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Patent Analysis of the Development of Technologies Applied to the Combustion Process. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The use of combustion in industrial activity is of paramount importance for economic and social development. However, combustion reactions are the main sources of atmospheric pollutant emissions. Given this reality, it is necessary to study new combustion techniques, such as the application of oxygen in the process, in order to increase the efficiency and productivity of the burning process and energy production. In addition, studies have reported the use of acoustic excitation, a low-investment technique that can promote higher rates of heat and mass transfer. Thus, the goal of this study was to bring data on the current scenario related to the application of these two technologies to the combustion process where, through the reported results, they can be used as a guide for companies’ decisions about new technologies and global trends to be identified. For this, a technological prospection was carried out which focused on patents to investigate the use of oxygen-enhanced combustion and acoustic excitation coupled to the combustion process; a total of 88 documents were found. Few documents applied acoustic excitation for process improvement, indicating that its use is recent; however, according to the literature, it is a promising field to be explored. Siemens AG was the main depositor, and ten primary inventors were identified. Germany and the United States were the countries with the highest number of filings. In the prospected documents, it was possible to identify that there is a need for the further investigation of the joint use of both techniques. These investigations may lead to the development of processes and devices that can provide economic and environmental gains for the energy industry.
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Liu X, Wang S, Yao K, Sun R. Opportunistic behaviour in supply chain finance: a social media perspective on the ‘Noah event’. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.1878392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Xiaohong Liu
- Business School, Central University of Finance and Economics, Beijing, China
| | - Shiyun Wang
- Business School, Central University of Finance and Economics, Beijing, China
| | - Kai Yao
- Business School, Central University of Finance and Economics, Beijing, China
| | - Ruiqing Sun
- Business School, Central University of Finance and Economics, Beijing, China
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Assessment and Selection of Technologies for the Sustainable Development of an R&D Center. SUSTAINABILITY 2020. [DOI: 10.3390/su122310087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The central role of R&D centers in the advancement of technology within industrial enterprises is undeniable and clearly affects their strategies, their competitiveness and their business sustainability. R&D centers assume responsibility for technology recognition, collection, acquisition, development and transition. Among their activities, the efficient choice of emerging technologies in the Technology Management Process is becoming a real challenge. In such heterogeneous scenarios, Multiple Criteria Decision Making (MCDM) models are commonly proposed as an appropriate decision-making approach. Multiple research works address the selection of particular technologies in industrial applications, but very few references can be found related to research institutions, and R&D centers in particular. Therefore, a decision-making model is provided in this study following the MIVES multi criteria method for the assessment of one or more technologies. The model is then applied to two case studies related to the selection process of new technologies at a Spanish R&D Center specialized in manufacturing.
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Abstract
This paper proposes a multi-class classification model for technology evaluation (TE) using patent documents. TE is defined as converting technology quality to its present value; it supports efficient research and development using intellectual property rights–research & development (IP–R&D) and decision-making by companies. Through IP–R&D, companies create their patent portfolios and develop technology management strategies. They protect core patents and use those patents to cooperate with other companies. In modern society, as conversion technology has been rapidly developed, previous TE methods became difficult to apply to technology. This is because they relied on expert-based qualitative methods. Qualitative results are difficult to use to guarantee objectivity. Many previous studies have proposed models for evaluating technology based on patent data to address these limitations. However, those models can lose contextual information during the preprocessing of bibliographic information and require a lexical analyzer suitable for processing terminology in patents. This study uses a lexical analyzer produced using a deep learning structure to overcome this limitation. Furthermore, the proposed method uses quantitative information and bibliographic information of patents as explanatory variables and classifies the technology into multiple classes. The multi-class classification is conducted by sequentially evaluating the value of a technology. This method returns multiple classes in order, enabling class comparison. Moreover, it is model-agnostic, enabling diverse algorithms to be used. We conducted experiments using actual patent data to examine the practical applicability of the proposed methodology. Based on the experiment results, the proposed method was able to classify actual patents into an ordered multi-class. In addition, it was possible to guarantee the objectivity of the results. This is because our model used the information in the patent specification. Furthermore, the model using both quantitative and bibliographic information exhibited higher classification performance than the model using only quantitative information. Therefore, the proposed model can contribute to the sustainable growth of companies by classifying the value of technology into more detailed categories.
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Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis. INFORMATION 2020. [DOI: 10.3390/info11060333] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.
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A Methodology of Partner Selection for Sustainable Industry-University Cooperation Based on LDA Topic Model. SUSTAINABILITY 2019. [DOI: 10.3390/su11123478] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.
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A Multi-Risk Approach to Climate Change Adaptation, Based on an Analysis of South Korean Newspaper Articles. SUSTAINABILITY 2018. [DOI: 10.3390/su10051596] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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