To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate tempor...Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.展开更多
This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features ...This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.展开更多
Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email...Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.展开更多
Feature modeling is the key to the realization of CAD/CAPP/CAM and the information integration of concurrent engineering. This paper describes the method for the advanced development of the parametric modeling system ...Feature modeling is the key to the realization of CAD/CAPP/CAM and the information integration of concurrent engineering. This paper describes the method for the advanced development of the parametric modeling system based on features by using I DEAS 5 system. It elaborates the modeling technique based on the features and generates the product information models based on the features providing abundant information for the process of the ensuing applications. The development of the feature modeling system on the commercial CAD software platform can take a great advantage of the solid modeling resources of the existing software, save the input of funds and shorten the development cycles of the new systems.展开更多
Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. In feature models＇ construction, one basic task is to ensure the consistency of feature models, which often ...Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. In feature models＇ construction, one basic task is to ensure the consistency of feature models, which often involves detecting and fixing of inconsistencies in feature models. While many approaches have been proposed, most of them focus on detecting inconsistencies rather than fixing inconsistencies. In this paper, we propose a novel dynamic-priority based approach to interactively fixing inconsistencies in feature models, and report an implementation of a system that not only automatically recommends a solution to fixing inconsistencies but also supports domain analysts to gradually reach the desirable solution by dynamically adjusting priorities of constraints. The key technical contribution is, as far as we are aware, the first application of the constraint hierarchy theory to feature modeling, where the degree of domain analysts＇ confidence on constraints is expressed by using priority and inconsistencies are resolved by deleting one or more lower-priority constraints. Two case studies demonstrate the usability and scalability （efficiency） of our new approach.展开更多
On the platform of UG general CAD system, a customized module dedicated to turbo-jet engine blade design is implemented to support the integration of CAD/CAE/CAM processes and multidisciplinary optimization of structu...On the platform of UG general CAD system, a customized module dedicated to turbo-jet engine blade design is implemented to support the integration of CAD/CAE/CAM processes and multidisciplinary optimization of structure design. An example is presented to illustrate the related techniques.展开更多
The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This p...The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This paper introduces a new method for modeling parts by using adaptability feature (AF), by which the consistent relationship among parts and assemblies can be maintained in whole design process. In addition, the design process, can be speeded, time-to-market shortened, and product quality improved. Some essential issues of the strategy are discussed. A system, KMCAD3D, by taking advantages of AF has been developed. It is shown that the method discussed is a feasible and effective way to improve current feature modeling technology.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segme...A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.展开更多
Use of features in order to achieve the integration of design and manufacture has been considered to be a key factor recent years. Features such as manufacturing properties form the workpiece. Features are structured ...Use of features in order to achieve the integration of design and manufacture has been considered to be a key factor recent years. Features such as manufacturing properties form the workpiece. Features are structured systematically through object oriented modeling. This article explains an object coding method developed for prismatic workpieces and the use of that method in process planning. Features have been determined and modeled as objects. Features have been coded according to their types and locations on the workpiece in this given method. Feature codings have been seen to be very advantageous in process planning.展开更多
In conformity with the principle of Design for Manufacture,feature-based design strate- (?)es have been developed.As the“feature”is relevant to the“macro process plan”and“macro NC programs”,obviously,“feature”...In conformity with the principle of Design for Manufacture,feature-based design strate- (?)es have been developed.As the“feature”is relevant to the“macro process plan”and“macro NC programs”,obviously,“feature”is beyond the power of conventional solid modellers.Neverthe- less,substantial breakthrough has not been made in the solid modeling field,except“feature at- taching”or“feature recognizing”methods have been taken on.In this paper,the theory, concepts,system architecture,and algorithm principles of solid modeling tool system have been represented.The practice of Feature Solid Modeling Tool System (FSMTS) developed at Huazhong University has proved that the tool may be a new foundation of Feature-Based Design.展开更多
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited recepti...Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.展开更多
Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such a...Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
The adaptability of features definition to applications is an essential condition for implementing feature based design. This paper makes attempt to present a hierarchical definition structure of features. The propos...The adaptability of features definition to applications is an essential condition for implementing feature based design. This paper makes attempt to present a hierarchical definition structure of features. The proposed scheme divides feature definition into application level, form level and geometric level, and provides links between different levels with feature semantics interpretation and enhanced geometric face adjacent graph. respectively. The results not only enable feature definition to abate from the specific dependence and become more extensive, but also provide a theoretical foundation for establishing the concurrent feature based design process model.展开更多
Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may preven...Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。展开更多
With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectiv...With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.展开更多
Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) a...Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.展开更多
Aiming at the axiom of design for manufacture (DFM), this paper describes a recognition method for abstracting compound features from a part model and discloses the basic mechanism of compounding, also builds the cor...Aiming at the axiom of design for manufacture (DFM), this paper describes a recognition method for abstracting compound features from a part model and discloses the basic mechanism of compounding, also builds the corresponding 2D-simulation model. The inner association between feature neighboring and feature compounding is deeply discussed and, based on the essential transforming rule of two neighboring features, the corresponding feature adjacency matrix (FAM) of multi - feature entities are generated. For the manufacturing feature converted from the pure design feature; an innovative concept-homogenous compounding is presented to clarify the architecture of machining domain. Then, the FAM recurrence elimination algorithm is developed to determine all the compound features, and according to machining sequence, outputs a group of machining domains.展开更多
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by National Key Research and Development Program of China(Key Techniques of Adaptive Grid Integration and Active Synchronization for Extremely High Penetration Distributed Photovoltaic Power Generation,2022YFB2402900).
文摘Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.
基金This work is supported by the National Natural Science Foundation of China(12002218)the Youth Foundation of Education Department of Liaoning Province(JYT19034).These supports are gratefully acknowledged.
文摘This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.
基金supported by the National Key Research and Development Program of China(No.2023YFB3105700).
文摘Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.
文摘Feature modeling is the key to the realization of CAD/CAPP/CAM and the information integration of concurrent engineering. This paper describes the method for the advanced development of the parametric modeling system based on features by using I DEAS 5 system. It elaborates the modeling technique based on the features and generates the product information models based on the features providing abundant information for the process of the ensuing applications. The development of the feature modeling system on the commercial CAD software platform can take a great advantage of the solid modeling resources of the existing software, save the input of funds and shorten the development cycles of the new systems.
基金supported by the National High Technology Research and Development 863 Program of China under Grant No.2013AA01A605the National Basic Research 973 Program of China under Grant No.2011CB302604+1 种基金the National Natural Science Foundation of China under Grant Nos.61121063,U1201252,61272163,61202071,and 60528006the Japan MEXT Grant-in-Aid for Scientific Research(A)under Grant No.25240009
文摘Feature models have been widely adopted to reuse the requirements of a set of similar products in a domain. In feature models＇ construction, one basic task is to ensure the consistency of feature models, which often involves detecting and fixing of inconsistencies in feature models. While many approaches have been proposed, most of them focus on detecting inconsistencies rather than fixing inconsistencies. In this paper, we propose a novel dynamic-priority based approach to interactively fixing inconsistencies in feature models, and report an implementation of a system that not only automatically recommends a solution to fixing inconsistencies but also supports domain analysts to gradually reach the desirable solution by dynamically adjusting priorities of constraints. The key technical contribution is, as far as we are aware, the first application of the constraint hierarchy theory to feature modeling, where the degree of domain analysts＇ confidence on constraints is expressed by using priority and inconsistencies are resolved by deleting one or more lower-priority constraints. Two case studies demonstrate the usability and scalability （efficiency） of our new approach.
基金Supported by the Aeronautical Science Foundation of China (04C51053)
文摘On the platform of UG general CAD system, a customized module dedicated to turbo-jet engine blade design is implemented to support the integration of CAD/CAE/CAM processes and multidisciplinary optimization of structure design. An example is presented to illustrate the related techniques.
文摘The current 3D CAD/CAM system, both research prototypes and commercial systems, based on traditional feature modeling are always hampered by the problems in their complicated modeling and difficult maintaining. This paper introduces a new method for modeling parts by using adaptability feature (AF), by which the consistent relationship among parts and assemblies can be maintained in whole design process. In addition, the design process, can be speeded, time-to-market shortened, and product quality improved. Some essential issues of the strategy are discussed. A system, KMCAD3D, by taking advantages of AF has been developed. It is shown that the method discussed is a feasible and effective way to improve current feature modeling technology.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
基金This project is supported by General Electric Company and National Advanced Technology Project of China(No.863-511-942-018).
文摘A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.
文摘Use of features in order to achieve the integration of design and manufacture has been considered to be a key factor recent years. Features such as manufacturing properties form the workpiece. Features are structured systematically through object oriented modeling. This article explains an object coding method developed for prismatic workpieces and the use of that method in process planning. Features have been determined and modeled as objects. Features have been coded according to their types and locations on the workpiece in this given method. Feature codings have been seen to be very advantageous in process planning.
文摘In conformity with the principle of Design for Manufacture,feature-based design strate- (?)es have been developed.As the“feature”is relevant to the“macro process plan”and“macro NC programs”,obviously,“feature”is beyond the power of conventional solid modellers.Neverthe- less,substantial breakthrough has not been made in the solid modeling field,except“feature at- taching”or“feature recognizing”methods have been taken on.In this paper,the theory, concepts,system architecture,and algorithm principles of solid modeling tool system have been represented.The practice of Feature Solid Modeling Tool System (FSMTS) developed at Huazhong University has proved that the tool may be a new foundation of Feature-Based Design.
基金supported by the National Natural Science Foundation of China(Nos.42371449,41801386).
文摘Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.
基金funded by the Guangzhou Development Zone Science and Technology Project(2023GH02)the University of Macao(MYRG2022-00271-FST)research grants by the Science and Technology Development Fund of Macao(0032/2022/A)and(0019/2025/RIB1).
文摘Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
文摘The adaptability of features definition to applications is an essential condition for implementing feature based design. This paper makes attempt to present a hierarchical definition structure of features. The proposed scheme divides feature definition into application level, form level and geometric level, and provides links between different levels with feature semantics interpretation and enhanced geometric face adjacent graph. respectively. The results not only enable feature definition to abate from the specific dependence and become more extensive, but also provide a theoretical foundation for establishing the concurrent feature based design process model.
文摘Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。
基金supported by the National Natural Science Foundation of China(62033008,61873143)。
文摘With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.
基金Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions,National Natural Science Foundation of China(No.41271438,41471316,41401440,41671389)
文摘Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.
文摘Aiming at the axiom of design for manufacture (DFM), this paper describes a recognition method for abstracting compound features from a part model and discloses the basic mechanism of compounding, also builds the corresponding 2D-simulation model. The inner association between feature neighboring and feature compounding is deeply discussed and, based on the essential transforming rule of two neighboring features, the corresponding feature adjacency matrix (FAM) of multi - feature entities are generated. For the manufacturing feature converted from the pure design feature; an innovative concept-homogenous compounding is presented to clarify the architecture of machining domain. Then, the FAM recurrence elimination algorithm is developed to determine all the compound features, and according to machining sequence, outputs a group of machining domains.
