In the paper,we introduce some of multipliers on residuated lattices and investigate the relations among them.First,basing on the properties of multipliers,we show that the set of all multiplicative multipliers on a r...In the paper,we introduce some of multipliers on residuated lattices and investigate the relations among them.First,basing on the properties of multipliers,we show that the set of all multiplicative multipliers on a residuated lattice A forms a residuated lattice which is isomorphic to A.Second,we prove that the set of all total multipliers on A is a Boolean subalgebra of the residuated lattice(which is constituted by all multiplicative multipliers on A)and is isomorphic to the Boolean center of A.Moreover,by partial multipliers,we study the maximal residuated lattices of quotients for residuated lattices.Finally,we focus on principal implicative multipliers on residuated lattices and obtain that the set of principal implicative multipliers on A is isomorphic to the set of all multiplicative multipliers on A under the opposite(dual)order.展开更多
In this paper, the problem of bipolar fuzzy ideal is further studied in non-involutive residuated lattices. The notion of normal bipolar fuzzy ideal is introduced, some important properties and equivalent characteriza...In this paper, the problem of bipolar fuzzy ideal is further studied in non-involutive residuated lattices. The notion of normal bipolar fuzzy ideal is introduced, some important properties and equivalent characterizations of normal bipolar fuzzy ideals are obtained. In addition, two special types of normal bipolar fuzzy ideals are defined, which are called maxima and completely normal bipolar fuzzy ideals, respectively, and their relationships are discussed.This work further expands the way for revealing the structural characteristics of non-involutive residuated lattices.展开更多
This paper is devoted to the discussion of filters in residuated lattices. Some equivalent conditions about filter were given. The structure of generated filter was established. The concept of implicative filter in re...This paper is devoted to the discussion of filters in residuated lattices. Some equivalent conditions about filter were given. The structure of generated filter was established. The concept of implicative filter in residuated lattice was proposed with its basic properties being discussed.展开更多
Residuated lattice is an important non-classical logic algebra, and L-fuzzy rough set based on residuated lattice can describe the information with incompleteness, fuzziness and uncomparativity in information systems....Residuated lattice is an important non-classical logic algebra, and L-fuzzy rough set based on residuated lattice can describe the information with incompleteness, fuzziness and uncomparativity in information systems. In this paper, the representation theorems of L-fuzzy rough sets based on residuated lattice are given. The properties and axiomatic definition of the lower and upper approximarion operators in L-fuzzy rough sets are discussed.展开更多
This paper is devoted to the discussion of filters in residuated lattices. The lattice structure of filters in residuated lattice was established. It is proved that the set of all filters forms a distributive lattice....This paper is devoted to the discussion of filters in residuated lattices. The lattice structure of filters in residuated lattice was established. It is proved that the set of all filters forms a distributive lattice. Also, the concept of prime filter in residuated lattice was proposed and some equivalent conditions about prime filter were given.展开更多
In this paper, we introduce the notion of L-topological spaces based on a complete bounded integral residuated lattice and discuss some properties of interior and left (right) closure operators.
We use a semantical method of complete residuated lattice-valued logic to give a general- ization of fuzzy topology as a partial answer to a problem by Rosser and Turquette.
Carbon fiber-reinforced carbon aerogel(C/CA)composites are one of the most promising candidates for applications requiring both thermal insulation and load bearing capabilities.The preparation of anti-oxidation coatin...Carbon fiber-reinforced carbon aerogel(C/CA)composites are one of the most promising candidates for applications requiring both thermal insulation and load bearing capabilities.The preparation of anti-oxidation coatings on C/CA to address its susceptibility to oxidation is a feasible approach to promote its application in oxidative environments.However,the currently reported coatings on C/CA mainly focus on improving the ablation performance and coating preparation process typically necessitating high-temperature heat treatment.This procedure can increase its thermal conductivity and reduce its thermal insulation ability.In this study,a series of ceramic-resin coatings were fabricated on C/CA through a simple slurry brushing-drying approach at room temperature.The effects of phenolic resin content on the coating structure,residual stress,thermal shock,and oxidation behaviors were investigated.Due to the adhesive properties and curing-induced shrinkage,the PR-7.5 coating(containing 7.5%(in mass)phenolic resin in the slurry)exhibits bonding strength close to fracture strength of the substrate and residual compressive stress of 0.853 GPa,which is beneficial for resisting thermal shock cracking.However,excessive resin content(PR-10.0 containing 10.0%(in mass)phenolic resin in the slurry)induces tensile stress due to uneven curing shrinkage,thereby leading to thermal shock cracking.Meanwhile,oxidation tests reveal significantly reduced weight losses for PR-7.5(17.46%at 800℃/100 min,8.15%at 1000℃/120 min,3.15%at 1200℃/120 min)versus uncoated C/CA’s 44.60%loss at 800℃/20 min.This work provides a brand-new and simple approach to improving the anti-oxidation performance of C/CA and expands its application in mild oxidative environments.展开更多
We give two generalizations of Tarski’s fixpoint theorem in the setting of residuated lattices and use them to establish van Emdem-Kowalski’s least fixpoint semantics for residuated lattice-valued logic programs.
We consider particular compatible orders on a given completely simple semi- group Sx= M（（x）; I, A; P） where （x） is an ordered cyclic group with x 〉 1 and p11= x-1. Of these, only the lexicographic and bootlace ...We consider particular compatible orders on a given completely simple semi- group Sx= M（（x）; I, A; P） where （x） is an ordered cyclic group with x 〉 1 and p11= x-1. Of these, only the lexicographic and bootlace orders yield residuated semigroups. With the lexicographic order, Sx is orthodox and has a biggest idempotent. With the bootlace order, the maximal idempotents of Sx are identified by specific locations in the sandwich matrix. In the orthodox case there is also a biggest idempotent and, for sandwich matrices of a given size, uniqueness up to ordered semigroup isomorphism is established.展开更多
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor...Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.展开更多
In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and rec...In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.展开更多
Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge percep...Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge perception,especially under complex dynamic blur.To address these challenges,we propose the Multi-Resolution Fusion Network(MRFNet),a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion.The network employs a three-stage design:(1)TransformerBlocks capture long-range dependencies and reconstruct coarse global structures;(2)Nonlinear Activation Free Blocks(NAFBlocks)enhance local detail representation and mid-level feature fusion;and(3)an optimized residual subnetwork based on gated feature modulation refines texture and edge details for high-fidelity restoration.Extensive experiments demonstrate that MRFNet achieves superior performance compared to state-of-the-art methods.On GoPro,it attains 32.52 dB Peak Signal-to-Noise Ratio(PSNR)and 0.071 Learned Perceptual Image Patch Similarity(LPIPS),outperforming MIMOWNet(32.50 dB,0.075).On HIDE,it achieves 30.25 dB PSNR and 0.945 Structural Similarity Index Measure(SSIM),representing gains of+0.26 dB and+0.015 SSIM over MIMO-UNet(29.99 dB,0.930).On RealBlur-J,it reaches 28.82 dB PSNR and 0.872 SSIM,surpassing MIMO-UNet by+1.19 dB and+0.035 SSIM(27.63 dB,0.837).These results validate the effectiveness of the proposed progressive residual fusion and hybrid attention mechanisms in balancing global context understanding and local detail recovery for blind image deblurring.展开更多
Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significan...Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significant lateral and vertical variability in crude oil properties,all of which contribute to limited recovery.To support more effective field development,alternative strategies and a deeper understanding of pore-scale flow behavior are urgently needed.In this work,CT imaging and digital image processing were used to construct a digital rock model representative of the target reservoir.A pore-scale flow model was then developed,and the Volume of Fluid(VOF)method was applied to simulate and optimize waterflooding schemes aimed at boosting oil recovery.Optimization focused on adjusting injection rates,varying the oil–water viscosity ratio,and implementing a water-alternating-gas(WAG)process.Results show that,for equal injection volumes,higher injection rates cause early water breakthrough through high-permeability pathways,yielding slower gains in recovery.Lowering the oil–water viscosity ratio improves mobility control,suppresses viscous fingering,enlarges sweep volume,and enhances recovery.When CH_(4)becomes fully miscible,it dissolves into the crude oil,lowering viscosity and eliminating interfacial tension,thereby providing greater displacement efficiency than partially miscible injection.Following a switch from water to gas injection,residual oil saturation decreases and becomes more uniformly distributed,indicating that the combined action of water and gas significantly improves both sweep efficiency and microscopic displacement.展开更多
This study investigates the influence of loading frequency on the fatigue behavior of ballastless track concrete for high-speed railways,aiming to support the development of concrete capable of withstanding higher ope...This study investigates the influence of loading frequency on the fatigue behavior of ballastless track concrete for high-speed railways,aiming to support the development of concrete capable of withstanding higher operational speeds.Fatigue tests were conducted at loading frequencies ranging from 5 to 40 Hz,with a focus on fatigue life,damage evolution,energy dissipation,and residual fatigue strain in the concrete.The results indicate that between 5 and 15 Hz,the fatigue life and energy dissipation remain relatively stable,with minimal damage evolution and small residual strains.As the frequency increases to 15-20 Hz,the fatigue life and energy dissipation gradually decrease,while damage accumulation and residual strain increase.Beyond 20 Hz,both fatigue life and energy dissipation decrease rapidly,damage accumulation becomes more pronounced,and residual strain continues to rise.These phenomena are primarily attributed to the increased strain rate and load change rate at higher frequencies,which affect the microstructure evolution and lead to reduced fatigue performance.展开更多
This study explores a novel method for processing cotton stalks—an abundant agricultural byproduct—into long strips that serve as sustainable raw material for engineered bio-based panels.To evaluate the effect of ra...This study explores a novel method for processing cotton stalks—an abundant agricultural byproduct—into long strips that serve as sustainable raw material for engineered bio-based panels.To evaluate the effect of raw material morphology on panel’s performance,two types of cotton stalk-based panels were developed:one using long strips,maintaining fiber continuity,and the other using ground particles,representing conventional processing.A wood strand-based panel made from commercial southern yellow pine strands served as the control.All panels were bonded using phenol-formaldehyde resin and hot-pressed to a target thickness of 12.7 mm and density of 640 kg/m^(3).Their mechanical and physical properties were evaluated through internal bond,bending,thickness swelling,and water absorption tests.Both cotton stalk-based panels showed improved bonding performance compared to the control.The internal bond of the strip-based panel was nearly four times higher than that of the control,while the particlebased panel exceeded it by a factor of two.The strip-based panel showed approximately 15% lower bending stiffness than the wood strand-based panel,yet it surpassed it in load-carrying capacity by 5%.In contrast,the particleboard showed significantly lower bending performance than the strip-based and control panels,despite particle processing being a more conventional method.Both cotton stalk-based panels exhibited higher water absorption and thickness swelling than the wood strand panel.Overall,cotton stalk-based panels—particularly those using strip processing—show promisingmechanical properties,suggesting potential applications in sheathing,furniture,and interior paneling.However,improvements in dimensional stability are needed for broader use.展开更多
To address the inefficient utilization of electrolytic manganese residue(EMR)caused by its high inert content,this study developed a multifunctional solid waste cementitious material by replacing 50-60%of ordinary Por...To address the inefficient utilization of electrolytic manganese residue(EMR)caused by its high inert content,this study developed a multifunctional solid waste cementitious material by replacing 50-60%of ordinary Portland cement(PO 42.5)with wet-ground electrolytic manganese residue(WEMR),wetground granulated blast-furnace slag(WGBFS),and carbide slag(CS).The mechanical properties,hydration characteristics,microstructure,and carbon emissions of the material were systematically investigated with varying WEMR dosages.The experimental results demonstrates that the wet-grinding process significantly refines the particle size and enhances the reactivity of both EMR and GBFS.As the WEMR dosage increases,the 28-day compressive strength initially rise and then declines.Optimal mechanical performance was achieved with 24%WEMR and 6%CS,yielding a 28-day compressive strength of 48.2 MPa.Advanced analytical techniques,including XRD,TG-DTG,SEM,and MIP,were employed to examine the hydration products.The findings reveal that the wet-grinding-alkali-sulfur synergistic activation system in the multi-solid waste cementitious material effectively utilize EMR to generate abundant hydration products such as AFt and C-(A)-S-H.Additionally,the fine particles of WEMR fill the pores in the mortar,further enhancing compressive strength.The cost and carbon emissions of this multifunctional system are only 65.97%and 46.9% of those of PO 42.5,respectively.This study provides a feasible approach for the efficient utilization of EMR,contributing to sustainable construction practices.展开更多
High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleim...High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constr...Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.展开更多
基金supported by the National Natural Science Foundation of China(11531009).
文摘In the paper,we introduce some of multipliers on residuated lattices and investigate the relations among them.First,basing on the properties of multipliers,we show that the set of all multiplicative multipliers on a residuated lattice A forms a residuated lattice which is isomorphic to A.Second,we prove that the set of all total multipliers on A is a Boolean subalgebra of the residuated lattice(which is constituted by all multiplicative multipliers on A)and is isomorphic to the Boolean center of A.Moreover,by partial multipliers,we study the maximal residuated lattices of quotients for residuated lattices.Finally,we focus on principal implicative multipliers on residuated lattices and obtain that the set of principal implicative multipliers on A is isomorphic to the set of all multiplicative multipliers on A under the opposite(dual)order.
基金Supported by the Higher School Research Foundation of Inner Mongolia(Grant No.NJZY18206).
文摘In this paper, the problem of bipolar fuzzy ideal is further studied in non-involutive residuated lattices. The notion of normal bipolar fuzzy ideal is introduced, some important properties and equivalent characterizations of normal bipolar fuzzy ideals are obtained. In addition, two special types of normal bipolar fuzzy ideals are defined, which are called maxima and completely normal bipolar fuzzy ideals, respectively, and their relationships are discussed.This work further expands the way for revealing the structural characteristics of non-involutive residuated lattices.
文摘This paper is devoted to the discussion of filters in residuated lattices. Some equivalent conditions about filter were given. The structure of generated filter was established. The concept of implicative filter in residuated lattice was proposed with its basic properties being discussed.
基金The National Natural Science Foundation of China (No60474022)
文摘Residuated lattice is an important non-classical logic algebra, and L-fuzzy rough set based on residuated lattice can describe the information with incompleteness, fuzziness and uncomparativity in information systems. In this paper, the representation theorems of L-fuzzy rough sets based on residuated lattice are given. The properties and axiomatic definition of the lower and upper approximarion operators in L-fuzzy rough sets are discussed.
文摘This paper is devoted to the discussion of filters in residuated lattices. The lattice structure of filters in residuated lattice was established. It is proved that the set of all filters forms a distributive lattice. Also, the concept of prime filter in residuated lattice was proposed and some equivalent conditions about prime filter were given.
文摘In this paper, we introduce the notion of L-topological spaces based on a complete bounded integral residuated lattice and discuss some properties of interior and left (right) closure operators.
基金supported by the National Foundation for Distionguished Young Scholars(Grant No:69725004)Rrsearch and Development Project of High-Technology(Grant No:863-306-ZT06-04-3)+1 种基金Foundation of Natural Sciences(Grant No:6982001) of ChinaFOK Ying-Tung Edu
文摘We use a semantical method of complete residuated lattice-valued logic to give a general- ization of fuzzy topology as a partial answer to a problem by Rosser and Turquette.
基金National Natural Science Foundation of China(52272075,52472053)Research Fund of Youth Innovation Promotion Association of CAS,China(2021190)Defense Industrial Technology Development Program(JCKY2021130B007)。
文摘Carbon fiber-reinforced carbon aerogel(C/CA)composites are one of the most promising candidates for applications requiring both thermal insulation and load bearing capabilities.The preparation of anti-oxidation coatings on C/CA to address its susceptibility to oxidation is a feasible approach to promote its application in oxidative environments.However,the currently reported coatings on C/CA mainly focus on improving the ablation performance and coating preparation process typically necessitating high-temperature heat treatment.This procedure can increase its thermal conductivity and reduce its thermal insulation ability.In this study,a series of ceramic-resin coatings were fabricated on C/CA through a simple slurry brushing-drying approach at room temperature.The effects of phenolic resin content on the coating structure,residual stress,thermal shock,and oxidation behaviors were investigated.Due to the adhesive properties and curing-induced shrinkage,the PR-7.5 coating(containing 7.5%(in mass)phenolic resin in the slurry)exhibits bonding strength close to fracture strength of the substrate and residual compressive stress of 0.853 GPa,which is beneficial for resisting thermal shock cracking.However,excessive resin content(PR-10.0 containing 10.0%(in mass)phenolic resin in the slurry)induces tensile stress due to uneven curing shrinkage,thereby leading to thermal shock cracking.Meanwhile,oxidation tests reveal significantly reduced weight losses for PR-7.5(17.46%at 800℃/100 min,8.15%at 1000℃/120 min,3.15%at 1200℃/120 min)versus uncoated C/CA’s 44.60%loss at 800℃/20 min.This work provides a brand-new and simple approach to improving the anti-oxidation performance of C/CA and expands its application in mild oxidative environments.
文摘We give two generalizations of Tarski’s fixpoint theorem in the setting of residuated lattices and use them to establish van Emdem-Kowalski’s least fixpoint semantics for residuated lattice-valued logic programs.
文摘We consider particular compatible orders on a given completely simple semi- group Sx= M（（x）; I, A; P） where （x） is an ordered cyclic group with x 〉 1 and p11= x-1. Of these, only the lexicographic and bootlace orders yield residuated semigroups. With the lexicographic order, Sx is orthodox and has a biggest idempotent. With the bootlace order, the maximal idempotents of Sx are identified by specific locations in the sandwich matrix. In the orthodox case there is also a biggest idempotent and, for sandwich matrices of a given size, uniqueness up to ordered semigroup isomorphism is established.
文摘Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.
文摘In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.
基金funded by Qinghai University Postgraduate Research and Practice Innovation Program of Funder,grant number 2025-GMKY-42.
文摘Recent advances in deep learning have significantly improved image deblurring;however,existing approaches still suffer from limited global context modeling,inadequate detail restoration,and poor texture or edge perception,especially under complex dynamic blur.To address these challenges,we propose the Multi-Resolution Fusion Network(MRFNet),a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion.The network employs a three-stage design:(1)TransformerBlocks capture long-range dependencies and reconstruct coarse global structures;(2)Nonlinear Activation Free Blocks(NAFBlocks)enhance local detail representation and mid-level feature fusion;and(3)an optimized residual subnetwork based on gated feature modulation refines texture and edge details for high-fidelity restoration.Extensive experiments demonstrate that MRFNet achieves superior performance compared to state-of-the-art methods.On GoPro,it attains 32.52 dB Peak Signal-to-Noise Ratio(PSNR)and 0.071 Learned Perceptual Image Patch Similarity(LPIPS),outperforming MIMOWNet(32.50 dB,0.075).On HIDE,it achieves 30.25 dB PSNR and 0.945 Structural Similarity Index Measure(SSIM),representing gains of+0.26 dB and+0.015 SSIM over MIMO-UNet(29.99 dB,0.930).On RealBlur-J,it reaches 28.82 dB PSNR and 0.872 SSIM,surpassing MIMO-UNet by+1.19 dB and+0.035 SSIM(27.63 dB,0.837).These results validate the effectiveness of the proposed progressive residual fusion and hybrid attention mechanisms in balancing global context understanding and local detail recovery for blind image deblurring.
基金funded by the Research Project of CNOOC(China)Co.,Ltd.Shanghai Branch,grant number 202417716474Research Project of CNOOC Limited,grant number KJZX-2024-0102.
文摘Weak water-drive offshore reservoirs with complex pore architecture and strong permeability heterogeneity present major challenges,including rapid depletion of formation energy,low waterflood efficiency,and significant lateral and vertical variability in crude oil properties,all of which contribute to limited recovery.To support more effective field development,alternative strategies and a deeper understanding of pore-scale flow behavior are urgently needed.In this work,CT imaging and digital image processing were used to construct a digital rock model representative of the target reservoir.A pore-scale flow model was then developed,and the Volume of Fluid(VOF)method was applied to simulate and optimize waterflooding schemes aimed at boosting oil recovery.Optimization focused on adjusting injection rates,varying the oil–water viscosity ratio,and implementing a water-alternating-gas(WAG)process.Results show that,for equal injection volumes,higher injection rates cause early water breakthrough through high-permeability pathways,yielding slower gains in recovery.Lowering the oil–water viscosity ratio improves mobility control,suppresses viscous fingering,enlarges sweep volume,and enhances recovery.When CH_(4)becomes fully miscible,it dissolves into the crude oil,lowering viscosity and eliminating interfacial tension,thereby providing greater displacement efficiency than partially miscible injection.Following a switch from water to gas injection,residual oil saturation decreases and becomes more uniformly distributed,indicating that the combined action of water and gas significantly improves both sweep efficiency and microscopic displacement.
基金sponsored by the National Natural Science Foundation of China(Grant No.52438002)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘This study investigates the influence of loading frequency on the fatigue behavior of ballastless track concrete for high-speed railways,aiming to support the development of concrete capable of withstanding higher operational speeds.Fatigue tests were conducted at loading frequencies ranging from 5 to 40 Hz,with a focus on fatigue life,damage evolution,energy dissipation,and residual fatigue strain in the concrete.The results indicate that between 5 and 15 Hz,the fatigue life and energy dissipation remain relatively stable,with minimal damage evolution and small residual strains.As the frequency increases to 15-20 Hz,the fatigue life and energy dissipation gradually decrease,while damage accumulation and residual strain increase.Beyond 20 Hz,both fatigue life and energy dissipation decrease rapidly,damage accumulation becomes more pronounced,and residual strain continues to rise.These phenomena are primarily attributed to the increased strain rate and load change rate at higher frequencies,which affect the microstructure evolution and lead to reduced fatigue performance.
基金supported by the intramural research program of the U.S.Department of Agriculture,National Institute of Food and Agriculture,Biobased Economy Through Biobased Products,under Award#2023-68016-40132.
文摘This study explores a novel method for processing cotton stalks—an abundant agricultural byproduct—into long strips that serve as sustainable raw material for engineered bio-based panels.To evaluate the effect of raw material morphology on panel’s performance,two types of cotton stalk-based panels were developed:one using long strips,maintaining fiber continuity,and the other using ground particles,representing conventional processing.A wood strand-based panel made from commercial southern yellow pine strands served as the control.All panels were bonded using phenol-formaldehyde resin and hot-pressed to a target thickness of 12.7 mm and density of 640 kg/m^(3).Their mechanical and physical properties were evaluated through internal bond,bending,thickness swelling,and water absorption tests.Both cotton stalk-based panels showed improved bonding performance compared to the control.The internal bond of the strip-based panel was nearly four times higher than that of the control,while the particlebased panel exceeded it by a factor of two.The strip-based panel showed approximately 15% lower bending stiffness than the wood strand-based panel,yet it surpassed it in load-carrying capacity by 5%.In contrast,the particleboard showed significantly lower bending performance than the strip-based and control panels,despite particle processing being a more conventional method.Both cotton stalk-based panels exhibited higher water absorption and thickness swelling than the wood strand panel.Overall,cotton stalk-based panels—particularly those using strip processing—show promisingmechanical properties,suggesting potential applications in sheathing,furniture,and interior paneling.However,improvements in dimensional stability are needed for broader use.
基金Funded by the Guangxi Key Research and Development Program(Nos.GK AB24010020,and GK AB23026071)the Key Project of Guangxi Natural Science Foundation(No.2025GXNSFDA090046)the Guangxi Science and Technology Base and Talent Special Project(No.GK AD24010062)。
文摘To address the inefficient utilization of electrolytic manganese residue(EMR)caused by its high inert content,this study developed a multifunctional solid waste cementitious material by replacing 50-60%of ordinary Portland cement(PO 42.5)with wet-ground electrolytic manganese residue(WEMR),wetground granulated blast-furnace slag(WGBFS),and carbide slag(CS).The mechanical properties,hydration characteristics,microstructure,and carbon emissions of the material were systematically investigated with varying WEMR dosages.The experimental results demonstrates that the wet-grinding process significantly refines the particle size and enhances the reactivity of both EMR and GBFS.As the WEMR dosage increases,the 28-day compressive strength initially rise and then declines.Optimal mechanical performance was achieved with 24%WEMR and 6%CS,yielding a 28-day compressive strength of 48.2 MPa.Advanced analytical techniques,including XRD,TG-DTG,SEM,and MIP,were employed to examine the hydration products.The findings reveal that the wet-grinding-alkali-sulfur synergistic activation system in the multi-solid waste cementitious material effectively utilize EMR to generate abundant hydration products such as AFt and C-(A)-S-H.Additionally,the fine particles of WEMR fill the pores in the mortar,further enhancing compressive strength.The cost and carbon emissions of this multifunctional system are only 65.97%and 46.9% of those of PO 42.5,respectively.This study provides a feasible approach for the efficient utilization of EMR,contributing to sustainable construction practices.
基金funded by the Henan Province Key R&D Program Project,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”,grant number 231111210400.
文摘High-resolution remote sensing imagery is essential for critical applications such as precision agriculture,urban management planning,and military reconnaissance.Although significant progress has been made in singleimage super-resolution(SISR)using generative adversarial networks(GANs),existing approaches still face challenges in recovering high-frequency details,effectively utilizing features,maintaining structural integrity,and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery.To address these limitations,this paper proposes the Improved ResidualModule and AttentionMechanism Network(IRMANet),a novel architecture specifically designed for remote sensing image reconstruction.IRMANet builds upon the Super-Resolution Generative Adversarial Network(SRGAN)framework and introduces several key innovations.First,the Enhanced Residual Unit(ERU)enhances feature reuse and stabilizes training through deep residual connections.Second,the Self-Attention Residual Block(SARB)incorporates a self-attentionmechanism into the Improved Residual Module(IRM)to effectivelymodel long-range dependencies and automatically emphasize salient features.Additionally,the IRM adopts amulti-scale feature fusion strategy to facilitate synergistic interactions between local detail and global semantic information.The effectiveness of each component is validated through ablation studies,while comprehensive comparative experiments on standard remote sensing datasets demonstrate that IRMANet significantly outperforms both the baseline and state-of-the-art methods in terms of perceptual quality and quantitative metrics.Specifically,compared to the baseline model,at a magnification factor of 2,IRMANet achieves an improvement of 0.24 dB in peak signal-to-noise ratio(PSNR)and 0.54 in structural similarity index(SSIM);at a magnification factor of 4,it achieves gains of 0.22 dB in PSNR and 0.51 in SSIM.These results confirm that the proposedmethod effectively enhances detail representation and structural reconstruction accuracy in complex remote sensing scenarios,offering robust technical support for high-precision detection and identification of both military and civilian aircraft.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
基金funded by Science and Technology Innovation Project grant No.ZZKY20222304.
文摘Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet,this paper proposes a novel lightweight neural network model called ResghostNet.This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks,which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations.Specifically,ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow,and designs a weight self-attention mechanism combined with SE blocks to enhance feature expression capabilities in cheap operations.Experimental results on the ImageNet dataset show that,compared to GhostNet,ResghostNet achieves higher accuracy while reducing the number of parameters by 52%.Although the computational complexity increases,by optimizing the usage strategy of GPU cachememory,themodel’s inference speed becomes faster.The ResghostNet is optimized in terms of classification accuracy and the number of model parameters,and shows great potential in edge computing devices.
