• International Journal of CAD/CAM
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• v.3 no.1_2
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• pp.67-75
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• 2003

Viewpoint-dependent feature-based modelling in computer-aided design is developed for the purposes of supporting engineering design representation and automation. The approach of this paper uses a combination of a multi-level modelling approach. This has two stages of mapping between models, and the multi-level model approach is implemented in three-level architecture. Top of this level is a feature-based description for each viewpoint, comprising a combination of form features and other features such as loads and constraints for analysis. The middle level is an executable representation of the feature model. The bottom of this multi-level modelling is a evaluation of a feature-based CAD model obtained by executable feature representations defined in the middle level. The mappings involved in the system comprise firstly, mapping between the top level feature representations associated with different viewpoints, for example for the geometric simplification and addition of boundary conditions associated with moving from a design model to an analysis model, and secondly mapping between the top level and the middle level representations in which the feature model is transformed into the executable representation. Because an executable representation is used as the intermediate layer, the low level evaluation can be active. The example will be implemented with an analysis model which is evaluated and for which results are output. This multi-level modelling approach will be investigated within the framework aimed for the design automation with a feature-based model.

• Korean Journal of Computational Design and Engineering
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• v.18 no.1
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• pp.71-82
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• 2013

To modify product design easily, modern CAD systems adopt the feature-based model as their primary representation. On the other hand, the boundary representation (B-rep) model is used as their secondary representation. IGES and STEP AP203 edition 1 are the representative standard formats for the exchange of CAD files. Unfortunately, both of them only support the B-rep model. As a result, feature data are lost during the CAD file exchange based on these standards. Loss of feature data causes the difficulty of CAD model modification and prevents the transfer of design intent. To resolve this problem, a tool for recognizing design features from a B-rep model and then reconstructing a feature-based model with the recognized features should be developed. As the first part of this research, this paper presents a method for decomposing a B-rep model into simple volumes suitable for design feature recognition. The results of experiments with a prototype system are analyzed. From the analysis, future research issues are suggested.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• MALSORI
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• no.63
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• pp.125-137
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• 2007

Recently, many techniques have been proposed to improve the noise robustness for speaker verification. In this paper, we consider the feature recombination technique in multi-band approach. In the conventional feature recombination for speaker verification, to compute the likelihoods of speaker models or universal background model, whole feature components are used. This computation method is not effective in a view point of multi-band approach. To deal with non-effectiveness of the conventional feature recombination technique, we introduce a subband likelihood computation, and propose a modified feature recombination using subband likelihoods. In decision step of speaker verification system in noise environments, a few very low likelihood scores of a speaker model or universal background model cause speaker verification system to make wrong decision. To overcome this problem, a reliable feature selection method is proposed. The low likelihood scores of unreliable feature are substituted by likelihood scores of the adaptive noise model. In here, this adaptive noise model is estimated by maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. The proposed method using subband-based reliable feature selection obtains better performance than conventional feature recombination system. The error reduction rate is more than 31 % compared with the feature recombination-based speaker verification system.

• Korean Journal of Computational Design and Engineering
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• v.11 no.1
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• pp.27-40
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• 2006

The exchange of parameterized feature-based CAD models is important for product data sharing among different organizations and automation systems. The role of feature-based modeling is to gonerate the shape of product and capture design intends In a CAD system. A feature is generated by referring to topological entities in a solid. Identifying referenced topological entities of a feature is essential for exchanging feature-based CAD models through a neutral format. If the CAD data contains the modification history in addition to the construction history, a matching mechanism is also required to find the same entity in the new model (post-edit model) corresponding to the entity in the old model (preedit model). This problem is known as the persistent naming problem. There are additional problems arising from the exchange of parameterized feature-based CAD models. Authors have analyzed previous studies with regard to persistent naming and characteristics for the exchange of parameterized feature-based CAD models, and propose a solution to the persistent naming problem. This solution is comprised of two parts: (a) naming of topological entities based on the object spore information (OSI) and secondary name (SN); and (b) name matching under the proposed naming.

• Journal of Institute of Convergence Technology
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• v.4 no.2
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• pp.1-9
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• 2014

This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.733-744
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• 2016

The legacy methods for converting a business model to a feature model make it difficult to support an automatic transformation due to a dependence on a domain analyzers' intuitions, which hinders the feature oriented development for the integration of feature modeling in business modeling. This paper proposes a method for converting a BPMN business model into a feature model based on syntax. To allow the conversion between the heterogeneous models from BPMN to the FM(Feature Model), it defines the grouping mechanism based activities' syntax, and then makes translation rules and a method based on the element (represent business function) and structure (relationships and process among elements), which are common constructs of their models. This method was applied to an online shopping mall system as a case study. With this mechanism, it will help develop a mechanical or automated structure transformation from the BPMN model to the FM.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1614-1623
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• 2021

In this paper, the purpose is evaluation of the effect of using fractal feature in machine learning based pancreatic tumor classification. We used the data that Pancreas CT series 469 case including 1995 slice of benign and 1772 slice of malignant. Feature selection is implemented from 109 feature to 7 feature by Lasso regularization. In Fractal feature, fractal dimension is obtained by box-counting method, and hurst coefficient is calculated range data of pixel value in ROI. As a result, there were significant differences in both benign and malignancies tumor. Additionally, we compared the classification performance between model without fractal feature and model with fractal feature by using support vector machine. The train model with fractal feature showed statistically significant performance in comparison with train model without fractal feature.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.105-113
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• 2009

The feature models are widely used to model the commonalities and variabilities among the products in the domain engineering phase of software product line developments. The findings and corrections of the errors or consistencies in the feature models are essential to the successful software product line engineering. The aids of the automated tools are needed to perform the validation of the feature models effectively. This paper describes the approach based on JESS rule-base system to validate the feature models and proposes the feature model validation tool using this approach. The tool of this paper validates the feature models in real-time when modeling the feature models. Then it provides the information on existence of errors and the explanations on causes of those errors, which allows the feature modeler to create the error-free feature models. This attempt to validate the feature model using the rule-based system is supposed to be the first time in this research field.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.558-566
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• 2005

We propose a feature-based multi-resolution representation of B-rep solid models using history-based Boolean operations based on the merge-and-select algorithm. Because union and subtraction are commutative in the history-based Boolean operations, the integrity of the models at various levels of detail (LOD) is guaranteed for the reordered features regardless of whether the features are subtractive or additive. The multi-resolution solid representation proposed in this paper includes a non-manifold topological merged-set model of all feature primitives as well as a feature-modeling tree reordered consistently with a given LOD criterion. As a result, a B-rep solid model for a given LOD can be provided quickly, because the boundary of the model is evaluated without any geometric calculation and extracted from the merged set by selecting the entities contributing to the LOD model shape.