Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis

Chun, Eunsuk; Jun, Sungchan; Lee, Chulung

doi:10.3390/su131910709

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…According to the previous research, the smart industry was classified into IT manufacturing (e.g., computer, and electronic and electricity equipment), IT service (e.g., communication, S/W and broadcast) and knowledge service (e.g., education, health and welfare, culture and sports). In order to define the smart industry, this study identified the main technologies applied to the smart city through the smart-x case such as smart car [28], smart building [29], smart farm [30] and smart factory [31]. This smart-x industry expects production to grow exponentially by 2026 [32][33][34][35].…”

Section: Smart City Industrymentioning

confidence: 99%

Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea

Lee

2022

Buildings

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The research agenda on smart cities has increasingly extended not only on perspectives of social–economic relations between technologies and cities but also on the industrial economic ecosystem. The purpose of this study is to focus on an analytical method for the characteristics of a smart city’s ecology and industry. With that thought, we have developed a smart SPIN (Spectrum, Penetration, Impact and Network) model and applied it to analyze the ecology of the Korean smart city industry in general. This model consists of smart spectrum model, smart penetration model, smart impact path model and smart network clustering model. The smart SPIN model shows great potential as an analytical method for the smart city industry ecosystem. As a source of data for analyses from 1960, 1985 and 2015 via input–output table, we revised these data into 25 and 8 industries related to the smart city ecosystem. Additionally, we applied the 2015 GDP deflator. The results of analysis are as follows: First, spectrum, the number of smart industries is increasing. This means that the smart city industry scope and area are expanding. Second, analysis of the smart penetration model and smart ecological industry can be applied into other industries. In other words, traditional industries can crossover and utilize smart technology. Third, with the results of our analysis of the smart impact path model, production paths are increasing while parameter paths did not show a triple parameter path. This means the value chain of the smart city industry is highly divested, but the structure of the industry is weakening. Fourth, smart network analysis shows important clusters to be centered on traditional industries: the clusters do not appear in smart industry centers. This means the impact of the smart city is not strong. Our analysis shows that, today, the Korean industrial ecosystem of smart cities is interacting with existing industries and raising it to a more intelligent and smarter level. Thus, there is a need for this kind of analysis study in order to find optimized smart city industry ecosystem.

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Section: Smart City Industrymentioning

confidence: 99%

Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea

Lee

2022

Buildings

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“…1 In the first quadrant, both TAI and TII indicators are high, which is defined as high level, and 2 in the second and 3 third quadrants, one of the TAI and TII indicators is high and is defined as medium level. Finally, 4 the fourth quadrant is defined as the low level where all indicators are low [108,109].…”

Section: Technology Level Assessmentmentioning

confidence: 99%

“…○ 1 In the first quadrant, both TAI and TII indicators are h high level, and ○ 2 in the second and ○ 3 third quadrants, one o tors is high and is defined as medium level. Finally, ○ 4 the fourt the low level where all indicators are low [108,109]. Trend analysis aims to predict the future of technology.…”

Section: Technology Level Assessmentmentioning

confidence: 99%

Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap

et al. 2022

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The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. By using the Word2Vec algorithm, we will investigate the patent search formula that reflects the trend, the prediction of changes in logistics technology through LDA (Latent Dirichlet Allocation) clustering of patent data, and the derivation of vacant technology by experimental methods. The proposed framework is expected to be utilized for predicting technological change and deriving promising technologies for establishing technology roadmaps in logistics companies.

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“…Qualitative evaluation is a method of evaluating the relative level of technology for information on patent data [70]. We can identify the technology level by applying the technology mapping analysis [71]. In addition, through quantitative evaluation, the indicator of centrality and time series were extracted from each detailed IPC technology area, and the results of the analysis were confirmed through social network analysis and time series analysis [72,73].…”

Section: Technology Level Evaluation Of Ipcmentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis

Cited by 12 publications

References 53 publications

Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea

Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea

Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

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