achieve multi-period urban HRSs site selection and capacity planning with many-objective optimization for HSC. By comparing the results of single-objective optimization focusing on minimum cost and maximum hydrogen requirement coverage, it is observed that many-objective optimization achieves a better balance among the four conflicting objectives. After comprehensive analysis, the distribution of HRSs exhibits a clustered structure, influenced by the population and economic structure of the city. In the initial stages of HFCV development, most stations serve the peripheral areas around the city center; while n the later stages of the optimization period, a few high-capacity HRSs begin to concentrate in the city center.

Orientation selectivity mapping in the visual cortex

The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.

BiDiCOS: Camouflaged object segmentation via bilateral diffusion model

The task of camouflaged object segmentation (COS) is to extract objects with camouflage characteristics that are difficult to detect in an image. The current mainstream COS method mainly relies on the general object segmentation framework using the encoder-decoder structure to predict the objects. However, the current COS framework suffers from over-reliance on the labeled ground truth and the output results. To this end, we propose a bilateral diffusion model for camouflaged object segmentation (BiDiCOS). The main innovation of BiDiCOS is that we design a new bilateral diffusion paradigm, transforming the segmentation and depth estimation tasks into denoising tasks. At the same time, the depth estimation branch can provide semantic information to the segmentation branch. Specifically, we design the adaptive feature fusion module for two branches to provide the required features adaptively. We design the bilateral feature fusion module to fuse the depth information with the mask information to reduce the semantic and logical errors during segmentation. Finally, we develop the specialized training strategy and inference strategy for BiDiCOS, which provide a solid foundation for realizing rational training and accurate prediction. Comprehensive experiments on four challenging COS datasets attest to the superior performance of BiDiCOS compared to the existing 20 COS methods. Our BiDiCOS achieves state-of-the-art with different sizes of backbones, where the MAE is reduced by 4.3 % on average. We have also performed comparative experiments on depth estimation. The experiments demonstrate that BiDiCOS can achieve a win–win situation for both tasks. The code will be available at https://github.com/jiangxinhao2020/BiDiCOS.

Removal of Fe and Mn from Co leach solutions by adsorption on activated carbon based on post-consumer polyethylene terephthalate (PET) - Mechanism insights through linear and nonlinear isotherm and kinetic models

ABSTRACT The removal of Fe2+ and Mn2+ from cobalt sulfate-saturated solutions is frequently problematic for Cu-Co hydrometallurgical process performance. Among the applied and developed methods for the removal of these metals, adsorption has drawn less attention. This study investigates on the removal of Fe2+ and Mn2+ in industrial cobalt solutions known as raffinates by adsorption on activated carbon prepared from post-consumer polyethylene terephthalate (PET). The adsorption operating conditions were studied in synthetic solutions and industrial solutions. The Langmuir, Freundlich, and Temkin isotherms were then used to fit the adsorption data, while the pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich models were used to describe the kinetics. Linear and nonlinear kinetic and isotherm models were studied and compared. Furthermore, intraparticle diffusion (IPD) models were used to study the transfer mass mechanism. The results show that Fe2+ and Mn2+ are removed from Co solutions with the efficiencies of 62.14 and 68.43, respectively. The Langmuir model fits the Fe2+ and Mn2+ adsorption data better, indicating that chemisorption is preferred over physisorption, whereas the Freundlich model fits the Co2+ adsorption data better, indicating that physisorption is preferred over chemisorption. The PFO model describes well the kinetic behavior of Fe2+ and Mn2+, whereas the Elovich model describes better the kinetic behavior of Co2+. According to the IPD model, the step that limits the adsorption of Fe2+, Mn2+, and Co2+ is essentially diffusion through the activated carbon pores. The reported experimental results will highlight the use of activated carbon in the removal of Fe2+ and Mn2+ in Cu and Co hydrometallurgy.

How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N = 17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift–diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task.

Parameter sharing fault data generation method based on diffusion model under imbalance data

Abstract Rotating machinery will inevitably fail under long-term heavy load working conditions. Obtaining enough data to train the deep learning model can enable managers to detect and deal with related failures in time, which greatly improves the safety of equipment operation. Mechanical fault samples are often much smaller than healthy samples. Traditional data enhancement methods mostly change the original data, but cannot improve the diversity of its features, so that the number of fault samples becomes larger, but the features remain unchanged. Aiming at the above problems, a diffusion model based on parameter sharing and inverted bottleneck residual structure (DDPM) is proposed. Firstly, the diffusion process gradually covers the original data with Gaussian noise, to learn the corresponding fault characteristics of the original data. In the diffusion process, the parameter sharing attention mechanism is embedded in the learning process of the diffusion process. Then, the feature extraction module is constructed by using the inverted bottleneck residual structure to enhance the learning ability of the network. After obtaining the fault characteristics of the original data, the reverse process of the results restores the Gaussian noise to data with different fault characteristics through the same steps as the diffusion process. By comparing the results of various generation models and analysing the characteristics of the generated data, the feasibility and universality of the proposed method in data augmentation tasks are verified.

Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models.

In the sphere of urban renewal of historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and their internal function. This study explored an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a low-rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of the generated images. The methodology's performance was validated through qualitative and quantitative assessments, optimizing the batch size, repetition, and learning rate configurations. These evaluations confirmed the method's effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains the aesthetic and functional integrity, thereby fostering breakthroughs in traditional design thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.

Non-decision time: The Higgs Boson of decision.

Generative models of decision now permeate all subfields of psychology, cognitive, and clinical neuroscience. To successfully investigate decision mechanisms from behavior, it is necessary to assume the presence of delays prior and after the decision process itself. However, directly observing this "non-decision time (NDT)" from behavior long appeared beyond reach, the field mainly relying on models to estimate it. Here, we propose a biological definition of decision that includes perceptual discrimination and action selection, and in turn, explicitly equates NDT with the minimum sensorimotor delay, or "deadtime." We show how this delay is directly observable in behavioral data, without modeling assumptions, using the visual interference approach. We apply this approach to 11 novel and archival data sets from humans and monkeys gathered from multiple labs. We validate the method by showing that visual properties (brightness, color, size) consistently affect empirically measured visuomotor deadtime (VMDT), as predicted by neurophysiology. We then show that endogenous factors (strategic slowing, attention) do not affect VMDT. Therefore, VMDT consistently satisfies widespread selective influence assumptions, in contrast to NDT parameters from model fits. Last, contrasting empirically observed VMDT with NDT estimates from the EZ, drift diffusion, and linear ballistic accumulator models, we conclude that NDT parameters from these models are unlikely to consistently reflect visuomotor delays, neither at a group level nor for individual differences, in contrast to a widely held assumption. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Diffusion distance variations in coal pulverization based on equivalent matrix size: Implications for coal and gas outburst indicators

Coal matrix is rich in complex pores, constituting channels for gas diffusion. Diffusion distance plays a crucial role in diffusion resistance, directly influencing the intermediate processes of “desorption-diffusion-seepage” in coal. However, current research often quantifies diffusion distance using average particle size, failing to represent the true diffusion distance. In this study, we introduce a novel concept of equivalent matrix size based on gas desorption experiments. Diffusion coefficients were obtained by fitting the time-varying diffusion model, and the mass exchange of interporosity flow was estimated. Additionally, temporal matrix shape factors were analyzed to determine the matrix size with the aid of numerical simulation. Results demonstrate a temporal evolution in matrix shape factors, namely a decrease in the initial stage, stability in the medium stage, and inconsistency in the later stage. During pulverization, fracture and matrix damage occur simultaneously, with fractures undergoing more severe fragmentation. Lost time significantly impacts the accuracy of accessing lost gas amount ML and outburst indicator K1. Upon incorporating the matrix size, ML for XT and QD samples were overestimated by 0.83 cm3/g and 0.14 cm3/g, and K1 was overestimated by 21.2 % and 2.7 %, which could help establish a theoretical foundation for predicting coal and gas outbursts.

Amplitude equations for wave bifurcations in reaction–diffusion systems

A wave bifurcation is the counterpart to a Turing instability in reaction–diffusion systems, but where the critical wavenumber corresponds to a pure imaginary pair rather than a zero temporal eigenvalue. Such bifurcations require at least three components and give rise to patterns that are periodic in both space and time. Depending on boundary conditions, these patterns can comprise either rotating or standing waves. Restricting to systems in one spatial dimension, complete formulae are derived for the evaluation of the coefficients of the weakly nonlinear normal form of the bifurcation up to order five, including those that determine the criticality of both rotating and standing waves. The formulae apply to arbitrary n-component systems ( n⩾3 ) and their evaluation is implemented in software which is made available as supplementary material. The theory is illustrated on two different versions of three-component reaction–diffusion models of excitable media that were previously shown to feature super- and subcritical wave instabilities and on a five-component model of two-layer chemical reaction. In each case, two-parameter bifurcation diagrams are produced to illustrate the connection between complex dispersion relations and different types of Hopf, Turing, and wave bifurcations, including the existence of several codimension-two bifurcations.

Experimental and DFT calculation study on the efficient removal of high fluoride wastewater from metallurgical wastewater by kaolinite

Fluoride pollution and water scarcity are urgent issues. Reducing fluoride concentration in water is crucial. Kaolinite has been used to study adsorption and fluoride removal in water and to characterize material properties. The experimental results showed that the adsorption capacity of kaolinite decreased with increasing pH. The highest adsorption of fluoride occurred at pH 2, with a capacity of 11.1 mg/g. The fluoride removal efficiency remained high after four regeneration cycles. The fitting results with the Freundlich isotherm model and the external diffusion model showed that the non-homogeneous adsorption of kaolinite fit the adsorption behavior better. Finally, the adsorption mechanism was analyzed by FT-IR and XPS. The binding energies of various adsorption sites and the chemical adsorption properties of atomic states were discussed in relation to DFT calculations. The results showed that Al and H sites were the main binding sites, and the bonding stability for different forms of fluoride varies, with the size of Al–F (−7.498 eV) > H–F (−6.04 eV) > H–HF (−3.439 eV) > Al–HF (−3.283 eV). Furthermore, the density of states and Mulliken charge distribution revealed that the 2p orbital of F was found to be active in the adsorption process and was the main orbital for charge transfer.

A Markov Chain approach for video-based virtual try-on with denoising diffusion generative adversarial network

Video-based virtual try-ons have attracted unprecedented attention owing to the development of e-commerce. However, this problem is very challenging because of the arbitrary poses of persons and the demand for temporary consistency of frames, particularly when attempting to synthesize high-quality virtual try-on videos using single images. Specifically, there are two key challenges. 1) The existing video-based virtual try-on methods are based on generative adversarial networks (GAN), which are limited by unstable training and a lack of realism in generated details. 2) The explicit building of stronger constraints of generated frames, which aims to increase the coherence of generated videos. To address these challenges, this study proposed a novel framework, Extended Markov Chain Based Denoising Diffusion Generative Adversarial Network (EMC-DDGAN), which was derived from a denoising diffusion GAN, which is a diffusion model with efficient sampling. Moreover, we proposed an extended Markov chain that used a diffusion model to synthesize frames via sequential generation. With a carefully designed network and learning objects, the proposed approach achieved outstanding performance on public datasets. Rigorous experiments demonstrated that EMC-DDGAN could synthesize higher-quality videos compared to other state-of-the-art methods and validated the effectiveness of the proposed approach.

Detecting images generated by diffusers.

In recent years, the field of artificial intelligence has witnessed a remarkable surge in the generation of synthetic images, driven by advancements in deep learning techniques. These synthetic images, often created through complex algorithms, closely mimic real photographs, blurring the lines between reality and artificiality. This proliferation of synthetic visuals presents a pressing challenge: how to accurately and reliably distinguish between genuine and generated images. This article, in particular, explores the task of detecting images generated by text-to-image diffusion models, highlighting the challenges and peculiarities of this field. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple multi-layer perceptrons (MLPs), starting from features extracted by CLIP or RoBERTa, or using traditional convolutional neural networks (CNNs). These latter models achieve remarkable performances in particular when pretrained on large datasets. We also observe that models trained on images generated by Stable Diffusion can occasionally detect images generated by GLIDE, but only on the MSCOCO dataset. However, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images in some cases can lead to a better generalization capability, especially if textual features are closely related to visual ones. We also discovered that the type of subject depicted in the image can significantly impact performance. This work provides insights into the feasibility of detecting generated images and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers.

Unraveling the enigmatic role of the subthalamic nucleus.

Subpopulations of neurons in the subthalamic nucleus have distinct activity patterns that relate to the three hypotheses of the Drift Diffusion Model.

Research on prepositioned air distribution and dust control devices for continuous mining faces

This study, based on the construction of a wind flow-dust two-phase coupled diffusion model, develops a novel method using a prepositioned air distributor to create an air curtain for dust control. This research involved the analysis of wind flow transportation patterns and dust diffusion mechanisms with varying structural parameters, and the verification of simulation results through field tests. The findings indicate that with the increase of the axial horizontal opening angle of the prepositioned air distributor, the wind speed of the air curtain decreases, while the size of the vortex field in the heading face area expands. Increasing the axial vertical opening angle enhances the strength of the vortex, leading to improved dust control abilities. The dust concentration at the rear of the tunnel initially decreases and then rises. Additionally, as the turning angle increases, the speed at which the wind flow diffuses towards the rear of the tunnel also rises. Compared with traditional ventilation methods, the dust reduction efficiency at the respiratory zone height is improved to over 94.0 %. The concentration of dust within the tunnel is significantly reduced, with approximately 98.9 % of the dust confined within a 10.0 m range from the heading face.

Gas desorption characteristics of coal particles under dynamic temperature changes: Implication for the lost gas estimation during borehole coal sampling

The estimation of lost gas during borehole coal sampling determines the accuracy of coalbed methane (CBM) content determination. The isothermal desorption data is used frequently for analyzing the estimation error of the lost gas, which is not consistent with the engineering practice that the gas desorption during borehole coal sampling is in dynamic temperature changing condition (i.e., DTC), thus resulting in the result of the lost gas estimation is not accurate. In this study, a DTC adsorption/desorption experimental system was set up to study the gas desorption characteristics of coal particles during borehole coal sampling, and the differences between isothermal desorption and DTC desorption were analyzed in detail. Furthermore, the isothermal desorption data and DTC desorption data were fitted using the unipore diffusion model (UDM) to discuss the influence of DTC desorption on the lost gas estimation. Finally, the fractional diffusion model (FDM) was applied to estimate the lost gas. The results showed that there is a significant difference between isothermal desorption and DTC desorption. For the case of DTC desorption, more gas will be lost at the same exposure time, resulting in the lost gas being underestimated in the CBM content determination. With the exposure time of 12 min, the average fitting errors of lost gas using the FDM with different coal particle sizes and gas pressures are less than 17.9%, while that using the UDM are greater than 57.5%, suggesting that the FDM could improve the fitting accuracy of lost gas estimation in the CBM content determination.

Analysis of the explicit volume diffusion subgrid closure for the [formula omitted] model to interfacial flows over a wide range of Weber numbers

The explicit volume diffusion (EVD) method was recently proposed for simulating interfacial flows based on the volume of fluid method. In this work, the EVD equations are derived rigorously from the Σ−Y (surface density and mass fraction) perspective under the large eddy simulation (LES) framework. Volume averaging is applied over an explicit length scale ℓV that is grid-independent. The sub-volume flux and stress are closed through gradient diffusion and viscosity models that account for the presence of an interface and turbulence, and have the correct limits in the absence of interface vorticity and in the pure phases. An EVD equation for Σ is introduced for the first time to model the volume averaged surface tension. The EVD model and closures are tested for three different liquid jet breakup cases: a series of laminar round jets in the Rayleigh-Plateau regime (12<Wel<34), a turbulent coaxial air-blast jet (Weg=75) and a high Weber number turbulent crossflow (Weg=330). Numerical convergence and sensitivity to ℓV are investigated, along with comparisons to linear theory, experimental data, empirical correlations and previous simulations. The effects of the closures on flow dynamics and key dimensionless numbers are also analysed.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Cascaded learning with transformer for simultaneous eye landmark, eye state and gaze estimation

Eye tracking have garnered attention in human–machine interaction, disease monitoring, biometrics, etc. Existing investigations for eye tracking have predominantly concentrated on individual task for pupil detection or gaze estimation, overlooking the implicit relationships that exist among different tasks for eye tracking. In this work, we introduce a cascaded framework with transformer to collaboratively realize eye landmark detection, eye state detection and gaze estimation. Within our framework, we leverage Transformer to capture long dependencies with explicit eye-related structural information and implicit correlation among different tasks. Furthermore, the proposed cascade iteration framework alternatively optimize each task and boost the overall performance for pupil center, eye state and gaze estimation simultaneously. To address the problem of manual annotation, we further introduce the Control-Eye Diffusion Model (CEDM), a controllable eye image generation method conditioned on a simple contour with structure information. The proposed methods are evaluated on challenging datasets such as GI4E, BioID and MPIIGaze, and the results show that our methods outperform state-of-the-art methods in several tasks.

Research regarding the influence of size on the compressive strength of spontaneous combustion coal gangue concrete utilizing the diffusion model

This passage employs the U2-Net model to segment images of spontaneous combustion gangue coarse aggregate (SCGCA) concrete cross-section and randomly placed aggregate library. These methods are used to construct the dataset and subsequently establish and train the diffusion model. Based on this model, a two-dimensional micro-mechanical model of SCGCA concrete was constructed, accurately reproducing the distribution, grading, and shape of the aggregate in the concrete. The size effect of SCGCA concrete is analyzed through experimentation and numerical simulation of two-dimensional micro-models. The findings of the study reveal that the dimension conversion factors for the 100 mm and 200 mm cubic specimens are 0.92 and 1.12 respectively, which demonstrates the evident size effect of SCGCA concrete. The remarkable coherence between the empirical results and the computational simulation results serves as a powerful validation of the durability and efficacy of the model delineated in this paper. In accordance with the theory of Bažant's size effect, this paper elucidates the size effect law of uniaxial compressive strength in SCGCA concrete models ignited spontaneously at different dimensions.