Journal articles: 'CAD Features'

1

Kudlowitz, David, and Franco Muggia. "Clinical features of taxane neuropathy." Anti-Cancer Drugs 25 (June 2014): 495–501. http://dx.doi.org/10.1097/cad.0000000000000051.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Miao, Huikang K., Nandakumar Sridharan, and Jami J. Shah. "CAD-CAM integration using machining features." International Journal of Computer Integrated Manufacturing 15, no. 4 (January 2002): 296–318. http://dx.doi.org/10.1080/09511920110077502.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

M.G. Leesley, Dr. "CAD/CAM: Features, application and management." Computer-Aided Design 26, no. 5 (May 1994): 400. http://dx.doi.org/10.1016/0010-4485(94)90027-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Nnaji, Bartholomew O., and Tzong-Shyan Kang. "Interpretation of CAD models through neutral geometric knowledge." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 4, no. 1 (February 1990): 15–45. http://dx.doi.org/10.1017/s0890060400002225.

Full text

Abstract:

A generalized approach to fast interpretation of objects and their features has so far eluded researchers. In manufacturing, this interpretation can be approached from the vision point of view or from the CAD data perspective. Presently, CAD systems are widely used in several aspects of manufacturing production. It is therefore more efficient to use CAD data for object reasoning in manufacturing, especially when systems will eventually be data driven. Components can be modelled on a CAD system using various modelling techniques and the representation of their geometric information is still CAD system dependent. However, the advent of the Initial Graphics Exchange Specification (IGES) now makes it possible to represent CAD data in a neutral and standard manner.This paper describes a scheme for recognizing and representing features for CAD data extracted using the IGES interface. The concepts developed are based on graph-based feature representation, where features are represented by a set of faces as well as their topological adjacency.Strategies for classifying features and methods of decomposing a complicated feature into several simpler features for recognition purposes are discussed.

APA, Harvard, Vancouver, ISO, and other styles

5

Wei, Baoli, and Meng Lv. "CAD Integration of Mechanical Numerical Control Board Parts Based on Machining Features." Computer-Aided Design and Applications 18, S3 (October 20, 2020): 176–87. http://dx.doi.org/10.14733/cadaps.2021.s3.176-187.

Full text

Abstract:

The development and application of computer-aided design (CAD) technology has led to rapid improvements in product design automation, crafting process automation and numerical control programming automation. Machining feature refers to basic configuration units that constitute part shapes and the collection of non-geometric information with engineering semantics attached to it. The integration of mechanical numerical control parts is the integration of part design features and machining features, and each feature corresponds to a set of processing methods. Based on the summaries and analyses of previous research works, this paper expounded the current status and significance of mechanical numerical control board part integration, elaborated the development background, current status and future challenges of machining features and CAD technology, introduced a data transfer method of CAD integration and machining features-based part integration system, analyzed the design and machining features of CAD integration of board parts, constructed the graphics processing model and information reorganization model for CAD integration of board parts; conducted the feature description and modeling analysis of CAD integration of plate parts; discussed the crafting information similarity of mechanical numerical control plate part integration; explored the feature information and expression of feature library for plate parts integration.

APA, Harvard, Vancouver, ISO, and other styles

6

Gisi, Mark A., and Cristiano Sacchi. "Co-CAD: A Collaborative Mechanical CAD System." Presence: Teleoperators and Virtual Environments 3, no. 4 (January 1994): 341–50. http://dx.doi.org/10.1162/pres.1994.3.4.341.

Full text

Abstract:

It is becoming increasingly common for manufacturing design teams to be composed of members belonging to the same organization, yet located in geographically different places. This has significantly increased the need for better support of synchronous communication among team members collaborating over a design. Unfortunately, there is a considerable technological gap in the support for collaborative, synchronously communicating mechanical CAD systems. In this paper we describe a prototype system, CoCAD, that provides a number of features that support synchronous collaboration among a number of mechanical CAD engineers located at different sites. Some of these features include the ability for each person to edit a design, the ability for each user to customize their local view of a design, the ability for each user to share a common view of a design, a shared pointer, the ability for someone to join in the middle of a design session, and object ownership and access permissions.

APA, Harvard, Vancouver, ISO, and other styles

7

Liu, Tongtong, Peng Li, Yuanyuan Liu, Huan Zhang, Yuanyang Li, Yu Jiao, Changchun Liu, et al. "Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals." Entropy 23, no. 6 (May 21, 2021): 642. http://dx.doi.org/10.3390/e23060642.

Full text

Abstract:

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

APA, Harvard, Vancouver, ISO, and other styles

8

HAGIWARA, YUKI, and OLIVER FAUST. "NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740006. http://dx.doi.org/10.1142/s0219519417400061.

Full text

Abstract:

In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls. We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s [Formula: see text]-test and the [Formula: see text]-value was used for feature ranking. We found that nonlinear features can effectively represent the physiological realities of the human heart.

APA, Harvard, Vancouver, ISO, and other styles

9

Gaha, Raoudha, Abdelmajid Benamara, and Bernard Yannou. "Ecodesigning with CAD Features: Analysis and Proposals." Advances in Mechanical Engineering 5 (January 2013): 531714. http://dx.doi.org/10.1155/2013/531714.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Niu, Zhibin, Ralph R. Martin, Frank C. Langbein, and Malcolm A. Sabin. "Rapidly finding CAD features using database optimization." Computer-Aided Design 69 (December 2015): 35–50. http://dx.doi.org/10.1016/j.cad.2015.08.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Zhang, Kai Xing, Shu Sheng Zhang, and Xiao Liang Bai. "Partial Matching Algorithm of 3D CAD Models Based on the Constraints of Transition Features." Advanced Materials Research 97-101 (March 2010): 3371–75. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3371.

Full text

Abstract:

The CAD models of mechanical parts usually have many blends and chamfers, and the existence of these machining features can greatly change the geometric and topological patterns of the CAD models, but the existing partial matching algorithms cannot match the CAD models which contain machining features such as blends and chamfers. In this paper, a new approach to partial matching based on the constraints of transition features is proposed. Firstly, the transition features are identified by feature recognition, and then these machining features are removed to eliminate the impacts to the geometric and topological information of the CAD models, and the attribute adjacent graph is reconstructed, finally, the sub-graph isomorphism approach is used to achieve the partial matching. Experimental results show that this method can achieve partial matching of CAD models which contain machining features such as blends and chamfers, and the matching efficiency can satisfy the requirement of the engineering retrieval.

APA, Harvard, Vancouver, ISO, and other styles

12

Niu, Wen Tie, Peng Fei Wang, Yu Shen, Wei Guo Gao, and Li Na Wang. "A Feature-Based CAD-CAE Integrated Approach of Machine Tool and its Implementation." Advanced Materials Research 201-203 (February 2011): 54–58. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.54.

Full text

Abstract:

An analysis feature-based CAD-CAE integrated approach was proposed to solve the problems of rapidly CAE modeling for static and dynamic analysis process of machine tool. Firstly, analysis features were defined in CAD system and analysis feature library was constructed for machine tool and its structural components. Secondly, analysis feature model was constructed by attaching analysis feature to CAD model interactively. Finally, ANSYS parametric design language (APDL) file was generated automatically by mapping analysis features to APDL codes, which realized the integration of CAD system and ANSYS system. Based on application programming interface (API) of SolidWorks, a parametric CAD-CAE tool oriented to static and dynamic analysis of machine tool was developed, which realized parametric modeling and automatic analysis of machine tool and improved design efficiency and quality of machine tool.

APA, Harvard, Vancouver, ISO, and other styles

13

Parvaz, Hadi, and Mohammad Javad Nategh. "A Multi-TAD Framework for Recognizing Machining Features Using Hint Based Recognition Algorithm." Advanced Materials Research 445 (January 2012): 905–10. http://dx.doi.org/10.4028/www.scientific.net/amr.445.905.

Full text

Abstract:

Feature recognition is a powerful tool for integrating CAD and CAM. Hint based algorithm is one of the most useful tools for recognizing features that is usually used with other algorithms forming hybrid systems. In the present study, hint based algorithm has been chosen as the main tool for recognizing machining features by defined recognition rules. Beside the need for low computation, the specific characteristic of the proposed recognition system is its multi-tool access direction (multi-TAD) property where the designed algorithm recognizes and lists each machining feature with its special access direction. It detects all the features available in each TAD and sorts the TADs in descending order of their number of features. The developed recognition system is capable of recognizing different types of isolated and nested intersecting feature types. The developed software accepts neutral file formats (i.e. STEP and IGES) beside the B-Rep CAD model as its input CAD file. The developed system has been tested with different CAD parts.

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Shen, Xiao Dong Shao, and Xiao Bo Ge. "A Kind of CAD/CAE Integrated Modeling Technology Based on FEATURE." Advanced Materials Research 97-101 (March 2010): 3436–42. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3436.

Full text

Abstract:

A kind of CAD/CAE integrated modeling method based on feature is proposed. Firstly, analysis features are attached to CAD model of part and standard part library is formed. Secondly, the CAD model is created using standard parts. Thirdly, geometry information of analysis features is extracted from CAD model and is reconstructed automatically under CAE environment. Finally, based on feature-based meshing and combination technique, CAE model is built quickly. A prototype software for large-scale antenna structure (LSAS) CAD/CAE integrated modeling has been developed and used in performance analysis of a 16-meter-diameter LSAS. The method has been proved to be useful for improving speed, accuracy and consistency of complicated structure CAE modeling notably.

APA, Harvard, Vancouver, ISO, and other styles

15

Hao, Jian Ling, Kai Ling Li, and Hai Ming Zhang. "The Research on Part Information Model of Injection Mold CAD/CAPP." Advanced Materials Research 139-141 (October 2010): 1211–14. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1211.

Full text

Abstract:

Feature-modeling has reached the design mainstream, which was considered as one of ways to integrated system unblocked. Discussing the definition and classification of feature and giving the contents of features of management information, features of technical information and form features, then the part information model was established based on the features in the paper. The systems of treelike part classification coding and feature coding take an important part in the model, which were described based on structural and technological characters of injection parts. Under the rules of feature coding, the feature codes for sprue bush and B plate were developed. The part information model is used to describe, obtain, store, and transmit the part information of the integrated system.

APA, Harvard, Vancouver, ISO, and other styles

16

Safdar, Mutahar, Tahir Abbas Jauhar, Youngki Kim, Hanra Lee, Chiho Noh, Hyebin Kim, Inhwan Lee, Imgyu Kim, Soonjo Kwon, and Soonhung Han. "Feature-based translation of CAD models with macro-parametric approach: issues of feature mapping, persistent naming, and constraint translation." Journal of Computational Design and Engineering 7, no. 5 (April 9, 2020): 603–14. http://dx.doi.org/10.1093/jcde/qwaa043.

Full text

Abstract:

Abstract Feature-based translation of computer-aided design (CAD) models allows designers to preserve the modeling history as a series of modeling operations. Modeling operations or features contain information that is required to modify CAD models to create different variants. Conventional formats, including the standard for the exchange of product model data or the initial graphics exchange specification, cannot preserve design intent and only geometric models can be exchanged. As a result, it is not possible to modify these models after their exchange. Macro-parametric approach (MPA) is a method for exchanging feature-based CAD models among heterogeneous CAD systems. TransCAD, a CAD system for inter-CAD translation, is based on this approach. Translators based on MPA were implemented and tested for exchange between two commercial CAD systems. The issues found during the test rallies are reported and analyzed in this work. MPA can be further extended to remaining features and constraints for exchange between commercial CAD systems.

APA, Harvard, Vancouver, ISO, and other styles

17

Singh, Ram Sewak, Barjinder Singh Saini, and Ramesh Kumar Sunkaria. "ASSESSMENT OF CARDIAC HEART FAILURE AND CARDIAC ARTERY DISEASE BY THE HIGHER ORDER SPECTRA." Biomedical Engineering: Applications, Basis and Communications 30, no. 02 (March 26, 2018): 1850016. http://dx.doi.org/10.4015/s1016237218500163.

Full text

Abstract:

Cardiac diseases are major reason of death in the world populace and the numeral of cases is upsurging every year. Due to cardiac artery disease (CAD), the strength of heart muscles becomes weak and heart pumping is disturbed which may eventually lead to abnormal heart beat and heart failure. Therefore, the beginning stage detection of CAD and cardiac heart failure (CHF) are of prime importance. In this work, we have used a non-invasive diagnosis method as higher order spectra (HOS) for assessment of cardiac diseases. The method indicates whether or not a cardiac heart disease is present, by assessing the cardiac health of subjects using extracted features from heart rate variability (HRV) signals. This assessment is based on 10 spectra nonlinear features. These features were extracted from HRV signals by using the HOS method. For this study, the R-R interval data (i.e. HRV signals) were taken from the standard database of cardiac heart failure (CHF), CAD patients, healthy young (YNG) and Self recorded of healthy young (SELF_YNG) subjects. Statistical assessments were performed on the group of database sets as YNG-CAD, YNG-CHF, SELF_YNG-CAD and Self_YNG-CHF subjects. A Wilcoxon rank sum test ([Formula: see text]-value) was used to statistically compare the features extracted by HOS for group of data sets. It indicates whether or not the same features of individual classes of HRV data sets are dissimilar. The results depicted that the all features are very significant ([Formula: see text]) except the phase entropy (PHE) feature which is not significant for CAD-CHF, SELF_YNG-CAD and SELF_YNG-CHF group of subjects. While in the case of YNG-CAD group of subjects, features like first-order spectral moment of amplitudes of diagonal elements (H3), PHE and logarithmic amplitudes of diagonal elements (H2) are significant ([Formula: see text]) and excluding these features, the remaining features are very significant except MM and H1 which are not significant. The results also depicted that the mean value of sum of logarithmic amplitude (H1), H2, normalized entropy (P1), normalized squared entropy (P2) and PHE features of healthy YNG subjects are having higher values than that of CAD and CHF patients. While weighted center of bi-spectrum (WCOB2) and FLAT spectrum features are lower than CAD and CHF patients compared to YNG subjects. In case of CAD and CHF patients, all the features of CAD patients are having higher values compared to CHF except P1, P2 and WCOB1.

APA, Harvard, Vancouver, ISO, and other styles

18

Tanmay Ghosh, Khan Alamgir, Yang Xingyao, Muhammad Fayaz,. "Computer-Aided Diagnostic System for Digital Mammography." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 12, 2021): 989–95. http://dx.doi.org/10.17762/itii.v9i2.443.

Full text

Abstract:

In this work, Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems are developed and tested using the public and freely available mammographic databases named MIAS and DDSM databases, respectively. CADe system is used to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing a breast abnormality. At the same time, CADx is developed to distinguish between normal, benign and malignant breast tissues, and it helps radiologists to decide whether a biopsy is needed when reading a diagnostic mammogram or not. Any CAD system is constituted of typical stages including preprocessing and segmentation of mammogram images, extraction of regions of interest (ROI), features removal, features selection and classification. In both proposed CAD systems, ROIs are selected using a window size of 32×32 pixels, then a total of 543 features from four different feature categories are extracted from each ROI and then normalized. After that, the selection of the most relevant features is performed using four different selection methods from MATLAB Pattern Recognition Toolbox v.5 (PRtool5) named Sequential Backward Selection (SBS), Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS) and Branch and Bound Selection (BBS) methods. We also utilized Principal Component Analysis (PCA) as the fifth method to reduce the dimensions of the features set. After that, we used different classifiers such as Support Vector Machines (SVM), K-voting Nearest Neighbor (K-NN), Quadratic Discriminant Analysis (QDA) and Artificial Neural Networks (ANN) for the classification. Both CAD systems have the same implementation stages but different output. CADe systems are designed to detect breast abnormalities while CADx system indicates the likelihood of malignancy of lesions. Finally, we independently compared the performance of all classifiers with each selection method in both modes. The evaluation of the proposed CAD systems is done using performance indices such as sensitivity, specificity, the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curves, the overall accuracy and Cohen-k factor. Both CAD systems provided encouraging results. These results were different corresponding to the selection method and classifier.

APA, Harvard, Vancouver, ISO, and other styles

19

Yang, Hsin-Chia, Chen-Han Chang, Sheng-Wen Huang, Yi-Hong Chou, and Pai-Chi Li. "Correlations among Acoustic, Texture and Morphological Features for Breast Ultrasound CAD." Ultrasonic Imaging 30, no. 4 (October 2008): 228–36. http://dx.doi.org/10.1177/016173460803000404.

Full text

Abstract:

Acoustic, textural and morphological features of the breast in ultrasound imaging were extracted for computer-aided diagnosis. In addition, correlations among different categories of features were analyzed. Clinical data from 14 patients (7 malignant and 7 benign samples) were acquired. A custom-made experimental apparatus was used for simultaneous data acquisition of B-mode ultrasound and limited-angle tomography images. Textural features were extracted from B-mode images, including five parameters derived from the gray-level concurrence matrix and five parameters derived from a nonseparable wavelet transform. Morphological features were also extracted from B-mode images, including the depth-to-width ratio and normalized radial gradient. Acoustic features were estimated using limited-angle tomography, including the sound velocity and attenuation coefficient. Generally, the correlation coefficients for features within the textural feature group were relatively high (0.48–0.79), whereas those between different feature categories were relatively low (0.17–0.40). This suggests that combining different sets of features would improve the computer-aided diagnosis of breast cancer.

APA, Harvard, Vancouver, ISO, and other styles

20

Shah, Jami J., David Anderson, Yong Se Kim, and Sanjay Joshi. "A Discourse on Geometric Feature Recognition From CAD Models." Journal of Computing and Information Science in Engineering 1, no. 1 (November 1, 2000): 41–51. http://dx.doi.org/10.1115/1.1345522.

Full text

Abstract:

This paper discusses the past 25 years of research in feature recognition. Although a great variety of feature recognition techniques have been developed, the discussion here focuses on the more successful ones. These include graph based and “hint” based methods, convex hull decomposition, and volume decomposition-recomposition techniques. Recent advances in recognizing features with free form features are also presented. In order to benchmark these methods, a frame of reference is created based on topological generality, feature interactions handled, surface geometry supported, pattern matching criteria used, and computational complexity. This framework is used to compare each of the recognition techniques. Problems related to domain dependence and multiple interpretations are also addressed. Finally, some current research challenges are discussed.

APA, Harvard, Vancouver, ISO, and other styles

21

Stolyarov, Aleksey I., Yulia V. Donetskaya, and Yuriy A. Gatchin. "CAD FEATURES IN THE DESIGN OF A COMPLEX." Proceedings of Irkutsk State Technical University 22, no. 7 (July 2018): 88–95. http://dx.doi.org/10.21285/1814-3520-2018-7-88-95.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Ando, Sonken, Ryo Ikeda, Hideki Aoyama, and Norihito Hiruma. "Development of CAD System Based on Function Features." Key Engineering Materials 447-448 (September 2010): 442–46. http://dx.doi.org/10.4028/www.scientific.net/kem.447-448.442.

Full text

Abstract:

Three-dimensional CAD systems contribute considerably to the detailed design processes of products. They are applied to the construction of 3D design models which are also utilized for design evaluation using a CAE system and for NC data generation using a CAM system. Since the functions of 3D CAD systems for constructing 3D models are increasingly being enhanced, they enable designers to easily construct 3D product models without design expertise. In detailed design work, designers are required not only to exactly define product shapes but also to assign attribute information such as dimensional tolerance, geometrical tolerance, roughness, machining process to be performed etc., which are essential for the manufacturing process. However, inexperienced designers often find it extremely hard to determine optimum attribute values and design values. In addition, it is more difficult to construct the required die/mold from the desired product shape taking into account forming errors caused by shrinkage during plastic injection and springback during press forming. This paper proposes a method to automatically assign required attribute information to each part of a designed product, to assist the model construction of a die/mold from a product shape, and to provide design support information on each part of a designed product to a designer. The proposed method is realized by assigning a Function Feature to each part; all the function features proposed in this paper are original. A CAD system based on the proposed method for injection molding and press forming was developed, and results of simple design experiments confirmed the usefulness of the CAD system and function features

APA, Harvard, Vancouver, ISO, and other styles

23

Gao, J., D. T. Zheng, and N. Gindy. "Extraction of machining features for CAD/CAM integration." International Journal of Advanced Manufacturing Technology 24, no. 7-8 (April 21, 2004): 573–81. http://dx.doi.org/10.1007/s00170-003-1882-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Xiao, Hong, Yuan Li, JianFeng Yu, and Jie Zhang. "CAD mesh model simplification with assembly features preservation." Science China Information Sciences 57, no. 3 (March 28, 2013): 1–11. http://dx.doi.org/10.1007/s11432-013-4791-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Cheng, Fengbei, Zhenyu Liu, Guifang Duan, Chan Qiu, Bing Yi, and Jianrong Tan. "Complex CAD surface shape design using semantic features." Journal of Mechanical Science and Technology 28, no. 7 (July 2014): 2715–22. http://dx.doi.org/10.1007/s12206-014-0628-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

M, Surendra Prasad, and Manimurugan S. "A New Modified Recurrent Extreme Learning with PSO Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 15–21. http://dx.doi.org/10.53409/mnaa.jcsit20201303.

Full text

Abstract:

Breast cancer is a prevalent cause of death, and is the only form of cancer that is common among women worldwide and mammograms-based computer-aided diagnosis (CAD) program that allows early detection, diagnosis and treatment of breast cancer. But the performance of the current CAD systems is still unsatisfactory. Early recognition of lumps will reduce overall breast cancer mortality. This study investigates a method of breast CAD, focused on feature fusion with deep features of the Convolutional Neural Network (CNN). First, present a scheme of mass detection based on CNN deep features and modified clustering of the Extreme Learning Machine (MRELM). It forecasts load through Recurrent Extreme Learning Machine (RELM) and utilizes Artificial Bee Colony (ABC) to optimize weights and biases. Second, a collection of features is constructed that relays deep features, morphological features, texture features, and density features. Third, MRELM classifier is developed to distinguish benign and malignant breast masses using the fused feature set. Extensive studies show the precision and efficacy of the proposed method of mass diagnosis and classification of breast cancer.

APA, Harvard, Vancouver, ISO, and other styles

27

Qiu, Yuchen, Jie Song, Xianglan Lu, Yuhua Li, Bin Zheng, Shibo Li, and Hong Liu. "Feature Selection for the Automated Detection of Metaphase Chromosomes: Performance Comparison Using a Receiver Operating Characteristic Method." Analytical Cellular Pathology 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/565392.

Full text

Abstract:

Background. The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented.Methods. In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method.Results. The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity.Conclusion. This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.

APA, Harvard, Vancouver, ISO, and other styles

28

Xu, Hui, Zhuo Chen, and Chouyang Li. "The prognostic value of Piezo1 in breast cancer patients with various clinicopathological features." Anti-Cancer Drugs 32, no. 4 (February 4, 2021): 448–55. http://dx.doi.org/10.1097/cad.0000000000001049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Singh, Ram Sewak, Barjinder Singh Saini, and Ramesh Kumar Sunkaria. "Detection of coronary artery disease by reduced features and extreme learning machine." Medicine and Pharmacy Reports 91, no. 2 (April 26, 2018): 166–75. http://dx.doi.org/10.15386/cjmed-882.

Full text

Abstract:

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

APA, Harvard, Vancouver, ISO, and other styles

30

Sethi, Gaurav, and B. S. Saini. "Computer Aided Diagnosis of Abdomen Diseases Using Curvelet Transform." International Journal of Image and Graphics 16, no. 03 (July 2016): 1650013. http://dx.doi.org/10.1142/s0219467816500133.

Full text

Abstract:

Abdomen related diseases are responsible of many deaths every year. These deaths can be reduced by early diagnosis of abdomen diseases. Computer aided diagnosis (CAD) can play vital role in early detection of diseases. Hence, a novel CAD is proposed in this paper that can diagnose abdomen diseases like Hepatocellular carcinoma, cysts and Calculi using statistical curvelet texture descriptors. The proposed CAD is divided into four stages: (a) Image segmentation using active contours, (b) feature extraction, (c) feature selection and (d) abdomen disease classification. The regions of interest (ROIs) are segmented from 120[Formula: see text]CT images using active contour models. The statistical features are extracted from segmented ROIs. Further, the classifiers are used to evaluate the ability of feature set in diagnosis various diseases of abdomen. The performance metrics indicates that the proposed CAD achieves accuracy of 87.9% using curvelet coefficient features and neural network as classifier.

APA, Harvard, Vancouver, ISO, and other styles

31

Thilmany, Jean. "Pros and Cons of CAD." Mechanical Engineering 128, no. 09 (September 1, 2006): 38–40. http://dx.doi.org/10.1115/1.2006-sep-3.

Full text

Abstract:

This paper describes the pros and cons of computer-aided design (CAD). CAD packages lack features to easily make the intuitive, complex shapes so pervasive in modern products. It is much easier with CAD to create a part with square features and rectangles and straight lines and round things. Even in a current design project, engineers will sculpt the design in clay, scan it with a digitizer, bring it back into the CAD package, then change it into a solid model and refine that. One of the biggest criticisms of CAD systems is that digital design is slower than sketching and that inhibits the brainstorming process. Today’s systems are not equipped to let engineers play with a design. Engineers start with a basic design and they can change parameters as they draw, but cannot change complete concepts midstream or cut and paste ideas between designs. Most CAD packages include features that track design changes so engineers working collaboratively can see what's been changed, where, when, and why.

APA, Harvard, Vancouver, ISO, and other styles

32

Szecsi, T., and A. S. M. Hoque. "Implementing Manufacturing Features in Mechanical Design." Key Engineering Materials 502 (February 2012): 73–78. http://dx.doi.org/10.4028/www.scientific.net/kem.502.73.

Full text

Abstract:

This paper presents a design system that enables the composition of parts using manufacturing features. Features are selected from feature libraries. Upon insertion, the system ensures that the feature does not contradict the design-for-manufacture rules. This helps eliminating costly manufacturing problems. The system is developed as an extension to a commercial CAD/CAM system Pro/Engineer.

APA, Harvard, Vancouver, ISO, and other styles

33

Wypysiński, Rafał. "Hybrid modeling in CAD." Advanced Technologies in Mechanics 2, no. 1(2) (July 7, 2015): 15. http://dx.doi.org/10.17814/atim.2015.1(2).14.

Full text

Abstract:

Computer aided 3D modeling is rapidly growing field of techniques. Various mod-eling techniques are continuously developed and improved – but hybrid modeling as combination of the best features seems to be worthy of interest. This article de-scribe main principle of full hybrid modeling with examples of practical applications.

APA, Harvard, Vancouver, ISO, and other styles

34

Lechniak, Zbigniew. "Significance of specific CAD/CAM features for the accuracy improvement of ruled surfaces shaped by the WEDM." Mechanik, no. 4 (April 2015): 326/9–326/12. http://dx.doi.org/10.17814/mechanik.2015.4.161.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

LATIF, M. N., R. D. BOYD, and R. G. HANNAM. "Integrating CAD and manufacturing intelligence through features and objects." International Journal of Computer Integrated Manufacturing 6, no. 1-2 (January 1993): 87–93. http://dx.doi.org/10.1080/09511929308944558.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Sonawane, Chandrakant R., and Ghadge Sujit. "Design Intent for CAD Modeling Features Using Boolean Operations." IOP Conference Series: Materials Science and Engineering 197 (May 2017): 012047. http://dx.doi.org/10.1088/1757-899x/197/1/012047.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Zhu, K. P., Y. S. Wong, W. F. Lu, and H. T. Loh. "3D CAD model matching from 2D local invariant features." Computers in Industry 61, no. 5 (June 2010): 432–39. http://dx.doi.org/10.1016/j.compind.2009.11.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Sunil, V. B., and S. S. Pande. "Automatic recognition of features from freeform surface CAD models." Computer-Aided Design 40, no. 4 (April 2008): 502–17. http://dx.doi.org/10.1016/j.cad.2008.01.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Huang, Renhong, Xiaowei Zhang, Sadia Sophia, Zhijun Min, and Xiaojian Liu. "Clinicopathological features and prediction values of HDAC1, HDAC2, HDAC3, and HDAC11 in classical Hodgkin lymphoma." Anti-Cancer Drugs 29, no. 4 (April 2018): 364–70. http://dx.doi.org/10.1097/cad.0000000000000610.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Chen, Wei, Boqiang Liu, Suting Peng, Jiawei Sun, and Xu Qiao. "Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics." International Journal of Biomedical Imaging 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2512037.

Full text

Abstract:

Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

APA, Harvard, Vancouver, ISO, and other styles

41

Bennasar, Mohamed, Duncan Banks, Blaine A. Price, and Attila Kardos. "Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques." JMIR Cardio 4, no. 1 (May 29, 2020): e16975. http://dx.doi.org/10.2196/16975.

Full text

Abstract:

Background Stress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients’ variables including risk factors, current medication, and anthropometric variables has not been widely investigated. Objective This study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes. Methods A machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages: feature extraction, preprocessing, feature selection, and classification stage. A mutual information–based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed. Data from 529 patients were used to train and validate the framework. Patient mean age was 61 (SD 12) years. The data consists of anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, prior diagnosis of CAD, and prescribed medications at the time of the test. There were 82 positive (abnormal) and 447 negative (normal) stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD. Results The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were the features that shared the most information about the outcome of stress echocardiography. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only these three features, we achieved an accuracy of 67.63% with sensitivity and specificity 72.87% and 66.67% respectively. However, for patients with no prior diagnosis of CAD, only two features (sex and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. Conclusions This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.

APA, Harvard, Vancouver, ISO, and other styles

42

Bektas, Jale, Turgay Ibrikci, and Ismail Turkay Ozcan. "Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression." International Journal on Artificial Intelligence Tools 26, no. 06 (December 2017): 1750019. http://dx.doi.org/10.1142/s0218213017500191.

Full text

Abstract:

Coronary Artery Disease (CAD) is very common among the major types of cardiovascular diseases, and there are several studies created with different features including data that is collected from patients for timely diagnosis of CAD. In this study, a dataset with 21 features have been used, and a risk score prediction system has been proposed. The patients were divided into four groups. To determine the effective features of CAD dataset; t-test and Relief-f methods on Logistic Regression Analysis (LRA); Relief-f on Neural Network (NN) feature selection methods were utilized. Sampling methods were used to improve imbalanced form of 4-classed dataset, and the effects of sampling methods were evaluated. Using NN with oversampling and Relief-f feature selection method; the results before the preprocess operations were detected as follows; 72.3% accuracy; after the operations, 84.1% accuracy were achieved with 0.84 sensitivity 0.94 specificity. These statistics obtained from the experiment, by detailed analysis, are the best ones for the CAD data set in this study. Using the feature selection and the sampling methods with the NN substantially improve the prediction accuracy as well as the other metrics. This suggests that these preprocessing methods and the NN may be used together to construct for prediction of the 4-classed imbalanced medical datasets.

APA, Harvard, Vancouver, ISO, and other styles

43

Vannusov, Denis, Vladimir Dadonov, and Maria Tereshchenko. "Organizational features of innovative CAD implementation in existing production systems." MATEC Web of Conferences 311 (2020): 02009. http://dx.doi.org/10.1051/matecconf/202031102009.

Full text

Abstract:

In the article reviewed automatic systems and their life cycle. Analyzed phases of the CAD implementation into civil engineering companies with regard to the factors that suppress their integration. Detailed processes of human-computer interaction during the implementation of innovative CAD technologies. Analyzed the results of this interaction.

APA, Harvard, Vancouver, ISO, and other styles

44

Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "An Ensemble Model With Genetic Algorithm for Classification of Coronary Artery Disease." International Journal of Computer Vision and Image Processing 11, no. 3 (July 2021): 70–83. http://dx.doi.org/10.4018/ijcvip.2021070105.

Full text

Abstract:

Coronary artery disease (CAD) is the most common form of heart disease and has become the primary reason for death. A correct and on-time diagnosis of CAD is very important. Diagnosis of CAD being a strenuous activity, scientists have planned different intelligent diagnostic frameworks for improved CAD diagnosis. Still, low CAD classification accuracy is an issue in these frameworks. In this paper, the authors propose a feature selection technique (FST) that utilizes a genetic algorithm (GA) with J48 classifier as the objective function to choose adequate features for better CAD classification accuracy. After feature removal, classification frameworks are used (i.e., artificial neural network [ANN]) like multilayer perceptron network (MLP), radial basis function network (RBFN), ANN-based ensemble model (ANN-EM), and deep neural network (DNN). Finally, this research proposes an integrated model of GA and ANN-EM for classification of CAD.

APA, Harvard, Vancouver, ISO, and other styles

45

Chen, Qiang, and Yong Mei Yu. "3D CAD Model Retrieval Based on Oriented Bounding Box and Normals Distribution." Applied Mechanics and Materials 496-500 (January 2014): 2324–27. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2324.

Full text

Abstract:

Shape Distribution (SD) describes the global feature of 3D CAD model which can be used to distinguish one model from others. But sometimes, different models may have the similar SD features. In this case SD is not suitable for 3D CAD model retrieval. Although two models have the same SD features, their oriented bounding box (OBB) and normals distribution may differ from each other. In this paper, a new algorithm based on OBB and normals distribution is proposed to tackle this problem. SD feature is used for initial retrieval, then OBB and normals distribution are used for fine retrieval. Experimental results indicate the efficiency and feasibility of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

46

Li, Qiong, and Carol A. Rubin. "Virtual Prototype Design and Test-Simplifying the CAD/Analysis Interface." Applied Mechanics and Materials 284-287 (January 2013): 3473–76. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3473.

Full text

Abstract:

The design of mechanical structural parts is now predominantly a digital process. As an important element of the virtual design cycle, these parts must be tested for their structural integrity using finite element analysis (FEA) software. However, the interface between CAD and FEA is imperfect. The process of preparing CAD models for FEA consumes a great deal of the stress analyst’s time. Existing “automatic” CAD to FEA translators tend to treat all part features as “solid”; this leads to longer computation times and less accurate results for features that can be better characterized as “thin” or “long.” In addition, many features of CAD parts (e.g. fillets and chamfers) are important for their size and shape in the manufactured product, but have relatively little impact on the strength of the part and needlessly complicate the stress analysis—these features are usually removed by the analyst prior to FEA; they may need to be evaluated with additional analyses to test if it is safe to remove them. The Automatic CAD-FEA Interface Project (ACFI), is developing algorithms to make the translation from CAD to FEA seamless and automatic; these algorithms are based on mathematical theory and the principles of theoretical mechanics. This paper presents the latest ACFI advances for (i) automatically evaluating and reworking three dimensional CAD part geometries to prepare them for finite element meshing, (ii) exporting the revised geometries to a preprocessor, and (iii) identifying element type to be associated with each feature geometry. The algorithms used in this work approximate the medial axis transform (MAT) of the CAD part, a “power shape” that represents the three-dimensional solid part. This part can then be evaluated for its geometric properties. This approach has been shown to be a robust method for shape interrogation of three dimensional geometries.

APA, Harvard, Vancouver, ISO, and other styles

47

Hao, Yong Tao, and Yong Min Chi. "ANN-Based Feature Recognition to Integrate CAD and CAM." Applied Mechanics and Materials 55-57 (May 2011): 1269–74. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1269.

Full text

Abstract:

This paper presents an intelligent manufacturing feature extraction method employing artificial neural network techniques. It discuss the subject about how to represent the features as the input expression of the ANN(Artificial Neural Network), how to determine the structure of ANN and the ANN-based feature recognition method. This method is mainly used pre-trained BP neural network to identify the B-rep model representation of the product. Through a lot testing, the validity of the system was verified.

APA, Harvard, Vancouver, ISO, and other styles

48

Li, Jia Wen, Zhi Xuan Pan, Hai Yan Zhang, and Bang De Li. "Railway Bridge CAD Software Program." Advanced Materials Research 403-408 (November 2011): 2893–96. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2893.

Full text

Abstract:

After analyzing the features of all kinds of CAD software program, this paper reaches such a conclusion: most of traditional bridge-design programs focus on calculating structural load, so nowadays the calculating feature of this software programs is greatly improved. At the same time, a common problem is still with traditional bridge-design software programs, and this problem is all this programs don’t have any model that offer information referred to the bridge being designed, and this problem result in isolation of data in different stage of designing. To solve this problem, this paper proposes that railway bridge CAD software program take ACCESS as core database and AUTOCAD as supporting graphical system, and we should develop new software program on this basis. This paper also propose that bridge-design software program we develop in future should take design knowledge and bridge information referred to 3D geometric data in consideration, so that the design software program can reuse the characteristic component databases and all kinds of information in different design stage.

APA, Harvard, Vancouver, ISO, and other styles

49

Wang, Yan. "CAD at the Nano Scale." Mechanical Engineering 136, no. 08 (August 1, 2014): 32–37. http://dx.doi.org/10.1115/1.2014-aug-1.

Full text

Abstract:

This article focuses on the nanotechnology-related research work at Georgina Institute of Technology. The Georgia Institute of Technology’s Multiscale Systems Engineering Research Group is working to integrate the modeling and simulation features of today’s computer-aided design (CAD) with materials design capability. These integrated features would be available at the nano, meso, micro, and macro scales, which is called multiscale CAD. In future CAD systems, engineers will be able to zoom in to specify material morphology and distributions. Offering the capability of designing materials in CAD requires the representation of many different kinds of shapes. The multiscale CAD would also allow engineers to design better functional materials, such as state-change materials. The geometric modeling of microstructures that make up material is still in its infancy. The efficiency and controllability of complex and porous shapes are the most important research topics for the interactive modeling and design of microstructures.

APA, Harvard, Vancouver, ISO, and other styles

50

Tripath, Kirti, Harsh Sohal, and Shruti Jain. "COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR FEATURE-BASED CLASSIFICATION USING HEART RATE VARIABILITY." Biomedical Engineering: Applications, Basis and Communications 32, no. 02 (April 2020): 2050009. http://dx.doi.org/10.4015/s101623722050009x.

Full text

Abstract:

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'CAD Features'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles