An application to Stereolithography of a feature recognition algorithm for manufacturability evaluation

2018 ◽  
Vol 31 (1) ◽  
pp. 199-214 ◽  
Author(s):  
Giampaolo Campana ◽  
Mattia Mele
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Manufacturability Evaluation

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

A feature complexity index

1998 ◽  
Vol 212 (5) ◽  
pp. 405-412 ◽  
Author(s):  
G Little ◽  
R Tuttle ◽  
D E R Clark ◽  
J Corney
Keyword(s):  
Feature Recognition ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Solid Model ◽  
Geometric Complexity ◽  
Relative Complexity ◽  
Complexity Index

An index is presented for quantifying the geometric complexity of a three-dimensional solid model. This provides a measure by which components may be compared one with another in relation to their relative complexity. The index is alphanumeric and readily computable. Such an index can be of use in the field of feature recognition as a means to determine how efficiently one algorithm handles components of varying complexity compared to another such algorithm. The performance of the authors’ own feature recognition algorithm is tested against components of differing complexity as determined by the index.

scholarly journals Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingchao Li
Keyword(s):  
Classroom Behavior ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Psychological Characteristics ◽  
Classroom Teaching ◽  
Analysis Model ◽  
Characteristic Extraction ◽  
Psychological Feature ◽  
Extraction Algorithm ◽  
Systemic Model

Contemporary classroom teaching requires the combination of students’ classroom behavior and their psychological activities and appropriately changes the teaching mode according to students’ psychological characteristics. This paper analyzes the traditional characteristic recognition algorithm, and after improving its deficiencies, an improved characteristic extraction algorithm is proposed, based on the actual situation of classroom learning. This new algorithm can effectively improve the students’ psychological feature prediction; with the support of this algorithm, a comprehensive analysis model with classroom behavior recognition and psychological feature recognition is constructed; also, the functional structure of the system is built up. Through experimental research, the model proposed in this paper is analyzed, and the experimental data has approved that the systemic model could play an important role in classroom teaching.

Performing DFM Analysis in Multiple Domains Using Interchangeable Manufacturable Entities

1997 ◽  
Author(s):  
Subramanian Krishnan ◽  
Edward B. Magrab
Keyword(s):  
Manufacturing Process ◽  
Feature Recognition ◽  
Integrated Design ◽  
Integrated System ◽  
New Approach ◽  
Small Set ◽  
Multiple Domains ◽  
Shape Characteristics ◽  
Manufacturability Evaluation ◽  
Made In

Abstract An integrated design for manufacture system that performs manufacturability analysis simultaneously in two domains using interchangeable process specific entities is presented. This new approach is illustrated by an integrated system that creates and evaluates parts for manufacture by injection molding using a C-entity, and simultaneously creates and evaluates its mold for manufacture by milling using the fundamental manufacturing entity for milling. The entity for the part manufacturing process is chosen as the complement of the entity for the mold manufacturing process. This obviates the need for converting features from one manufacturing domain to another, and also permits a single uniform data structure to capture the shape characteristics of parts made in the two manufacturing domains. Since all the shapes are represented in the same way, the manufacturability evaluation of the part and the mold is reduced to a small set of algorithms based on an evaluation of the entity’s profile. It is demonstrated that by using process specific entities one can perform manufacturability evaluation of a part without either feature extraction or designing with features. The advantages of using the process specific entities approach for design and manufacturability evaluation over the feature recognition and the design by features approach are enumerated.

Vehicle license plate recognition method based on deep convolution network in complex road scene

2019 ◽  
Vol 233 (9) ◽  
pp. 2284-2292
Author(s):  
Ze Liu ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Hai Wang ◽  
Youguo He
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Depth Analysis ◽  
Robust Recognition ◽  
Character Feature ◽  
Deep Convolution Network ◽  
Nonlinear Operation ◽  
Better Than

The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.

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