The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO_(2)-enhanced o...The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO_(2)-enhanced oil recovery,foam flooding,and unconventional reservoir development.Current characterization methods remain constrained by labor-intensive manual workflows and limited dynamic analysis capabilities,particularly for processing large-scale microscopy data and video sequences that capture critical transient behavior like gas cluster migration and droplet coalescence.These limitations hinder the establishment of robust correlations between pore-scale flow patterns and reservoir-scale production performance.This study introduces a novel computer vision framework that integrates foundation models with lightweight neural networks to address these industry challenges.Leveraging the segment anything model's zero-shot learning capability,we developed an automated workflow that achieves an efficiency improvement of approximately 29 times in bubble labeling compared to manual methods while maintaining less than 2%deviation from expert annotations.Engineering-oriented optimization ensures lightweight deployment with 94%segmentation accuracy,while the integrated quantification system precisely resolves gas saturation,shape factors,and interfacial dynamics,parameters critical for optimizing gas injection strategies and predicting phase redistribution patterns.Validated through microfluidic gas-liquid displacement experiments for discontinuous phase segmentation accuracy,this methodology enables precise bubble morphology quantification with broad application potential in multiphase systems,including emulsion droplet dynamics characterization and particle transport behavior analysis.This work bridges the critical gap between pore-scale dynamics characterization and reservoir-scale simulation requirements,providing a foundational framework for intelligent flow diagnostics and predictive modeling in next-generation digital oilfield systems.展开更多
Applications of multifractal analysis to white matter structure changes on magnetic resonance imaging（MRI） have recently received increasing attentions. Although some progresses have been made, there is no evident s...Applications of multifractal analysis to white matter structure changes on magnetic resonance imaging（MRI） have recently received increasing attentions. Although some progresses have been made, there is no evident study on applying multifractal analysis to evaluate the white matter structural changes on MRI for Alzheimer＇s disease（AD） research. In this paper, to explore multifractal analysis of white matter structural changes on 3D MRI volumes between normal aging and early AD, we not only extend the traditional box-counting multifractal analysis（BCMA） into the 3D case, but also propose a modified integer ratio based BCMA（IRBCMA） algorithm to compensate for the rigid division rule in BCMA. We verify multifractal characteristics in 3D white matter MRI volumes. In addition to the previously well studied multifractal feature,△α, we also demonstrated △ f as an alternative and effective multifractal feature to distinguish NC from AD subjects.Both △α and △ f are found to have strong positive correlation with the clinical MMSE scores with statistical significance.Moreover, the proposed IRBCMA can be an alternative and more accurate algorithm for 3D volume analysis. Our findings highlight the potential usefulness of multifractal analysis, which may contribute to clarify some aspects of the etiology of AD through detection of structural changes in white matter.展开更多
The constant winding tension can make the filament arranged in order. The stress distribution between the filament balance fully gives play to the enhancement of filament, and increases the intensive workload of the c...The constant winding tension can make the filament arranged in order. The stress distribution between the filament balance fully gives play to the enhancement of filament, and increases the intensive workload of the composite winding material. This paper conducts the mechanical analysis for the unwinding roller and tension measuring roller of the cylindrical winding machine so that gets the mechanical model, gives error compensation formula caused by the radius change of the yarn group in the unwinding side, designs the closed-loop control system and utilizes the dynamical- integral PID control strategy to achieve the tension control during the process of the cylindrical winding.展开更多
Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune...Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune the performance of database systems with a little help from the database servers. In this paper, we propose a new technique for automated performance tuning of data management systems. Firstly, we show how to use the periods of low workload time for performance improvements in the periods of high workload time. We demonstrate that extensions of a database system with materialised views and indices when a workload is low may contribute to better performance for a successive period of high workload. The paper proposes several online algorithms for continuous processing of estimated database workloads and for the discovery of the best plan for materialised view and index database extensions and of elimination of the extensions that are no longer needed. We present the results of experiments that show how the proposed automated performance tuning technique improves the overall performance of a data management system. 展开更多
This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations....This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.展开更多
To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak f...To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processe...Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.展开更多
This study used the marginal likelihood and Bayesian posterior model probability for evaluation of model complexity in order to avoid using over-complex models for numerical simulations. It focused on investigation of...This study used the marginal likelihood and Bayesian posterior model probability for evaluation of model complexity in order to avoid using over-complex models for numerical simulations. It focused on investigation of the impacts of prior parameter distributions(involved in calculating the marginal likelihood) on the evaluation of model complexity. We argue that prior parameter distributions should define the parameter space in which numerical simulations are made. New perspectives on the prior parameter distribution and posterior model probability were demonstrated in an example of groundwater solute transport modeling with four models, each simulating four column experiments. The models had different levels of complexity in terms of their model structures and numbers of calibrated parameters. The posterior model probability was evaluated for four cases with different prior parameter distributions. While the distributions substantially impacted model ranking, the model ranking in each case was reasonable for the specific circumstances in which numerical simulations were made. For evaluation of model complexity, it is thus necessary to determine the parameter spaces for modeling, which can be done by conducting numerical simulation and usineg engineering judgment based on understanding of the system being studied.展开更多
As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of ...As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.展开更多
With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color image...With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.展开更多
The application of deep learning in the field of object detection has experienced much progress.However,due to the domain shift problem,applying an off-the-shelf detector to another domain leads to a significant perfo...The application of deep learning in the field of object detection has experienced much progress.However,due to the domain shift problem,applying an off-the-shelf detector to another domain leads to a significant performance drop.A large number of ground truth labels are required when using another domain to train models,demanding a large amount of human and financial resources.In order to avoid excessive resource requirements and performance drop caused by domain shift,this paper proposes a new domain adaptive approach to cross-domain vehicle detection.Our approach improves the cross-domain vehicle detection model from image space and feature space.We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space.For feature space,we align feature distributions between the source domain and the target domain to improve the detection accuracy.Experiments are carried out using the method with two different datasets,proving that this technique effectively improves the accuracy of vehicle detection in the target domain.展开更多
In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sa...In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sahai and Wolf （LRSW） assumption, this scheme is proved secure in a random oracle model, and it requires only three pairing operations for verifying n signatures from a multi-signer setting.展开更多
In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a p...In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.展开更多
N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computati...N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.展开更多
This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Th...This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.展开更多
Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolut...Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks,Gated Recurrent Units,and a self-attention mechanism.Based on standardized data cleaning and normalization,the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately.The self-attention mechanism is then applied to assign weights to key time steps,after which the two feature streams are flattened and concatenated.Finally,a fully connected layer is used to generate the forecast.Under a training setup with mean squared error as the loss function and an adaptive optimization strategy,the proposed model consistently outperforms baseline methods across multiple error and fitting metrics,demonstrating stronger generalization capability and interpretability.The paper also provides a complete data processing and evaluation workflow,ensuring strong reproducibility and practical applicability.展开更多
Understanding the flow mechanisms between hydrocarbons and interfaces in nanopores is critical for fluid supply in tight reservoirs with huge reserves.In this paper,the nanoscale liquid-solid interface interaction pot...Understanding the flow mechanisms between hydrocarbons and interfaces in nanopores is critical for fluid supply in tight reservoirs with huge reserves.In this paper,the nanoscale liquid-solid interface interaction potential is analyzed based on the molecular interface theory,and a new nanoscale fluid viscosity model is constructed through the Eyring model,and the fluid velocity and flow flux models in nanopores are derived based on the liquid-solid interface slip condition.In addition,n-pentane flow characteristics in quartz nanopores were investigated with key parameters including:the Hamaker constant,the decay length,the wetting angle,the boundary slip and the flux coefficient.The proposed model is validated in a comparison of theory,simulation and laboratory results.The study results show:(1)influenced by the liquid-solid interfacial effect,there is a viscosity gap between the fluid in the bulk and at the boundary,resulting in a non-linear variation of the flow velocity.Of the multiple microscopic forces considered by the model,Ligshitz-Van der Waals force has the strongest effect in confined pores below 40 nm,and electrostatic force has the weakest effect.When the pore diameter less than 10 nm,the constrained fluid viscosity was improved above 4 times.(2)based on the microscopic liquid-solid interface slip condition,a constrained space velocity model is derived,which indicates that the flow is directly dependent on the effective shear stresses on the fluid and the strength of the liquid-solid interface effect.Under the low shear stress in a tight reservoir,the slip at the liquid-solid interface has obvious linear characteristics,and the slip velocity depends on the effective shear stress.The liquid-solid interfacial effect parameter is increased from 1 to 30,and the slip velocity is reduced to 3.2Å/ps,which is a 55%reduction.(3)in this paper,the hamaker constant of n-pentane-quartz interface based on the molecular spacing variation and the decay constant for different water types and solute concentrations are obtained,and the effect of the decay length on the flow coefficient of the nano confined flow model is explored for different pore radiuses.The flux coefficient increases with pore radius,and the effect of the decay length is greater for pores<100 nm.展开更多
Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on loc...Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.展开更多
基金supported by Sichuan Province Outstanding Young Scientist Fund(Grant No.2025NSFJQ0009)Sichuan Regional Innovation Cooperation Fund(Grant No.2025YFHZ0270)。
文摘The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO_(2)-enhanced oil recovery,foam flooding,and unconventional reservoir development.Current characterization methods remain constrained by labor-intensive manual workflows and limited dynamic analysis capabilities,particularly for processing large-scale microscopy data and video sequences that capture critical transient behavior like gas cluster migration and droplet coalescence.These limitations hinder the establishment of robust correlations between pore-scale flow patterns and reservoir-scale production performance.This study introduces a novel computer vision framework that integrates foundation models with lightweight neural networks to address these industry challenges.Leveraging the segment anything model's zero-shot learning capability,we developed an automated workflow that achieves an efficiency improvement of approximately 29 times in bubble labeling compared to manual methods while maintaining less than 2%deviation from expert annotations.Engineering-oriented optimization ensures lightweight deployment with 94%segmentation accuracy,while the integrated quantification system precisely resolves gas saturation,shape factors,and interfacial dynamics,parameters critical for optimizing gas injection strategies and predicting phase redistribution patterns.Validated through microfluidic gas-liquid displacement experiments for discontinuous phase segmentation accuracy,this methodology enables precise bubble morphology quantification with broad application potential in multiphase systems,including emulsion droplet dynamics characterization and particle transport behavior analysis.This work bridges the critical gap between pore-scale dynamics characterization and reservoir-scale simulation requirements,providing a foundational framework for intelligent flow diagnostics and predictive modeling in next-generation digital oilfield systems.
基金Project supported by the National Natural Science Foundation of China(Grant No.61271079)the Vice Chancellor Research Grant in University of Wollongongthe Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Applications of multifractal analysis to white matter structure changes on magnetic resonance imaging（MRI） have recently received increasing attentions. Although some progresses have been made, there is no evident study on applying multifractal analysis to evaluate the white matter structural changes on MRI for Alzheimer＇s disease（AD） research. In this paper, to explore multifractal analysis of white matter structural changes on 3D MRI volumes between normal aging and early AD, we not only extend the traditional box-counting multifractal analysis（BCMA） into the 3D case, but also propose a modified integer ratio based BCMA（IRBCMA） algorithm to compensate for the rigid division rule in BCMA. We verify multifractal characteristics in 3D white matter MRI volumes. In addition to the previously well studied multifractal feature,△α, we also demonstrated △ f as an alternative and effective multifractal feature to distinguish NC from AD subjects.Both △α and △ f are found to have strong positive correlation with the clinical MMSE scores with statistical significance.Moreover, the proposed IRBCMA can be an alternative and more accurate algorithm for 3D volume analysis. Our findings highlight the potential usefulness of multifractal analysis, which may contribute to clarify some aspects of the etiology of AD through detection of structural changes in white matter.
文摘The constant winding tension can make the filament arranged in order. The stress distribution between the filament balance fully gives play to the enhancement of filament, and increases the intensive workload of the composite winding material. This paper conducts the mechanical analysis for the unwinding roller and tension measuring roller of the cylindrical winding machine so that gets the mechanical model, gives error compensation formula caused by the radius change of the yarn group in the unwinding side, designs the closed-loop control system and utilizes the dynamical- integral PID control strategy to achieve the tension control during the process of the cylindrical winding.
文摘Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune the performance of database systems with a little help from the database servers. In this paper, we propose a new technique for automated performance tuning of data management systems. Firstly, we show how to use the periods of low workload time for performance improvements in the periods of high workload time. We demonstrate that extensions of a database system with materialised views and indices when a workload is low may contribute to better performance for a successive period of high workload. The paper proposes several online algorithms for continuous processing of estimated database workloads and for the discovery of the best plan for materialised view and index database extensions and of elimination of the extensions that are no longer needed. We present the results of experiments that show how the proposed automated performance tuning technique improves the overall performance of a data management system.
基金supported by Guangdong Province Rural Science and Technology Commissioner Project,Zen Tea Reliable Traceability and Intelligent Planting Key Technology Research and Development,Promotion and Application(KTP20210199)Special Project of Guangdong Provincial Education Department,Research on Abnormal Behavior Recognition Technology of Pregnant Sows Based onGraph Convolution(2021ZDZX1091)+2 种基金Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515110729Shenzhen Science and Technology Program under Grant 20231128093642002the Research Foundation of Shenzhen Polytechnic University under Grant 6023312007K.
文摘This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.
基金supported in part by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025)the Graduate Science and Technology Innovation Project of University of Science and Technology Liaoning(LKDYC202310).
文摘To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
文摘Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.
基金supported by the U.S.Department of Energy Early Career Research Program Award(Grant No.DE-SC0008272)U.S.National Science Foundation(Grant No.1552329)
文摘This study used the marginal likelihood and Bayesian posterior model probability for evaluation of model complexity in order to avoid using over-complex models for numerical simulations. It focused on investigation of the impacts of prior parameter distributions(involved in calculating the marginal likelihood) on the evaluation of model complexity. We argue that prior parameter distributions should define the parameter space in which numerical simulations are made. New perspectives on the prior parameter distribution and posterior model probability were demonstrated in an example of groundwater solute transport modeling with four models, each simulating four column experiments. The models had different levels of complexity in terms of their model structures and numbers of calibrated parameters. The posterior model probability was evaluated for four cases with different prior parameter distributions. While the distributions substantially impacted model ranking, the model ranking in each case was reasonable for the specific circumstances in which numerical simulations were made. For evaluation of model complexity, it is thus necessary to determine the parameter spaces for modeling, which can be done by conducting numerical simulation and usineg engineering judgment based on understanding of the system being studied.
基金This work was funded by the foundation of Liaoning Educational committee under the Grant No.2019LNJC03.
文摘As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 71571091,71771112the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds under Grant PAL-N201801the Excellent Talent Training Project of University of Science and Technology Liaoning under Grant 2019RC05.
文摘With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.
文摘The application of deep learning in the field of object detection has experienced much progress.However,due to the domain shift problem,applying an off-the-shelf detector to another domain leads to a significant performance drop.A large number of ground truth labels are required when using another domain to train models,demanding a large amount of human and financial resources.In order to avoid excessive resource requirements and performance drop caused by domain shift,this paper proposes a new domain adaptive approach to cross-domain vehicle detection.Our approach improves the cross-domain vehicle detection model from image space and feature space.We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space.For feature space,we align feature distributions between the source domain and the target domain to improve the detection accuracy.Experiments are carried out using the method with two different datasets,proving that this technique effectively improves the accuracy of vehicle detection in the target domain.
基金Supported by the National Natural Science Foundation of China(61072080)the Foundation of Education Bureau of Fujian Province (JK2010012)Key Project of Services for Haixia Construction in Universities of Fujian Province
文摘In this paper, we propose a method to construct an online/offiine batch verification signature scheme in a multi-signer setting. The length of the scheme is approximately 480 bits. Based on the Lysyanskaya, Rivest, Sahai and Wolf （LRSW） assumption, this scheme is proved secure in a random oracle model, and it requires only three pairing operations for verifying n signatures from a multi-signer setting.
基金This research was funded by Shenzhen Science and Technology Program(Grant No.RCBS20221008093121051)the General Higher Education Project of Guangdong Provincial Education Department(Grant No.2020ZDZX3085)+1 种基金China Postdoctoral Science Foundation(Grant No.2021M703371)the Post-Doctoral Foundation Project of Shenzhen Polytechnic(Grant No.6021330002K).
文摘In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.
基金supported by the National Natural Science Foundation of China(Nos.62071079 and 61803065).
文摘N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.
基金supported by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025).
文摘This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.
基金supported in part by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025)the Innovation and Entrepreneurship Training Program for College students of University of Science and Technology Liaoning(S202510146005).
文摘Power load forecasting load forecasting is a core task in power system scheduling,operation,and planning.To enhance forecasting performance,this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks,Gated Recurrent Units,and a self-attention mechanism.Based on standardized data cleaning and normalization,the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately.The self-attention mechanism is then applied to assign weights to key time steps,after which the two feature streams are flattened and concatenated.Finally,a fully connected layer is used to generate the forecast.Under a training setup with mean squared error as the loss function and an adaptive optimization strategy,the proposed model consistently outperforms baseline methods across multiple error and fitting metrics,demonstrating stronger generalization capability and interpretability.The paper also provides a complete data processing and evaluation workflow,ensuring strong reproducibility and practical applicability.
基金supported by the Key Program of the National Natural Science Foundation of China[No.U24B200683]Natural Science Foundation of Sichuan Provincial,China[No.2024NSFSC2012].
文摘Understanding the flow mechanisms between hydrocarbons and interfaces in nanopores is critical for fluid supply in tight reservoirs with huge reserves.In this paper,the nanoscale liquid-solid interface interaction potential is analyzed based on the molecular interface theory,and a new nanoscale fluid viscosity model is constructed through the Eyring model,and the fluid velocity and flow flux models in nanopores are derived based on the liquid-solid interface slip condition.In addition,n-pentane flow characteristics in quartz nanopores were investigated with key parameters including:the Hamaker constant,the decay length,the wetting angle,the boundary slip and the flux coefficient.The proposed model is validated in a comparison of theory,simulation and laboratory results.The study results show:(1)influenced by the liquid-solid interfacial effect,there is a viscosity gap between the fluid in the bulk and at the boundary,resulting in a non-linear variation of the flow velocity.Of the multiple microscopic forces considered by the model,Ligshitz-Van der Waals force has the strongest effect in confined pores below 40 nm,and electrostatic force has the weakest effect.When the pore diameter less than 10 nm,the constrained fluid viscosity was improved above 4 times.(2)based on the microscopic liquid-solid interface slip condition,a constrained space velocity model is derived,which indicates that the flow is directly dependent on the effective shear stresses on the fluid and the strength of the liquid-solid interface effect.Under the low shear stress in a tight reservoir,the slip at the liquid-solid interface has obvious linear characteristics,and the slip velocity depends on the effective shear stress.The liquid-solid interfacial effect parameter is increased from 1 to 30,and the slip velocity is reduced to 3.2Å/ps,which is a 55%reduction.(3)in this paper,the hamaker constant of n-pentane-quartz interface based on the molecular spacing variation and the decay constant for different water types and solute concentrations are obtained,and the effect of the decay length on the flow coefficient of the nano confined flow model is explored for different pore radiuses.The flux coefficient increases with pore radius,and the effect of the decay length is greater for pores<100 nm.
基金supported by the National Natural Science Foundation of China(Grant No.62173065)the Natural Science Foundation of Beijing(Grant No.4242040)+1 种基金the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2025(Grant No.ZHKFYB2503)the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2024(Grant No.ZHKFZD2401).
文摘Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.
