Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
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
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade compone...Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade components.In this paper,a dynamic model of 3D 4-directional braided composite thin plates considering braiding directions is established.Based on Kirchhoff's plate assumptions,the displacement variables of the plate are expressed.By incorporating the braiding directions into the constitutive equation of the braided composites,the dynamic model of the plate considering braiding directions is obtained.The effects of the speeds,braiding directions,and braided angles on the responses of the plate with fixed-axis rotation and translational motion,respectively,are investigated.This paper presents a dynamic theory for calculating the deformation of 3D braided composite structures undergoing both translational and rotational motions.It also provides a simulation method for investigating the dynamic behavior of non-isotropic material plates in various applications.展开更多
Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certai...Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certain models,they do not invariably guarantee the extraction of the most critical or impactful features.Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features.However,the challenge of discerning the most relevant and influential features persists,particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial intelligence(AI)applications.In response,this study introduces an innovative,automated statistical method termed Farea Similarity for Feature Selection(FSFS).The FSFS approach computes a similarity metric for each feature by benchmarking it against the record-wise mean,thereby finding feature dependencies and mitigating the influence of outliers that could potentially distort evaluation outcomes.Features are subsequently ranked according to their similarity scores,with the threshold established at the average similarity score.Notably,lower FSFS values indicate higher similarity and stronger data correlations,whereas higher values suggest lower similarity.The FSFS method is designed not only to yield reliable evaluation metrics but also to reduce data complexity without compromising model performance.Comparative analyses were performed against several established techniques,including Chi-squared(CS),Correlation Coefficient(CC),Genetic Algorithm(GA),Exhaustive Approach,Greedy Stepwise Approach,Gain Ratio,and Filtered Subset Eval,using a variety of datasets such as the Experimental Dataset,Breast Cancer Wisconsin(Original),KDD CUP 1999,NSL-KDD,UNSW-NB15,and Edge-IIoT.In the absence of the FSFS method,the highest classifier accuracies observed were 60.00%,95.13%,97.02%,98.17%,95.86%,and 94.62%for the respective datasets.When the FSFS technique was integrated with data normalization,encoding,balancing,and feature importance selection processes,accuracies improved to 100.00%,97.81%,98.63%,98.94%,94.27%,and 98.46%,respectively.The FSFS method,with a computational complexity of O(fn log n),demonstrates robust scalability and is well-suited for datasets of large size,ensuring efficient processing even when the number of features is substantial.By automatically eliminating outliers and redundant data,FSFS reduces computational overhead,resulting in faster training and improved model performance.Overall,the FSFS framework not only optimizes performance but also enhances the interpretability and explainability of data-driven models,thereby facilitating more trustworthy decision-making in AI applications.展开更多
To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets ...To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to b...Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS met...Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS methods based on data-driving can reduce the redundancy of data features and improve the prediction accuracy of mechanical properties.Based on the collected data of hot-rolled microalloyed steels,the association rules are used to mine the correlation information between the data.High-quality feature subsets are selected by the proposed FS method(FS method based on genetic algorithm embedding,GAMIC).Compared with the common FS method,it is shown on dataset that GAMIC selects feature subsets more appropriately.Six different ML algorithms are trained and tested for mechanical properties prediction.The result shows that the root-mean-square error of yield strength,tensile strength and elongation based on limit gradient enhancement(XGBoost)algorithm is 21.95 MPa,20.85 MPa and 1.96%,the correlation coefficient(R^(2))is 0.969,0.968 and 0.830,and the mean absolute error is 16.84 MPa,15.83 MPa and 1.48%,respectively,showing the best prediction performance.Finally,SHapley Additive exPlanation is used to further explore the influence of feature variables on mechanical properties.GAMIC feature selection method proposed is universal,which provides a basis for the development of high-precision mechanical property prediction model.展开更多
The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
In endoscopic surgery,the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces fr...In endoscopic surgery,the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences.To address these challenges,we propose a novel Hybrid Triangular Matching(HTM)modeling framework for soft tissue feature tracking.Specifically,HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm.By leveraging barycentric coordinate theory,HTMrapidly and accurately establishes inter-frame correspondences within the triangulated model,enabling stable feature tracking without explicit markers or extensive training data.Experimental results on endoscopic sequences demonstrate that this model-based tracking approach achieves lower computational complexity,maintains robustness against tissue deformation,and provides a scalable geometric modeling method for real-time soft tissue tracking in surgical computer vision.展开更多
In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection cr...In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.展开更多
The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying ge...The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.展开更多
To estimate basal water storage beneath the Antarctic ice sheet, it is essential to have data on the three-dimensional characteristics of subglacial lakes. We present a method to estimate the water depth and surface a...To estimate basal water storage beneath the Antarctic ice sheet, it is essential to have data on the three-dimensional characteristics of subglacial lakes. We present a method to estimate the water depth and surface area of Antarctic subglacial lakes from the inversion of hydraulic potential method. Lake Vostok is chosen as a case study because of the diverse and comprehensive measurements that have been obtained over and around the lake. The average depth of Lake Vostok is around 345±4 m. We estimated the surface area of Lake Vostok beneath the ice sheet to be about 13300±594 km^2. The lake consists of two sub-basins separated by a ridge at water depths of about 200–300 m. The surface area of the northern sub-basin is estimated to be about half of that of the southern basin. The maximum depths of the northern and southern sub-basins are estimated to be about 450 and 850 m, respectively. Total water volume is estimated to be about 4658±204 km^3. These estimates are compared with previous estimates obtained from seismic data and inversion of aerogravity data. In general, our estimates are closer to those obtained from the inversion of aerogravity data than those from seismic data, indicating the applicability of our method to the estimation of water depths of other subglacial lakes.展开更多
Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining indust...Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry.Discrete fracture networks(DFNs)are increasingly used in engineering analyses to spatially model fractures at various scales.The reliability of coal DFNs largely depends on the confidence in the input cleat statistics.Estimates of these parameters can be made from image-based three-dimensional(3D)characterization of coal cleats using X-ray micro-computed tomography(m CT).One key step in this process,after cleat extraction,is the separation of individual cleats,without which the cleats are a connected network and statistics for different cleat sets cannot be measured.In this paper,a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray m CT images.Kernels(filters)representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images.The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin,Queensland,Australia.It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation.Bedding-parallel fractures are also identified and separated,which has his-torically been challenging to delineate and rarely reported.A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling.Finally,variability and heterogeneity with respect to the core axis are investigated.Significant heterogeneity is observed and suggests that the representative elementary volume(REV)of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.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
It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory...It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory（EA） model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.展开更多
This paper presents a hybrid model for three-dimensional Geographical Information Systems which is an integration of surface- and volume-based models. The Triangulated Irregular Network (TIN) and octree models are int...This paper presents a hybrid model for three-dimensional Geographical Information Systems which is an integration of surface- and volume-based models. The Triangulated Irregular Network (TIN) and octree models are integrated in this hybrid models. The TIN model works as a surface-based model which mainly serves for surface presentation and visualization. On the other hand, the octree encoding supports volumetric analysis. The designed data structure brings a major advantage in the three-dimensional selective retrieval. This technique increases the efficiency of three-dimensional data operation.展开更多
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
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 Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金Project supported by the National Natural Science Foundation of China(Nos.12372071 and 12372070)the Aeronautical Science Fund of China(No.2022Z055052001)the Foundation of China Scholarship Council(No.202306830079)。
文摘Currently,there are a limited number of dynamic models available for braided composite plates with large overall motions,despite the incorporation of three-dimensional(3D)braided composites into rotating blade components.In this paper,a dynamic model of 3D 4-directional braided composite thin plates considering braiding directions is established.Based on Kirchhoff's plate assumptions,the displacement variables of the plate are expressed.By incorporating the braiding directions into the constitutive equation of the braided composites,the dynamic model of the plate considering braiding directions is obtained.The effects of the speeds,braiding directions,and braided angles on the responses of the plate with fixed-axis rotation and translational motion,respectively,are investigated.This paper presents a dynamic theory for calculating the deformation of 3D braided composite structures undergoing both translational and rotational motions.It also provides a simulation method for investigating the dynamic behavior of non-isotropic material plates in various applications.
文摘Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certain models,they do not invariably guarantee the extraction of the most critical or impactful features.Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features.However,the challenge of discerning the most relevant and influential features persists,particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial intelligence(AI)applications.In response,this study introduces an innovative,automated statistical method termed Farea Similarity for Feature Selection(FSFS).The FSFS approach computes a similarity metric for each feature by benchmarking it against the record-wise mean,thereby finding feature dependencies and mitigating the influence of outliers that could potentially distort evaluation outcomes.Features are subsequently ranked according to their similarity scores,with the threshold established at the average similarity score.Notably,lower FSFS values indicate higher similarity and stronger data correlations,whereas higher values suggest lower similarity.The FSFS method is designed not only to yield reliable evaluation metrics but also to reduce data complexity without compromising model performance.Comparative analyses were performed against several established techniques,including Chi-squared(CS),Correlation Coefficient(CC),Genetic Algorithm(GA),Exhaustive Approach,Greedy Stepwise Approach,Gain Ratio,and Filtered Subset Eval,using a variety of datasets such as the Experimental Dataset,Breast Cancer Wisconsin(Original),KDD CUP 1999,NSL-KDD,UNSW-NB15,and Edge-IIoT.In the absence of the FSFS method,the highest classifier accuracies observed were 60.00%,95.13%,97.02%,98.17%,95.86%,and 94.62%for the respective datasets.When the FSFS technique was integrated with data normalization,encoding,balancing,and feature importance selection processes,accuracies improved to 100.00%,97.81%,98.63%,98.94%,94.27%,and 98.46%,respectively.The FSFS method,with a computational complexity of O(fn log n),demonstrates robust scalability and is well-suited for datasets of large size,ensuring efficient processing even when the number of features is substantial.By automatically eliminating outliers and redundant data,FSFS reduces computational overhead,resulting in faster training and improved model performance.Overall,the FSFS framework not only optimizes performance but also enhances the interpretability and explainability of data-driven models,thereby facilitating more trustworthy decision-making in AI applications.
基金financially supported by the Ministry of Science and Technology of China(Nos.2022YFF0801201,2021YFC2900300)the National Natural Science Foundation of China(Nos.41872245,U1911202)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515010666)。
文摘To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
文摘Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3702404)the National Natural Science Foundation of China(Grant No.52104370)+4 种基金the Reviving-Liaoning Excellence Plan(XLYC2203186)Science and Technology Special Projects of Liaoning Province(Grant No.2022JH25/10200001)the Postdoctoral Research Fund for Northeastern(Grant No.20210203)Independent Projects of Basic Scientific Research(ZZ2021005)CITIC Niobium Steel Development Award Fund(2022-M1824).
文摘Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS methods based on data-driving can reduce the redundancy of data features and improve the prediction accuracy of mechanical properties.Based on the collected data of hot-rolled microalloyed steels,the association rules are used to mine the correlation information between the data.High-quality feature subsets are selected by the proposed FS method(FS method based on genetic algorithm embedding,GAMIC).Compared with the common FS method,it is shown on dataset that GAMIC selects feature subsets more appropriately.Six different ML algorithms are trained and tested for mechanical properties prediction.The result shows that the root-mean-square error of yield strength,tensile strength and elongation based on limit gradient enhancement(XGBoost)algorithm is 21.95 MPa,20.85 MPa and 1.96%,the correlation coefficient(R^(2))is 0.969,0.968 and 0.830,and the mean absolute error is 16.84 MPa,15.83 MPa and 1.48%,respectively,showing the best prediction performance.Finally,SHapley Additive exPlanation is used to further explore the influence of feature variables on mechanical properties.GAMIC feature selection method proposed is universal,which provides a basis for the development of high-precision mechanical property prediction model.
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004].
文摘In endoscopic surgery,the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences.To address these challenges,we propose a novel Hybrid Triangular Matching(HTM)modeling framework for soft tissue feature tracking.Specifically,HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm.By leveraging barycentric coordinate theory,HTMrapidly and accurately establishes inter-frame correspondences within the triangulated model,enabling stable feature tracking without explicit markers or extensive training data.Experimental results on endoscopic sequences demonstrate that this model-based tracking approach achieves lower computational complexity,maintains robustness against tissue deformation,and provides a scalable geometric modeling method for real-time soft tissue tracking in surgical computer vision.
基金National Natural Science Foundation of China(62161048)Sichuan Science and Technology Program(2022NSFSC0547,2022ZYD0109)。
文摘In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.
基金supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region(Project No.11207724).
文摘The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.
基金funded by the Natural Science Foundation of China (Grant nos. 41674085 and 41621091)the National Key Basic Research Program of China (973 program, Grant nos. 2012CB957703 and 2013CB733301)
文摘To estimate basal water storage beneath the Antarctic ice sheet, it is essential to have data on the three-dimensional characteristics of subglacial lakes. We present a method to estimate the water depth and surface area of Antarctic subglacial lakes from the inversion of hydraulic potential method. Lake Vostok is chosen as a case study because of the diverse and comprehensive measurements that have been obtained over and around the lake. The average depth of Lake Vostok is around 345±4 m. We estimated the surface area of Lake Vostok beneath the ice sheet to be about 13300±594 km^2. The lake consists of two sub-basins separated by a ridge at water depths of about 200–300 m. The surface area of the northern sub-basin is estimated to be about half of that of the southern basin. The maximum depths of the northern and southern sub-basins are estimated to be about 450 and 850 m, respectively. Total water volume is estimated to be about 4658±204 km^3. These estimates are compared with previous estimates obtained from seismic data and inversion of aerogravity data. In general, our estimates are closer to those obtained from the inversion of aerogravity data than those from seismic data, indicating the applicability of our method to the estimation of water depths of other subglacial lakes.
文摘Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal.Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry.Discrete fracture networks(DFNs)are increasingly used in engineering analyses to spatially model fractures at various scales.The reliability of coal DFNs largely depends on the confidence in the input cleat statistics.Estimates of these parameters can be made from image-based three-dimensional(3D)characterization of coal cleats using X-ray micro-computed tomography(m CT).One key step in this process,after cleat extraction,is the separation of individual cleats,without which the cleats are a connected network and statistics for different cleat sets cannot be measured.In this paper,a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray m CT images.Kernels(filters)representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images.The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin,Queensland,Australia.It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation.Bedding-parallel fractures are also identified and separated,which has his-torically been challenging to delineate and rarely reported.A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling.Finally,variability and heterogeneity with respect to the core axis are investigated.Significant heterogeneity is observed and suggests that the representative elementary volume(REV)of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.
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 National Natural Science Foundation of China (Grant No. 50805021)
文摘It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory（EA） model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.
文摘This paper presents a hybrid model for three-dimensional Geographical Information Systems which is an integration of surface- and volume-based models. The Triangulated Irregular Network (TIN) and octree models are integrated in this hybrid models. The TIN model works as a surface-based model which mainly serves for surface presentation and visualization. On the other hand, the octree encoding supports volumetric analysis. The designed data structure brings a major advantage in the three-dimensional selective retrieval. This technique increases the efficiency of three-dimensional data operation.
