Artificial Intelligence to Optimize Melting Processes: An Approach Combining Data Acquisition and Modeling

2019 ◽  
pp. 1159-1164
Author(s):  
Amin Rostamian ◽  
Stéphane Lesquereux ◽  
Marc Bertherat ◽  
Michel Rappaz
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
Combining Data

scholarly journals Deep neural network: Recognize Data Management of Artificial Intelligence in Retail

2019 ◽  
Vol 8 (10) ◽  
pp. 3594-3596
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Data Management ◽  
Deep Neural Networks ◽  
Data Management System ◽  
Research Approach ◽  
Vast Amount ◽  
Retail Business ◽  
Combining Data ◽  
Hidden Layer

Deep neural networks with the artificial intelligence on Machine Learning (ML) algorithms constitute the best design specifically to deal with vast amount of data for retail business. The limited research approach is referred towards reducing memory consumption on integrating ML algorithms on data management system. This paper proposed combining data management and deep neural networks, ideas to build systems, which vast amount data can share in the database system. Therefore, ML algorithm has a pattern with multi-hidden layer that can use to synthesis different decision within a minimum processing. Finally, system precede and follow a NoSQL layers of a model employs in-memory database compression techniques and executes data management challenges with large datasets successfully.

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.

Profiting From Digital Transformation?

2021 ◽  
Vol 12 (5) ◽  
pp. 68-79
Author(s):  
Ulrich Lichtenthaler
Keyword(s):  
Artificial Intelligence ◽  
Digital Transformation ◽  
Superior Performance ◽  
Business Operations ◽  
Combining Data ◽  
New Knowledge ◽  
Business Opportunities ◽  
Recent Trends ◽  
Advanced Analytics

Many companies have recently started digital transformation initiatives, and they now increasingly focus on artificial intelligence (AI). By means of smart algorithms and advanced analytics, firms attempt to leverage some of the results of their ongoing digital transformation initiatives, for example with regard to data about their established business operations. A conceptual framework underscores the need for combining data management and AI initiatives in order to ensure a firm's digital readiness and to realize digital business opportunities subsequently. An overview of recent trends further illustrates how different companies respond to these managerial challenges. This paper contributes to the literature on digitalization, AI, and ‘integrated intelligence' by highlighting the role of AI for leveraging data from digital transformation initiatives. Specifically, the use of AI applications helps companies to turn data into valuable knowledge and intelligence. In addition, this paper provides new knowledge about achieving superior performance in the digital economy.

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

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.

Enhanced LV supervision by combining data from meters, secondary substation measurements and medium voltage supervisory control and data acquisition

2017 ◽  
Vol 2017 (1) ◽  
pp. 1089-1093 ◽  
Author(s):  
Nicholas Etherden ◽  
Anders Kim Johansson ◽  
Ulf Ysberg ◽  
Kjetil Kvamme ◽  
David Pampliega ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Supervisory Control ◽  
Medium Voltage ◽  
Combining Data

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 UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence

2019 ◽  
Vol 11 (4) ◽  
pp. 410 ◽  
Author(s):  
Yiannis Ampatzidis ◽  
Victor Partel
Keyword(s):  
Artificial Intelligence ◽  
Data Acquisition ◽  
High Throughput ◽  
Multispectral Imaging ◽  
Phenotypic Trait ◽  
Phenotypic Characteristics ◽  
Individual Tree ◽  
High Throughput Phenotyping ◽  
Citrus Varieties ◽  
Canopy Size

Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.

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