A system framework of model quality analysis for product model in collaborative manufacturing

2021 ◽  
Author(s):  
Wei Yang ◽  
Qing Zhao ◽  
Xiaoguang Yan ◽  
Zhuoning Chen
Keyword(s):  
Quality Analysis ◽  
System Framework ◽  
Model Quality ◽  
Product Model ◽  
Collaborative Manufacturing

scholarly journals PSICA: a fast and accurate web service for protein model quality analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W443-W450 ◽  
Author(s):  
Wenbo Wang ◽  
Zhaoyu Li ◽  
Junlin Wang ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Web Service ◽  
Structural Information ◽  
Protein Structures ◽  
Quality Analysis ◽  
Consensus Method ◽  
Model Quality ◽  
Protein Model ◽  
Local Quality ◽  
The Difference

Abstract This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

scholarly journals 3D Interpreted Model: A Novel Product Definition Model by Integrating and Fusing a 3D Annotated Model and Design Knowledge

2021 ◽  
Vol 11 (16) ◽  
pp. 7192
Author(s):  
Yongzhu Hou ◽  
Jihong Liu ◽  
Gaofeng Yue
Keyword(s):  
Product Information ◽  
New Product ◽  
Design Knowledge ◽  
Prototype System ◽  
Model Quality ◽  
Product Model ◽  
Representation Method ◽  
Integrate Design ◽  
Product Definition ◽  
Is Design

Model based definition (MBD) is the core of product digital research and development. The extension of the scope and content of the product model is an important way to improve the application ability of MBD methodology. In view of the demand of the development of the product definition method and the lack of systematic theoretical methods to support the further improvement of product model quality and ability, this paper proposes the concept of a 3D interpreted model as a new product definition model by integrating and fusing design knowledge on the basis of the MBD model. Firstly, the concept, characteristics and representation method of the 3D interpreted model are elaborated. Then, the construction method of the 3D interpreted model is proposed, which includes two steps. The first step is design knowledge annotation, which is used to integrate design knowledge with the MBD model and form the 3D interpreted model in a CAD environment. The second step is information extraction and knowledge fusion, which is used to construct a 3D interpreted model network by processing the product information and design knowledge stored in a STEP file and knowledge index file, respectively. Finally, a prototype system is developed, and the construction process of a 3D interpreted model is demonstrated and verified through an example. The results show that as a new product definition model, a 3D interpreted model realizes the comprehensive integration of a MBD model and design knowledge, so as to realize the inheritance and development of the product definition method, and supports business activities and saves more social resources.

scholarly journals Development of OCIPSE Learning Model to Increase Students’ Scientific Creativity in Natural Science Learning

2020 ◽  
Vol 1 (1) ◽  
pp. 1-18
Author(s):  
Rizqi ◽  
Prabowo ◽  
Tjandra Kirana
Keyword(s):  
High School Students ◽  
Junior High School ◽  
Natural Science ◽  
Science Learning ◽  
Learning Model ◽  
Assessment Instrument ◽  
Quality Analysis ◽  
Quality Data ◽  
Model Quality ◽  
Scientific Creativity

This Research & Development (R & D) has the main goal to develop and produce OCIPSE learning model. The main product of this research is the OCIPSE learning model with five phases, they are 1) Orient and organize the students for study; 2) Collaborative Investigation; 3) Presentation and discussion; 4) Strengthening of scientific creativity; and 5) Evaluate and provide recognition. The OCIPSE learning model’ quality data is obtained through an expert validation process by using the OCIPSE learning model Qualification Assessment Instrument. The OCIPSE learning model quality analysis used an average validity score, single measures ICC, and Cronbach's coefficient alpha. The result of the research shows OCIPSE learning model with average content validity (3.69), construct validity (3.69), with the validity of each aspect statistically in (rα = .92) and reliability in (α = .87).  The results of this study indicate that the developed OCIPSE learning model was declared qualified by experts. The research implication is that a qualified OCIPSE learning model can be used to enhance the scientific creativity of junior high school students in natural science learning. 

Standardization of Image Quality Analysis – ISO 19264

2016 ◽  
Vol 2016 (1) ◽  
pp. 111-116 ◽  
Author(s):  
Dietmar Wueller ◽  
Ulla Bøgvad Kejser
Keyword(s):  
Image Quality ◽  
Quality Analysis ◽  
Image Quality Analysis

Image Quality Analysis of X-ray Grating Interferometer Using Integrating-bucket Method

2020 ◽  
Vol 64 (2) ◽  
pp. 20503-1-20503-5
Author(s):  
Faiz Wali ◽  
Shenghao Wang ◽  
Ji Li ◽  
Jianheng Huang ◽  
Yaohu Lei ◽  
...  
Keyword(s):  
Image Quality ◽  
Phase Contrast ◽  
Quality Analysis ◽  
Phase Contrast Imaging ◽  
Phase Image ◽  
Contrast Imaging ◽  
High Quality ◽  
Image Quality Analysis ◽  
X Ray ◽  
Grating Interferometer

Abstract Grating-based x-ray phase-contrast imaging has the potential to enhance image quality and provide inner structure details non-destructively. In this work, using grating-based x-ray phase-contrast imaging system and employing integrating-bucket method, the quantitative expressions of signal-to-noise ratios due to photon statistics and mechanical error are analyzed in detail. Photon statistical noise and mechanical error are the main sources affecting the image noise in x-ray grating interferometry. Integrating-bucket method is a new phase extraction method translated to x-ray grating interferometry; hence, its image quality analysis would be of great importance to get high-quality phase image. The authors’ conclusions provide an alternate method to get high-quality refraction signal using grating interferometer, and hence increases applicability of grating interferometry in preclinical and clinical usage.

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