An Effective Feature Modeling Approach for 3D Structural Topology Design Optimization

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.

Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability information.Self-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts.However,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources.The process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and error-prone.Nevertheless,systems’complexity can be reduced while the development process of systems by reusing elements or components.FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components.While current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been considered.In this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse process.Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

RESEARCH ON PARAMETRIC MODELING SYSTEM BASED ON FEATURE

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.

A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics

A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated.General initialization schemes as well as general regimes for the network width and training data size are considered.In the overparametrized regime,it is shown that gradient descent dynamics can achieve zero training loss exponentially fast regardless of the quality of the labels.In addition,it is proved that throughout the training process the functions represented by the neural network model are uniformly close to those of a kernel method.For general values of the network width and training data size,sharp estimates of the generalization error are established for target functions in the appropriate reproducing kernel Hilbert space.

A Feature Model Based Framework for Refactoring Software Product Line Architecture

Software product line （SPL） is an approach used to develop a range of software products with a high degree of similarity. In this approach, a feature model is usually used to keep track of similarities and differences. Over time, as modifications are made to the SPL, inconsistencies with the feature model could arise. The first approach to dealing with these inconsistencies is refactoring. Refactoring consists of small steps which, when accumulated, may lead to large-scale changes in the SPL, resulting in features being added to or eliminated from the SPL. In this paper, we propose a framework for refactoring SPLs, which helps keep SPLs consistent with the feature model. After some introductory remarks, we describe a formal model for representing the feature model. We express various refactoring patterns applicable to the feature model and the SPL formally, and then introduce an algorithm for finding them in the SPL. In the end, we use a real-world case study of an SPL to illustrate the applicability of the framework introduced in the paper.

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

Supporting feature model refinement with updatable view

In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software do- main. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the view updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible view, and finally refine the feature model through modifications on the view. The main characteristics of this approach are twofold： a set of powerful rules （as the slicing criterion） to slice the feature model into a view auto- matically, and a novel use of a bidirectional transformation language to make the view updatable. We have successfully developed a tool, and a nontrivial case study shows the feasi- bility of this approach.

Interactive Inconsistency Fixing in Feature Modeling

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.

Full feature data model for spatial information network integration

In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.

Structure Design of Turbo-Jet Engine Blade with Feature Based Parametric Modeling

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.

Adaptability Feature's Concept, Modeling and Application in Product Design

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.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

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.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

DIMENSION VARIATION OF FEATURE-BASED MODEL BY OPERATING DIRECTLY ON B-REP

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Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

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.

Features of animal models of complex partial epilepsy established through unilateral,bilateral and alternate-side kindling at hippocampus of rats

BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at different sites. OBJECTIVE： To compare the features of animal models of complex partial epilepsy established through unilateral, bilateral and alternate-side kindling at hippocampus and successful rate of modeling among these 3 different ways. DESIGN： A randomized and controlled animal experiment SETTING： Department of Neurology, Qilu Hospital, Shandong University MATERIALS： Totally 60 healthy adult Wistar rats, weighing 200 to 300 g, of either gender, were used in this experiment. BL-410 biological functional experimental system （Taimeng Science and Technology Co. Ltd, Chengdu） and SE-7102 type electronic stimulator （Guangdian Company, Japan） were used in the experiment. METHODS： This experiment was carried out in the Experimental Animal Center of Shandong University from April to June 2004. After rats were anesthetized, electrode was implanted into the hippocampus. From the first day of measurement of afterdischarge threshold value, rats were given two-square-wave suprathreshold stimulation once per day with 400 μA intensity, 1ms wave length, 60 Hz frequency for 1 s duration. Left hippocampus was stimulated in unilateral kindling group, bilateral hippocampi were stimulated in bilateral kindling group, and left and right hippocampi were stimulated alternately every day in the alternate-side kindling group. Seizure intensity was scored： grade 0： normal, 1： wet dog-like shivering, facial spasm, such as, winking, touching the beard, rhythmic chewing and so on; 2： rhythmic nodding; 3： forelimb spasm;4： standing accompanied by bilateral forelimb spasm;5： tumbling, losing balance, four limbs spasm. Modeling was successful when seizure intensity reached grade 5. t test was used for the comparison of mean value between two samples. MAIN OUTCOME MEASURES： Comparison of the successful rate of modeling, the times of stimulation to reach intensity of grade 5, the lasting time of seizure of grade 3 of rats in each group. RESULTS： Four rats of alternate-side kindling group dropped out due to infection-induced electrode loss, and 56 rats were involved in the result analysis. The successful rate of unilateral kindling group, bilateral kin- dling group and alternate-side kindling group was 55%（11/20）,100%（16/16）and 100%（20/20）, respective- ly. The stimuli to reach the grade 5 spasm were significantly more in the bilateral kindling group than in the unilateral kindling group [（30.63±3.48）, （19.36±3.47）times, t=8.268, P 〈 0.01], and those were significantly fewer in the alternate-side kindling group than in the unilateral kindling group [（ 10.85±1.98）times, t=-8.744, P 〈 0.01]. The duration of grade 3 spasm was significantly longer in the bilateral kindling group than in the unilateral kindling group [（9.75±2.59）, （3.21 ±1.58）days,t=-8.183,P 〈 0.01], Among 20 successful rats of al- ternate-side kindling group, grade 5 spasm was found in the left hippocampi of 11 rats, but grade 3 spasm in their right hippocampi; Grade 5 spasm was found in the right hippocampi of the other 9 rats, grade 4 spasm in the left hippocampus of 1 rat and grade 3 of 8 rats. CONCLUSION ： The speed of establishing epilepsy seizure model by alternate-side kindling is faster than that by unilateral kindling, while that by bilateral kindling is slower than that by unilateral kindling. The successful rate is very high to establish complex partial epilepsy with alternate-side or bilateral kindling. Epilepsy seizure established by alternate-side kindling has antagonistic effect of kindling and the seizure duration of grade 3 spasm is prolonged.

REVERSE MODELING FOR CONIC BLENDING FEATURE

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.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

The Feature-Based a New Object Coding Approach for Prismatic Parts at the Part Modeling

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.

FEATURE SOLID MODELING TOOL SYSTEM:FSMTS

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.