Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper.The prior knowledge for the dynamic spectrum access is modeled and a reliability...The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper.The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process.Furthermore,a spectrum access scheme based on the prior knowledge enabled RL(PKRL)is designed,which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment.Compared with the existing methods,the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy.Simulation results show that,the convergence speed of the learning is improved by about 66%with the invariant average throughput.展开更多
The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient e...The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.展开更多
In some sample based regression tasks,the observed samples are quite few or not informative enough.As a result,the conflict between the number of samples and the model complexity emerges,and the regression method will...In some sample based regression tasks,the observed samples are quite few or not informative enough.As a result,the conflict between the number of samples and the model complexity emerges,and the regression method will confront the dilemma whether to choose a complex model or not.Incorporating the prior knowledge is a potential solution for this dilemma.In this paper,a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed.The proposed prior knowledge based kernel regression(PKBKR)method includes two subproblems:representing the prior knowledge in the function space,and combining this representation and the training samples to obtain the regression function.A greedy algorithm for the representing step and a weighted loss function for the incorporation step axe proposed.Finally,experiments are performed to validate the proposed PKBKR method,wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity,especially when the number of samples is small or the observation noise is large.展开更多
Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances.In this paper,we propose the metho...Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances.In this paper,we propose the methodology of designing better prior-knowledge-based hybrid methods by combining the existing ones. Then according to this methodology,two hybrid methods,interpolation-optimization (IO) method and interpolation penalty-function (IPF) method,are designed as examples.Finally,both methods are applied to modeling two cases in chemical engineering to investigate their effectiveness.Simulation results show that the performances of the hybrid methods are better than those of their parents.展开更多
The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macrosc...The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.展开更多
Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection.In this ...Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection.In this paper,we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image detection.The stage forward stagewise additive model of boosting is analyzed,and the approach how to combine it with the prior knowledge model is presented.The approach is then applied to landmark image detection,where the multi-features are boosted to solve a series of problems,such as rotation,noises affected,etc.Results of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.展开更多
The electro-chemo-mechanical mechanism is critical for understanding the initiation and propagation of lithium(Li)dendrites in solid-state lithium metal battery(SSLMB).Li dendrites often nucleate within surface defect...The electro-chemo-mechanical mechanism is critical for understanding the initiation and propagation of lithium(Li)dendrites in solid-state lithium metal battery(SSLMB).Li dendrites often nucleate within surface defects in the solid-state electrolyte,leading to internal short circuits that hinder practical application of SSLMB.While conventional experimental and finite element methods provide valuable insights,they are often costly,time-consuming,and inefficient for capturing the complicated stress evolution inside solid-state electrolyte.In this study,we propose a novel machine learning strategy that integrates prior knowledge and physics-informed constraints to predict the von Mises stress distribution induced by the internal defects of solid-state electrolyte.High-quality training datasets generated using a multiphysics simulation framework and key findings from previous studies were incorporated as physicsguided constraints to enhance prediction reliability and physical consistency of machine learning models.By employing a modified UNet architecture with squeeze-and-excitation module,it demonstrates remarkably high accuracy in stress prediction and exhibits excellent robustness and generalization across a wide range of defect scenarios.This model allows us to efficiently obtain the electro-chemo-mechanical failure of solid-state electrolyte,thereby guiding micro structural modifications and facilitating the design of SSLMB for practical applications.展开更多
Deep learning-based crop and weed detection is essential for modern precision weed control.But its effectiveness is limited when facing newly presented weed species due to the impracticality of collecting large,balanc...Deep learning-based crop and weed detection is essential for modern precision weed control.But its effectiveness is limited when facing newly presented weed species due to the impracticality of collecting large,balanced training datasets in field conditions.To address these challenges,this study presents a few-shot learning framework that achieves rapid and effective adaptation to new weed species by leveraging domain-specific characteristics of plant detection.We proposed few-shot enhanced attention(FSEA)network,built upon Faster R-CNN,which implements three prior knowledge in weed detection through:(1)designing a channel attention-based feature fusion module with an excess-green feature extractor to leverage color characteristics of plants and background,(2)designing a feature enhancement module to accommodate diverse plant morphol-ogies,and(3)applying an optimized loss function designed specifically for plant occlusion scenarios.Using commonly observed crop and weed species(common beet,sugarcane,barnyard grass,field pennycress and Chinese money plant)as base classes,FSEA achieved an all-class mAP of 0.416 and a novel-class mAP of 0.346 when adapting to less frequent weed species(common purslane,Asian copperleaf,goosefoot,clover,and goosegrass),after training for 40 epochs using only 30 samples per species.This performance significantly outperforms state-of-the-art few-shot detectors(TFA,FSCE,Meta R-CNN,Meta-DETR,DCFS,DiGEO)and traditional detector YOLOv7,indicating the effectiveness of incorporating domain-specific prior knowledge into few-shot weed detection.This study provides a fundamental methodology for rapid adaptation of weed detection systems to new environments and species,making automated weed management more practical and accessible for various agricultural applications.The source code and dataset are publicly available(m/skyofyao/FSEA)to facilitate further research in this domain.展开更多
Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time （NP） har...Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time （NP） hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose an algorithm called Constrained MCMC （C-MCMC） algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined： existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution （CPD） of edges and the probability distribution （PD） of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.展开更多
With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development ...With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.展开更多
Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking...Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic- based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowl- edge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.展开更多
Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection pr...Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments.The farmed tiger puffer detection model,called knowledge aggregation YOLO(KAYOLO),fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem.To alleviate feature loss caused by target blurring,we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer’s features and improve detection precision.To address missed detection caused by mutual occlusion in high-density farming environments,a prediction box aggregation method,aggregating prediction boxes of the same object,was proposed to reduce the influence among different objects to improve detection recall.To validate the effectiveness of the proposed methods,ablation experiments,model performance experiments,and model robustness experiments were designed.The experimental results showed that KAYOLO’s detection precision and recall results reached 94.92% and 92.21%,respectively.The two indices were improved by 1.29% and 1.35%,respectively,compared to those of YOLOv5.Compared with the recent state-of-the-art underwater object detection models,such as SWIPENet,RoIMix,FERNet,and SK-YOLOv5,KAYOLO achieved 2.09%,1.63%,1.13% and 0.85% higher precision and 1.2%,0.18%,1.74% and 0.39% higher recall,respectively.Experiments were conducted on different datasets to verify the model’s robustness,and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5.The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects.Additionally,the model had a strong generalization ability on different datasets,indicating that the model can be adapted to different environments,and it has strong robustness.展开更多
As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,en...As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,engineering complexity,and engineering practicability. Engineers often finish successful satellite designs by way of their plenty of experience and wisdom,lessons learnt from the past practices,as well as the assistance of the advanced computational techniques. Enlightened by the ripe patterns,th...展开更多
The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that th...The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.展开更多
The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adap...The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.展开更多
Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on se...Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.展开更多
Although it is convenient to exchange data by publishing view, but it may disclose sensitive information. The problem of how to eliminate information disclosure becomes a core problem in the view publishing process. I...Although it is convenient to exchange data by publishing view, but it may disclose sensitive information. The problem of how to eliminate information disclosure becomes a core problem in the view publishing process. In order to eliminate information disclosure, deciding view security algorithm and eliminating information disclosure algorithm are proposed, and the validity of the algorithms are proved by experiment. The experimental results showing, deciding view security algorithm can decide the safety of a set of views under prior knowledge, and eliminating information disclosure algorithm can eliminate disclosure efficiently.展开更多
A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to ...A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.展开更多
Optoelectronic materials are essential for today's scientific and technological development,and machine learning provides new ideas and tools for their research.In this paper,we first summarize the development his...Optoelectronic materials are essential for today's scientific and technological development,and machine learning provides new ideas and tools for their research.In this paper,we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices.Then,we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods.We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices,including the methods related to crystal structure,properties(defects,electronic structure)research,materials and devices optimization,material characterization,and process optimization.In summarizing the algorithms and feature representations used in different studies,it is noted that prior knowledge can improve optoelectronic materials design,research,and decision-making processes.Finally,the prospect of machine learning applications in optoelectronic materials is discussed,along with current challenges and future directions.This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by National Natural Science Foundation of China (No. 62131005)
文摘The spectrum access problem of cognitive users in the fast-changing dynamic interference spectrum environment is addressed in this paper.The prior knowledge for the dynamic spectrum access is modeled and a reliability quantification scheme is presented to guide the use of the prior knowledge in the learning process.Furthermore,a spectrum access scheme based on the prior knowledge enabled RL(PKRL)is designed,which effectively improved the learning efficiency and provided a solution for users to better adapt to the fast-changing and high-density electromagnetic environment.Compared with the existing methods,the proposed algorithm can adjust the access channel online according to historical information and improve the efficiency of the algorithm to obtain the optimal access policy.Simulation results show that,the convergence speed of the learning is improved by about 66%with the invariant average throughput.
基金supported by the Guangxi University of Science and Technology,Liuzhou,China,sponsored by the Researchers Supporting Project(No.XiaoKeBo21Z27,The Construction of Electronic Information Team Supported by Artificial Intelligence Theory and ThreeDimensional Visual Technology,Yuesheng Zhao)supported by the Key Laboratory for Space-based Integrated Information Systems 2022 Laboratory Funding Program(No.SpaceInfoNet20221120,Research on the Key Technologies of Intelligent Spatio-Temporal Data Engine Based on Space-Based Information Network,Yuesheng Zhao)supported by the 2023 Guangxi University Young and Middle-Aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0352,Research on the Recognition of Psychological Abnormalities in College Students Based on the Fusion of Pulse and EEG Techniques,Yutong Lu).
文摘The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.
基金Supported_by National Key Technologies Research and De-velopment Program of China during the 11th Five-year Plan(2006AA060206)Basic Research Foundation of Tsinghua Uni-versity(JC2007024)
文摘In some sample based regression tasks,the observed samples are quite few or not informative enough.As a result,the conflict between the number of samples and the model complexity emerges,and the regression method will confront the dilemma whether to choose a complex model or not.Incorporating the prior knowledge is a potential solution for this dilemma.In this paper,a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed.The proposed prior knowledge based kernel regression(PKBKR)method includes two subproblems:representing the prior knowledge in the function space,and combining this representation and the training samples to obtain the regression function.A greedy algorithm for the representing step and a weighted loss function for the incorporation step axe proposed.Finally,experiments are performed to validate the proposed PKBKR method,wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity,especially when the number of samples is small or the observation noise is large.
基金Supported by the National Natural Science Foundation of China (No. 20076041)
文摘Three-layer feedforward networks have been widely used in modeling chemical engineering processes and prior-knowledge-based methods have been introduced to improve their performances.In this paper,we propose the methodology of designing better prior-knowledge-based hybrid methods by combining the existing ones. Then according to this methodology,two hybrid methods,interpolation-optimization (IO) method and interpolation penalty-function (IPF) method,are designed as examples.Finally,both methods are applied to modeling two cases in chemical engineering to investigate their effectiveness.Simulation results show that the performances of the hybrid methods are better than those of their parents.
基金Project supported by the National Natural Science Foundation of China (Grant No.12172226)。
文摘The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.
基金Project (No. 2006AA10Z204) supported by the National Hi-Tech Research and Development Program (863) of China
文摘Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection.In this paper,we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image detection.The stage forward stagewise additive model of boosting is analyzed,and the approach how to combine it with the prior knowledge model is presented.The approach is then applied to landmark image detection,where the multi-features are boosted to solve a series of problems,such as rotation,noises affected,etc.Results of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.
基金financially supported by the National Natural Science Foundation of China(22479067)Yunnan Young Talents Program for“Xingdian Talent Support Plan”(KKXX202551007)。
文摘The electro-chemo-mechanical mechanism is critical for understanding the initiation and propagation of lithium(Li)dendrites in solid-state lithium metal battery(SSLMB).Li dendrites often nucleate within surface defects in the solid-state electrolyte,leading to internal short circuits that hinder practical application of SSLMB.While conventional experimental and finite element methods provide valuable insights,they are often costly,time-consuming,and inefficient for capturing the complicated stress evolution inside solid-state electrolyte.In this study,we propose a novel machine learning strategy that integrates prior knowledge and physics-informed constraints to predict the von Mises stress distribution induced by the internal defects of solid-state electrolyte.High-quality training datasets generated using a multiphysics simulation framework and key findings from previous studies were incorporated as physicsguided constraints to enhance prediction reliability and physical consistency of machine learning models.By employing a modified UNet architecture with squeeze-and-excitation module,it demonstrates remarkably high accuracy in stress prediction and exhibits excellent robustness and generalization across a wide range of defect scenarios.This model allows us to efficiently obtain the electro-chemo-mechanical failure of solid-state electrolyte,thereby guiding micro structural modifications and facilitating the design of SSLMB for practical applications.
基金This project was funded by National Natural Science Foundation of China,China(Award No.:U23A20330)Specific Research Project of Guangxi for Research Bases and Talents,China(Award No.:AD22035919)This research is also a product of the Modern Industry School of Subtropical Intelligent Agricultural Machinery and Equipment,Guangxi University,China(Project No.T3010097930).
文摘Deep learning-based crop and weed detection is essential for modern precision weed control.But its effectiveness is limited when facing newly presented weed species due to the impracticality of collecting large,balanced training datasets in field conditions.To address these challenges,this study presents a few-shot learning framework that achieves rapid and effective adaptation to new weed species by leveraging domain-specific characteristics of plant detection.We proposed few-shot enhanced attention(FSEA)network,built upon Faster R-CNN,which implements three prior knowledge in weed detection through:(1)designing a channel attention-based feature fusion module with an excess-green feature extractor to leverage color characteristics of plants and background,(2)designing a feature enhancement module to accommodate diverse plant morphol-ogies,and(3)applying an optimized loss function designed specifically for plant occlusion scenarios.Using commonly observed crop and weed species(common beet,sugarcane,barnyard grass,field pennycress and Chinese money plant)as base classes,FSEA achieved an all-class mAP of 0.416 and a novel-class mAP of 0.346 when adapting to less frequent weed species(common purslane,Asian copperleaf,goosefoot,clover,and goosegrass),after training for 40 epochs using only 30 samples per species.This performance significantly outperforms state-of-the-art few-shot detectors(TFA,FSCE,Meta R-CNN,Meta-DETR,DCFS,DiGEO)and traditional detector YOLOv7,indicating the effectiveness of incorporating domain-specific prior knowledge into few-shot weed detection.This study provides a fundamental methodology for rapid adaptation of weed detection systems to new environments and species,making automated weed management more practical and accessible for various agricultural applications.The source code and dataset are publicly available(m/skyofyao/FSEA)to facilitate further research in this domain.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 61372171 and the National Key Technology Research and Development Program of China under Grant No. 2012BAH23B03. Acknowledgement We thank anonymous reviewers for their constructive and valuable comments. We also thank Professor Jian-Feng Zhan at Institute of Computing Technology, Chinese Academy of Sciences, Beijing, for his technical suggestions on this paper.
文摘Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time （NP） hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it di？cult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo （MCMC） algorithm and propose an algorithm called Constrained MCMC （C-MCMC） algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined： existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution （CPD） of edges and the probability distribution （PD） of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFF0600400)the National Natural Science Foundation of China(Grant Nos.72104123,72004113)。
文摘With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.
文摘Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic- based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowl- edge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.
基金supported by the Key Projects of Educational Department of Liaoning Province(LJKZ0729)National Natural Science Foundation of China(31972846)+1 种基金Liaoning Province Natural Science Foundation(2020-KF-12-09)Foundation of Educational Department of Liaoning Province(LJKZ0730).
文摘Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments.The farmed tiger puffer detection model,called knowledge aggregation YOLO(KAYOLO),fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem.To alleviate feature loss caused by target blurring,we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer’s features and improve detection precision.To address missed detection caused by mutual occlusion in high-density farming environments,a prediction box aggregation method,aggregating prediction boxes of the same object,was proposed to reduce the influence among different objects to improve detection recall.To validate the effectiveness of the proposed methods,ablation experiments,model performance experiments,and model robustness experiments were designed.The experimental results showed that KAYOLO’s detection precision and recall results reached 94.92% and 92.21%,respectively.The two indices were improved by 1.29% and 1.35%,respectively,compared to those of YOLOv5.Compared with the recent state-of-the-art underwater object detection models,such as SWIPENet,RoIMix,FERNet,and SK-YOLOv5,KAYOLO achieved 2.09%,1.63%,1.13% and 0.85% higher precision and 1.2%,0.18%,1.74% and 0.39% higher recall,respectively.Experiments were conducted on different datasets to verify the model’s robustness,and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5.The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects.Additionally,the model had a strong generalization ability on different datasets,indicating that the model can be adapted to different environments,and it has strong robustness.
基金National Natural Science Foundation of China (50575031, 50275019)National High-tech Research and Development Program (2006AA04Z109)
文摘As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,engineering complexity,and engineering practicability. Engineers often finish successful satellite designs by way of their plenty of experience and wisdom,lessons learnt from the past practices,as well as the assistance of the advanced computational techniques. Enlightened by the ripe patterns,th...
基金This work was supported by the National Key R&D Funding of China(No.2018YFB1403702)the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars(No.LR22F030003).
文摘The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.
基金supported by the National Natural Science Foundation of China(61371181)the Shandong Provincial Natural Science Foundation(ZR2012FQ007)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(HIT.NSRIF.2011118)
文摘The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.
基金funding partly by the National Natural Science Foundation of China under grant number 61701179.
文摘Sarcasm detection in Natural Language Processing(NLP)has become increasingly important,partic-ularly with the rise of social media and non-textual emotional expressions,such as images.Existing methods often rely on separate image and text modalities,which may not fully utilize the information available from both sources.To address this limitation,we propose a novel multimodal large model,i.e.,the PKME-MLM(Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection).The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images,which is then combined with text data for deeper analysis.This method improves the integration of image and text data,addressing the limitation of previous models that process these modalities separately.Additionally,we incorporate multi-label sentiment analysis,refining sentiment labels to improve sarcasm recognition accuracy.This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem,thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text.Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks,with an accuracy(Acc.)of 94.35%,and Macro-Average Precision and Recall reaching 93.92%and 94.21%,respectively.These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research.This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.
基金Supported bythe Key Project of Ministry of Educationof China(205014)
文摘Although it is convenient to exchange data by publishing view, but it may disclose sensitive information. The problem of how to eliminate information disclosure becomes a core problem in the view publishing process. In order to eliminate information disclosure, deciding view security algorithm and eliminating information disclosure algorithm are proposed, and the validity of the algorithms are proved by experiment. The experimental results showing, deciding view security algorithm can decide the safety of a set of views under prior knowledge, and eliminating information disclosure algorithm can eliminate disclosure efficiently.
文摘A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.
基金Project supported by the National Natural Science Foundation of China (Grant No.61601198)the University of Jinan PhD Foundation (Grant No.XBS1714)。
文摘Optoelectronic materials are essential for today's scientific and technological development,and machine learning provides new ideas and tools for their research.In this paper,we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices.Then,we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods.We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices,including the methods related to crystal structure,properties(defects,electronic structure)research,materials and devices optimization,material characterization,and process optimization.In summarizing the algorithms and feature representations used in different studies,it is noted that prior knowledge can improve optoelectronic materials design,research,and decision-making processes.Finally,the prospect of machine learning applications in optoelectronic materials is discussed,along with current challenges and future directions.This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.
