scholarly journals Application of Artificial Intelligence and Collaborative Knowledge for Manufacturing Design

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Tingting Luo ◽  
Guangyao Li ◽  
Naijiang Yu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Decision Support ◽  
Collaborative Design ◽  
Product Life Cycle ◽  
Manufacturing Industry ◽  
Product Information ◽  
Rapid Development ◽  
Influence Coefficient ◽  
Sensitivity Index

With the rapid development of science and technology, digital technology has brought the world economy and management into a new stage. Collaborative design can realize product design process by cross-regional and cross-industry designers and share and exchange product information through network. With the rapid development of big data and artificial intelligence, knowledge services have gradually developed into multirole collaborative design activities based on artificial intelligence decision support. Traditional manufacturing industry has gradually transformed into modern manufacturing service industry after integrating information technology means such as Internet, communication, computer, and modern management methods. This article focuses on artificial intelligence decision support systems and the complex product manufacturing industry. We present a detailed analysis of how to integrate the knowledge generated by the product life cycle in the era of big data. We calculate the influence coefficient and sensitivity index of four different industries and propose a metadata architecture to improve the value of products as a whole. The findings of the research study imply that a knowledge-based collaborative platform should be designed by the enterprises and industries and a platform-based construction approach for economical, practical, and reliable production. We also present a detailed discussion about other factors such as the network cost of symmetric services, raw data and forecast data, and the number of nodes and the processing complexity.

scholarly journals Information Fusion for Multi-Source Material Data: Progress and Challenges

2019 ◽  
Vol 9 (17) ◽  
pp. 3473 ◽  
Author(s):  
Zhou ◽  
Hong ◽  
Jin
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Fusion ◽  
Manufacturing Industry ◽  
Data Representation ◽  
Source Material ◽  
Future Research ◽  
Unified Framework ◽  
Open Problems ◽  
Material Data

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

A Translator for Converting CAD Models to Second Life

2010 ◽  
Author(s):  
Ratnadeep Paul ◽  
Sam Anand
Keyword(s):  
Virtual Worlds ◽  
Second Life ◽  
Product Life Cycle ◽  
Manufacturing Industry ◽  
Product Information ◽  
Life Cycle Management ◽  
3D Models ◽  
Transfer Product ◽  
Cad Models ◽  
Business Entities

Product Life-cycle Management (PLM) has been one of the single most important techniques to have been developed in the manufacturing industry. The increasing capabilities of internet and the ever increasing dependence of business entities on internet have led to the development of metaverses — internet-based 3D virtual worlds — which act as business platforms where companies display and showcase their latest products and services. This is in turn has led to a demand for development of methods for the easy transfer of data from stand alone PLM systems to the internet based virtual worlds. This paper presents the development of a translator which will transfer product data of 3D models created in CAD systems to an internet based virtual world. This translator uses a faceted-surface approach to transfer the product information. In this work CAD models were converted to a CAD-neutral data format, JT file format, and finally recreated in the metaverse Second Life (SL). Examples of models translated from JT to SL have been presented. A technique known as prim optimization, which increases the efficiency of the translation was also incorporated in the algorithm for the translator. Examples of prim optimization have been provided in the paper.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals A Study on the Design of Children Intelligent Guardianship Product Based on Artificial Intelligence Technology

2020 ◽  
Vol 179 ◽  
pp. 02050
Author(s):  
Yan-Xia Qu ◽  
Ming-Feng Wang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Child Care ◽  
Design Process ◽  
Rapid Development ◽  
Parental Supervision ◽  
Parental Experience ◽  
Artificial Intelligence Technology ◽  
Intelligent Child ◽  
The Way

The rapid development of AI has affected the design process. The ability to analyze big data and AI’s efficiency, rapidity will bring great changes to the monitoring products especially for children. At present, the vast majority of intelligent child care products are based on the parental experience, designed in the aspect of parental supervision, and the children who use the product are often neglected. So change the way of designing, from the perspective of children using Intelligence technology, the ultimate child care products can play the most important role.

scholarly journals Digital Technologies for Assessing the Credit Capacity of the Bank’s Clients

2021 ◽  
Vol 8 (12) ◽  
pp. 566
Author(s):  
Viktor Ivanovich Abramov ◽  
Azizbek Kurbonov
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Predictive Analytics ◽  
Rapid Development ◽  
Digital Technologies ◽  
Global Competition ◽  
Credit Risks ◽  
Reducing Costs

In modern conditions of global competition and the rapid development of digital technologies, there is a need for new tools for assessing the solvency of bank customers and reducing credit risks, reducing costs and increasing the profitability of the bank. The features and prospects of using big data and predictive analytics are analyzed, theoretical aspects of using Artificial intelligence (AI) technologies are considered and their advantages for banks are analyzed. The goal is to reduce the share of problem loans and quickly determine the solvency of clients.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Intelligent Sports Training System Based on Artificial Intelligence and Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunguang Li ◽  
Jianbiao Cui
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Body ◽  
Joint Angle ◽  
Rapid Development ◽  
Human Motion ◽  
Training System ◽  
Human Motion Analysis ◽  
Sports Training ◽  
Organizational System

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

scholarly journals RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 82-93
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Alan Tadeu Moraes Moraes ◽  
Paulo Sergio Gonçalves de Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Analytical Techniques ◽  
Strong Relationship ◽  
Decision Making Processes ◽  
Scopus Database

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works. 

Is Artificial Intelligence a New Dawn or Challenge for Corporate Decision Making?

2019 ◽  
pp. 20-42
Author(s):  
Maria Igorevna Nikishova ◽  
Mikhail E. Kuznetsov
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Decision Making Process ◽  
Fourth Industrial Revolution ◽  
Stakeholder Interaction ◽  
The Board Of Directors

The Fourth Industrial Revolution provides companies with new opportunities, and business picks up allies represented by technologies that can change mechanisms of corporate decision making in corporations. Rapid development of technologies, which allows working more efficiently with information, can lead to the creation of a new system of stakeholder interaction, thanks to better analytics, transparency, and speed of decisions. In this regard, the analyst based on big data with the use of artificial intelligence (AI) is able to significantly affect the quality of decisions. How can the application of AI for analysis of big data be able to influence the decision-making process and to what extent can it influence the system of corporate relationships? To answer this question, the authors will try to describe how transformation of decision-making methodology at the Board of Directors level under the influence of the Fourth Industrial Revolution and the development of AI technologies and big data, and what are the opportunities, limitations, and risks of the decision-making process with AI.

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