scholarly journals Implementation of Data Flow, Predictive Model, and Data Visualization in Corbion Process Analysis System

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Sie Ivan Hinman Siehoyono
Keyword(s):  
Predictive Model ◽  
Data Visualization ◽  
Data Flow ◽  
Process Analysis ◽  
Analysis System

Design of Survey Analysis System Based on Data Visualization

2011 ◽  
Vol 403-408 ◽  
pp. 1491-1494
Author(s):  
Wei Yu
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Social Science ◽  
Data Visualization ◽  
The Internet ◽  
Survey Analysis ◽  
Science Study ◽  
Social Science Study ◽  
Analysis System ◽  
Three Phases

Questionnaire Survey is one of the most popular methods in Social Science study, the process of which consists of three phases: data collection, analysis and representation. At present, the practical operations of the three phases are still backward, and the methods used in data analysis and statistic are still simple. This paper designed a Survey Analysis System based on data visualization, which can not only realize survey on the internet, but also brought data visualization into the phases of data analysis and representation, so as to help users to obtain and operate data visually and handily.

Fire risk zone analysis system using a predictive model for time and resource optimization

2021 ◽  
Author(s):  
Julio Cesar Cardenas Suca ◽  
Diego Jesus Tapia Medina ◽  
Daniel Alejandro Subauste Oliden
Keyword(s):  
Predictive Model ◽  
Fire Risk ◽  
Resource Optimization ◽  
Risk Zone ◽  
Analysis System

Fracture Mechanics Analysis of GI All-Ceramic Crowns

2013 ◽  
Vol 750-752 ◽  
pp. 529-532
Author(s):  
Cheng Fan
Keyword(s):  
Clinical Application ◽  
Process Analysis ◽  
Failure Process ◽  
Chemical Properties ◽  
Ceramic Materials ◽  
All Ceramic ◽  
Ceramic Crowns ◽  
Analysis System ◽  
Physical And Chemical ◽  
Ceramic Crown

Dental ceramic materials have approximate color and translucency with natural tooth, which is unmatched by other restorative materials. Because of its beautiful appearance, good physical and chemical properties, all-ceramic crown restorations are more widely used., However, due to the brittleness of ceramics and the stress mismatch between different materials, dropping or fracture phenomenon of porcelain veneer is often occurred in clinical application during the service period of all-ceramic crowns. The porcelain veneer failure mechanism is still not very clear, in this paper, the force performance of all-ceramic crowns is analyzed using the RFPA (realistic failure process analysis) system. The crack initiation, propagation and failure process of all-ceramic crown can be clearly observed and the research results provide guidance for clinical application

scholarly journals Data-flow-based adaption of the System-Theoretic Process Analysis for Security (STPA-Sec)

2021 ◽  
Vol 7 ◽  
pp. e362
Author(s):  
Jinghua Yu ◽  
Stefan Wagner ◽  
Feng Luo
Keyword(s):  
Information Security ◽  
Data Flow ◽  
Process Analysis ◽  
Security Analysis ◽  
Security Requirements ◽  
Security Issues ◽  
External Threats ◽  
Complex Interactions ◽  
Controlling System ◽  
Potential System

Security analysis is an essential activity in security engineering to identify potential system vulnerabilities and specify security requirements in the early design phases. Due to the increasing complexity of modern systems, traditional approaches lack the power to identify insecure incidents caused by complex interactions among physical systems, human and social entities. By contrast, the System-Theoretic Process Analysis for Security (STPA-Sec) approach views losses as resulting from interactions, focuses on controlling system vulnerabilities instead of external threats, and is applicable for complex socio-technical systems. However, the STPA-Sec pays less attention to the non-safety but information-security issues (e.g., data confidentiality) and lacks efficient guidance for identifying information security concepts. In this article, we propose a data-flow-based adaption of the STPA-Sec (named STPA-DFSec) to overcome the mentioned limitations and elicit security constraints systematically. We use the STPA-DFSec and STPA-Sec to analyze a vehicle digital key system and investigate the relationship and differences between both approaches, their applicability, and highlights. To conclude, the proposed approach can identify information-related problems more directly from the data processing aspect. As an adaption of the STPA-Sec, it can be used with other STPA-based approaches to co-design systems in multi-disciplines under the unified STPA framework.

scholarly journals 2E09 A vascular CFD analysis system based on 4D Flow velocimetry : Part 2 : data flow and processing

2013 ◽  
Vol 2013.25 (0) ◽  
pp. 401-402
Author(s):  
Toshiyasu SHIMIZU ◽  
Takashi KOSUGI ◽  
Yuki ONISHI ◽  
Kouhei AOKI ◽  
Haruo ISODA ◽  
...  
Keyword(s):  
Data Flow ◽  
Cfd Analysis ◽  
4D Flow ◽  
Flow Velocimetry ◽  
Analysis System

scholarly journals Process analysis of data flow priority management in software systems

2019 ◽  
Vol 62 (3) ◽  
pp. 208-211
Author(s):  
T.A. Egorova ◽  
L.A. Muraveva-Vitkovskaia ◽  
Li Shijia
Keyword(s):  
Data Flow ◽  
Process Analysis ◽  
Software Systems

scholarly journals Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3

2021 ◽  
Vol 251 ◽  
pp. 02009
Author(s):  
Ioan-Mihail Stan ◽  
Siarhei Padolski ◽  
Christopher Jon Lee ◽  
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Hadron Collider ◽  
Machine Learning Algorithms ◽  
Main Role ◽  
Scientific Experiments ◽  
Self Service ◽  
Analysis System ◽  
Learning Analysis ◽  
Computing Infrastructure

A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning algorithms, the BigPanDA community desires to expand the platform’s capabilities and fill the gap between Machine Learning processing and data visualization. In this regard, BigPanDA partially adopts the cloud-native paradigm and entrusts the data presentation to MLFlow services running on Openshift OKD. Thus, BigPanDA interacts with the OKD API and instructs the containers orchestrator how to locate and expose the results of the Machine Learning analysis. The proposed architecture also introduces various DevOps-specific patterns, including continuous integration for MLFlow middleware configuration and continuous deployment pipelines that implement rolling upgrades. The Machine Learning data visualization services operate on demand and run for a limited time, thus optimizing the resource consumption.

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