1807 Artificial Intelligence (AI) Tools for Data Acquisition and Probability Risk Analysis of Structure Databases

2009 ◽  
Vol 2009.22 (0) ◽  
pp. 216-217
Author(s):  
Nobuki Yamagata ◽  
Pedro Marcal ◽  
Jeffrey Fong
Keyword(s):  
Artificial Intelligence ◽  
Risk Analysis ◽  
Data Acquisition ◽  
Structure Databases

Artificial intelligence treating the problem of uncertainty in quantitative risk analysis (QRA)

2019 ◽  
Vol 18 (1) ◽  
pp. 40-54
Author(s):  
Mohamed Seddik Hellas ◽  
Rachid Chaib ◽  
Ion Verzea
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Risk Analysis ◽  
Lung Damage ◽  
Whole Body ◽  
Individual Risk ◽  
Content Type ◽  
Quantitative Risk Analysis ◽  
Accident Scenario ◽  
Imperfect Knowledge

Purpose Nowadays, artificial intelligence computational methods, such as knowledge-based systems, neural networks, genetic algorithms and fuzzy logic, have been increasingly applied to several industrial research studies, the purpose of this paper is to study the contribution of fuzzy and possibilistic techniques to quantitative risk analysis (QRA) in the presence of imperfect knowledge about the occurrence and consequences of accidental phenomena. Design/methodology/approach To solve the problem of uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences, the authors used fuzzy logic. Using this type of analysis, it is possible to visualize the contours of the dead or fuzzy injury by fireball thermal effect (first- and second-degree burn, death) and lesions caused by vapor cloud explosion overpressure (lung damage, eardrum rupture, head impact, whole-body displacement). The frequency and severity of fuzzy results are calculated by extended multiplication using the alpha-cuts method. Findings This research project aims to reflect the real situation in the in Amenas industrial area (SONATRACH company), specifically the liquefied petroleum gas storage tank On-Spec 05-V-411A, to deal with this type of risk. Using this analysis allows us to estimate the fuzzy individual risk using the approach of fuzzy logic to treating this uncertainty in the parameter information of accident scenarios. This index individuel risk (IR) was evaluated against the criterion of acceptability and then used for decision-making in the field of industrial risk analysis and evaluation. Originality/value The originality of the work is to identify the weak points of the classical QRA to solve the problem of the uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences to visualize the fuzzy risk contours. On the one hand and the development of software to calculate the probability of death by the overpressure effect and classify the most sensitive organs on the other hand. Given the importance of this study, it can be generalized for similar sites in the region.

scholarly journals Chemical Reaction Spectrum

2018 ◽  
Author(s):  
Jianchao Lee ◽  
Jianghong Li ◽  
Qiannan Duan ◽  
Sifan Bi ◽  
Ruen Luo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
Chemical Reaction ◽  
Chemical Characterization ◽  
Important Data ◽  
Data Set ◽  
Characterization Methods ◽  
Graphical Data ◽  
Reaction Characteristics ◽  
Reaction Spectrum

We proposed a new method of chemical reaction spectrum (CRS) in terms of chemical characterization, and established a method to fulfill it by combining with 3D chemical printing technology and 2D sampling. The CRS can provide a graphical data set for pure or mixed substances, which can comprehensively describe the reaction characteristics of the research object. Compared with common characterization methods (NMR, UV/vis, IR, Raman, GC or LC), it is more capable of revealing chemical behaviors enough, and is much lower in cost. It is expected to be an important data acquisition approach for the application of artificial intelligence in the field of chemistry in the future.

scholarly journals The application of artificial intelligence to pest risk analysis

2002 ◽  
Vol 55 ◽  
pp. 430-430
Author(s):  
L. Peacock ◽  
S.P. Worner ◽  
S. Samarasinghe
Keyword(s):  
Artificial Intelligence ◽  
Risk Analysis ◽  
Pest Risk Analysis ◽  
Pest Risk

scholarly journals Use of artificial intelligence in computed tomography dose optimisation

2020 ◽  
Vol 49 (1_suppl) ◽  
pp. 113-125
Author(s):  
C.H. McCollough ◽  
S. Leng
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Radiation Dose ◽  
Data Acquisition ◽  
Patient Positioning ◽  
Modern Society ◽  
Reconstruction Algorithms ◽  
Technical Advances ◽  
Increased Sensitivity ◽  
Acquisition Processes

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.

scholarly journals Chemical Reaction Spectrum

2018 ◽  
Author(s):  
Jianchao Lee ◽  
Jianghong Li ◽  
Qiannan Duan ◽  
Sifan Bi ◽  
Ruen Luo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
Chemical Reaction ◽  
Chemical Characterization ◽  
Important Data ◽  
Data Set ◽  
Characterization Methods ◽  
Graphical Data ◽  
Reaction Characteristics ◽  
Reaction Spectrum

We proposed a new method of chemical reaction spectrum (CRS) in terms of chemical characterization, and established a method to fulfill it by combining with 3D chemical printing technology and 2D sampling. The CRS can provide a graphical data set for pure or mixed substances, which can comprehensively describe the reaction characteristics of the research object. Compared with common characterization methods (NMR, UV/vis, IR, Raman, GC or LC), it is more capable of revealing chemical behaviors enough, and is much lower in cost. It is expected to be an important data acquisition approach for the application of artificial intelligence in the field of chemistry in the future.

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