Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis

2021 ◽  
Author(s):  
Soyea Lee ◽  
Junseok Hwang ◽  
Eunsang Cho
Keyword(s):  
Artificial Intelligence ◽  
Network Analysis ◽  
Clustering Analysis ◽  
Industrial Sectors ◽  
Technology Convergence

scholarly journals Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

2020 ◽  
Vol 12 (20) ◽  
pp. 8667
Author(s):  
Xi Yang ◽  
Xiang Yu
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Network Analysis ◽  
Network Model ◽  
Industrial Development ◽  
Research Collaboration ◽  
New Technology ◽  
Technological Field ◽  
Electric Equipment ◽  
Multi Level

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

scholarly journals Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

2020 ◽  
Vol 2020 ◽  
pp. 1-45 ◽  
Author(s):  
Ocident Bongomin ◽  
Aregawi Yemane ◽  
Brendah Kembabazi ◽  
Clement Malanda ◽  
Mwewa Chikonkolo Mwape ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Industrial Sector ◽  
Disruptive Technology ◽  
Logistics Industry ◽  
Disruptive Technologies ◽  
Industrial Sectors ◽  
Agriculture Industry ◽  
Education Industry ◽  
Technology Convergence

Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

scholarly journals BOUNDARY RE-ASSESSMENT OF THE METER READING UNIT (MRU) FOR TENAGA NASIONAL BERHAD, TNB CHERAS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 729-736
Author(s):  
A. Zulkflee ◽  
M. F. Abdul Khanan ◽  
H. A. Umar ◽  
M. Z. Abdul Rahman ◽  
F. Nik Mohd Kamil
Keyword(s):  
Network Analysis ◽  
Spatial Data ◽  
Clustering Analysis ◽  
Geographic Information ◽  
Spatial Data Analysis ◽  
Reading Processes ◽  
Gis Applications ◽  
Service Efficiency ◽  
Distribution Company ◽  
Growing Population

Abstract. The propensity of Geographic information systems (GIS) as a modelling tool for problems solving and spatial data analysis is remarkable and has been widely used in recent times. GIS applications has been utilized by Tenaga Nasional Berhad (TNB) in solving numerous problems. However, there are other units within the TNB where GIS can be extended and incorporated in order to improve service efficiency, reliability and operational success. Remote meter reading (RMR) technology has been successfully extended and maintain by TNB to more than 70% of highly consumed customers, which tremendously assist in improving meter reading processes and curtailed technical losses. But growing population within the cities and surrounding villages has been the major issue regarding bill distribution which the giant electrical distribution company must address squarely and timely. This paper therefore, employed the application of Geographic Information System in re-assessing the boundaries for meter reading units (MRU) using spatial analysis in order to reduced cost, travel time as well as other logistic requirements regarding billing distribution by TNB personnel. Clustering and network analysis has been employed to generate the new MRU boundary for TNB. The result of clustering analysis has been used to consider the location of customers in new suggested group of clusters, while the network analysis result generates shorter distance routes for meter reading. The results of the analysis can be validated with the analysis graph, where the patterns of the distance are compared in a graph which shows the distance generated from the new suggested cluster of new MRU boundary decrease incrementally compared with the distance of existing MRU routes.

scholarly journals Research on the Structural Characteristics of Entertainment Industrial Correlation in China: Based on Dual Perspective of Input-Output and Network Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Xun ◽  
Wensheng Shi ◽  
Tianyu Liu
Keyword(s):  
Network Analysis ◽  
Industrial Development ◽  
Structural Characteristics ◽  
Rapid Development ◽  
Correlation Network ◽  
Entertainment Industry ◽  
Input Output ◽  
Positive Role ◽  
Industrial Sectors ◽  
Dual Perspective

Based on the dual perspective of input-output and network analysis, this study takes typical industrial sectors of China’s entertainment industry as representatives. Through the input-output analysis of industrial correlation characteristic indicators and construction of an industrial correlation network, we conduct a systematic and quantitative study on the entertainment industrial correlation characteristics and structural characteristics of the industrial correlation network in China. Furthermore, we clarify the role of the entertainment industry in China’s industrial development and its positioning in China’s whole industrial correlation network. We have the following key findings: China’s entertainment industry as a whole shows the characteristics of final demand-oriented industries, whose rapid development plays a certain positive role in boosting consumption and driving economic growth. Within the whole industrial correlation network in China, there is frequent interaction between the entertainment industry and other industry sectors within the directly related network; it will especially exert obvious radiation and driving effect on the upstream industry. However, limited by the scale of the direct industrial correlation network, such influence is still difficult to achieve the common development of most industries in China.

scholarly journals Global gene network exploration based on explainable artificial intelligence approach

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241508
Author(s):  
Heewon Park ◽  
Koji Maruhashi ◽  
Rui Yamaguchi ◽  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Artificial Intelligence ◽  
Network Analysis ◽  
Gene Networks ◽  
Gene Network ◽  
Multilayer Networks ◽  
Mesenchymal Transition ◽  
Regulatory Pathways ◽  
Regulatory Systems ◽  
Explainable Ai ◽  
Gene Regulatory

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

scholarly journals Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Haotian Hu ◽  
Dongbo Wang ◽  
Sanhong Deng
Keyword(s):  
Artificial Intelligence ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Collaboration ◽  
Edge Weight ◽  
Global Collaboration ◽  
The Us ◽  
Collaboration Patterns ◽  
The Uk

AbstractPurposeThis study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research.Design/methodology/approachWe selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis.FindingsThe bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups.Research limitationsFirst, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implicationsThe findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research.Originality/valueThis work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.

scholarly journals PARAMETERS FORECASTING OF LASER WELDING BY THE ARTIFICIAL INTELLIGENCE TECHNIQUES

2018 ◽  
Vol 16 (2) ◽  
pp. 193 ◽  
Author(s):  
Vlastimir Nikolić ◽  
Miloš Milovančević ◽  
Dalibor Petković ◽  
Dejan Jocić ◽  
Milan Savić
Keyword(s):  
Artificial Intelligence ◽  
Laser Welding ◽  
Practical Importance ◽  
Welding Process ◽  
Geometrical Parameters ◽  
Machining Parameters ◽  
Welding Quality ◽  
Industrial Sectors ◽  
Welding Joints ◽  
Laser Welding Process

Laser welding process is used in many industrial sectors. One of the most important aspects of the laser welding quality refers to the geometrical and mechanical properties of welding joints. In order to develop optimal conditions for the laser welding process it is desirable to know in advance which machining parameters to select. Though there are manuals which recommend specific parameters combinations for the desired laser welding quality it is difficult to cover all possible combinations because of the process nonlinearity. Therefore, in this study the main aim is to establish an algorithm for optimal parameters forecasting of the laser welding process. The algorithm is based on an artificial intelligence approach. The main goal is to forecast the geometrical parameters of the welding joints like front width, front heights, back width and back heights of the welding joints. Experimental process was performed in order to acquire training and testing data of the laser welding process. The obtained results could be of practical importance for engineers in industry.

Sign in / Sign up

Export Citation Format

Share Document