• Proceedings of the Plant Resources Society of Korea Conference
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• 2021.04a
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• pp.45-45
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• 2021

This study aimed to explore research on bibliometric features of cannabis by applying scientometric analysis method, and to approach experimental research evaluation based on it. A total of 30,352 articles on cannabis published since 2001 from SCOPUS were analyzed using KnowledgeMatrix Plus and VOSviewer software. Results showed differences in research activities in countries where cannabis is legalized (Canada, the United States, the Netherlands) and Asian countries where its use is illegal. Related to medical cannabis, there has been a noticeable increase in the number of studies on pain, epilepsy, seizures and brain diseases such as multiple sclerosis. In the field of basic research, the number of pharmacological studies of cannabis on the cannabinoid type 1 receptor signaling pathway and inflammation and obesity has increased significantly. Subsequent experimental studies have compared the anti-inflammatory effects of four parts of Korean cannabis such as root, stem, leaf, and bark. Consistent with the predicted results of the scientometric analysis, all parts of C. sativa showed inhibitory effects on COX-2, NO/iNOS and TNF-α expression in LPS-stimulated RAW264.7 cells. These attempts provide an experimental research approach based on scientometric assessment.

• Journal of Industrial Technology
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• v.9
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• pp.109-117
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• 1989

In the finite element analysis of semiconductor devices analysis, the solution often be diverged due to the numerical instability of discretized equations. To overcome this problems, a noble finite element code which guarantees a successful convergence is developed. The factor of divergence in the current continuity equation of semiconductor governing equations is derived using stability test and an adaptive mesh refine scheme is introduced to eliminates the divergence properties. A test calculation of GaAs MESFET model reveals that the proposed scheme has a robust self-convergence property and is suitable for the semiconductor devices analysis.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.1-24
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• 2017

With the rise of internet of things (IoT), there have been several studies to analyze the technological trend and technological convergence. However, previous work have been relied on the qualitative work that investigate the IoT trend and implication for future business. In response, this study considers the patent information as the proxy measure of technology, and conducts a quantitative and analytic approach for analyzing technological convergence using patent co-classification analysis and text mining. First, this study investigate the characteristics of IoT business, and characterize IoT business into four dimensions: device, network, platform, and services. After this process, total 923 patent classes are classified into four types of IoT technology group. Since most of patent classes are classified into device technology, we developed a co-classification network for both device technology and all technologies. Patent keywords are also extracted and these keywords are also classified into four types: device, network, platform, and services. As a result, technologies for several IoT devices such as sensors, healthcare, and energy management are derived as a main convergence group for the device network. For the total IoT network, base network technology plays a key role to characterize technological convergence in the IoT network, mediating the technological convergence in each application area such as smart healthcare, smart home, and smart grid. This work is expected to effectively be utilized in the technology planning of IoT businesses.

• Korean Circulation Journal
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• v.53 no.1
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• pp.52-54
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• 2023

• ETRI Journal
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• v.34 no.3
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• pp.439-449
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• 2012

In recent innovation trends, one notable feature is the merging and overlapping of technologies: in other words, technological convergence. A key technological convergence is the fusion of biotechnology (BT) and information technology (IT). Major IT advances have led to innovative devices that allow us to advance BT. However, the lack of data on IT-BT convergence is a major impediment: relatively little research has analyzed the inter-disciplinary relationship of different industries. We propose a systematic approach to analyzing the technological convergence of BT and IT. Patent analysis, including citation and co-classification analyses, was adopted as a main method to measure the convergence intensity and coverage, and two portfolio matrices were developed to manage the technological convergence. The contribution of this paper is that it provides practical evidences for IT-BT convergence, based on quantitative data and systematic processes. This has managerial implications for each sector of IT and BT.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.53-58
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• 2024

This study is about the technology convergence analysis of e-commerce related patents containing Artificial Intelligence applied for in Korea. The relationships between core technologies were analyzed and visualized using social network analysis. As a result of social network analysis, the core IPC codes that make up the mutual technology network in e-commerce related patents containing Artificial Intelligence were found to be G06Q, G06F, G06N, G16H, G10L, H04N, G06T, and A61B. In particular, it can be confirmed that there is an important convergence of data processing-related technologies such as [G06Q-G06F], [G06Q-G06N], and voice and image signals such as [G06Q-G10L], [G06Q-H04N], and [G06Q-G06T]. Using this research method, it is possible to identify future technology trends in e-commerce related patents and create new Business Models.

• International Journal of Contents
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• v.12 no.3
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• pp.54-58
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• 2016

In this study, articles of the science and technology field that had been monitored for the period from 2002 to 2013 using GTB (Global Trends Briefing) were analyzed. Specifically, the VOSviewer was used to analyze the annual science and technology trends by keyword and the science and technology standard-classification information indicated in the GTB articles, and the convergence trends were therefore monitored. The findings of this study show that active studies were under way in the fields of material science and new and renewable energy, and that convergence has progressed. This result indicates that the information of the articles on papers and patents is more reliable, as it can reflect the current trends more rapidly in the science and technology field than the paper information or the patent information that is traditionally used in analyses of science and technology information.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.139-146
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• 2017

The interest about Artificial Intelligence through the AlphaGo Match in Korea has been increasing rapidly. So far, very little has been done in Artificial Intelligence. The aim of this paper is to reveal technology convergence and to assess the country profile in the field of artificial intelligence(AI). Technology convergence map was created after extracting USPTO patent grants and Web of Science data and generating matrics in the field of AI. Several Indicators were obtained by extracting and calculating SCOPUS Data that KISTI has. According to USPTO patent grants, it shows that AI technology has a strong relationship with several sectors such as cost/price determination, image analysis, and surgery, etc. Also, AI has a active convergence with some fields of Electrical and Electronic Engineering, BioTechnologies, and Medicine etc. According to country profile analysis, Korea reaches a global average growth index. However, in terms of specialization index (SI) and average of relative citations (ARC), there is a large gap between Korea and research leading countries.

• Mass Spectrometry Letters
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• v.2 no.3
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• pp.69-72
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• 2011

Defective synthesis of the steroid hormones by the adrenal cortex has profound effects on human development and homeostasis. Due to the time-consuming chromatography procedure combined with mass spectrometry, the matrix-assisted laser desorption ionization method coupled to the linear ion-trap tandem mass spectrometry (MALDI-LTQ-MS/MS) was developed for quantitative analysis of 10 adrenal steroids in human serum. Although MALDI-MS can be introduced for its applicability as a high-throughput screening method, it has a limitation on reproducibility within and between samples, which renders poor reproducibility for quantification. For quantitative MALDI-MS/MS analysis, the stable-isotope labeled internal standards were used and the conditions of crystallization were tested. The precision and accuracy were 3.1~35.5% and 83.8~138.5%, respectively, when a mixture of 10 mg/mL ${\alpha}$-cyano-4-hydroxycinnamic acid in 0.2% TFA of 70% acetonitrile was used as the MALDI matrix. The limit of quantification ranged from 5 to 340 ng/mL, and the linearity as a correlation coefficient was higher than 0.988 for all analytes in the calibration range. Clinical applications include quantitative analyses of patients with congenital adrenal hyperplasia. The devised MALDI-MS/MS technique could be successfully applied to diagnosis of clinical samples.

• ETRI Journal
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• v.37 no.6
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• pp.1231-1241
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• 2015

In this study, we investigate the relationship between emotions and the physiological responses, with emotion recognition, using the proposed fuzzy information granulation-based neural network (FIGNN) for boredom, pain, and surprise emotions. For an analysis of the physiological responses, three emotions are induced through emotional stimuli, and the physiological signals are obtained from the evoked emotions. To recognize the emotions, we design an FIGNN recognizer and deal with the feature selection through an analysis of the physiological signals. The proposed method is accomplished in premise, consequence, and aggregation design phases. The premise phase takes information granulation using fuzzy c-means clustering, the consequence phase adopts a polynomial function, and the aggregation phase resorts to a general fuzzy inference. Experiments show that a suitable methodology and a substantial reduction of the feature space can be accomplished, and that the proposed FIGNN has a high recognition accuracy for the three emotions using physiological signals.