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- 저자 : 金顯昌 (검색결과 3 건)
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金顯昌,權秀逸,鄭汶奎,張浚成 成均館大學校 1981 論文集 Vol.29 No.-
This paper describes the merit of the HgI_2 single crystals obtained by solution growth, 2-& 3-region temperature growth, and temperature oscillation growth for soft γ-ray detectors which can be operated at room temperature. Special efforts are put on the design, construction, and operation of the TOM single crystal growing furnace. Experimental results show that HgI_2 detectors fabricated by vapour phase growth method usually exibit sufficient enough detector characteristics for soft γ-ray spectrometry. However further investigation should be carried out to eliminate detector deterioration due to polarization effect.
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金顯昌,李鍾民,李毛星 성균관대학교 기초과학연구소 1985 論文集 Vol.36 No.1
The residual magnetic field exists out of the edge in mass analyzing magnet(fringing effect). Therefore, the trajectories of charged particles are affected by the fringing field. From a study of particle trajectories, it is known that the effective magnetic field boundary is not the mechanical boundary of the magnet but the virtual field boundary determined by ∫??h(S)ds=s_1 for the finging field function h(S). In this experiment, the effective field boundary is found to be 1.5cm from the mechanical boundary for the magnet with 1 inch air gap.
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금현창,변경원 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.3
Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and ‘diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into ‘purchase cluster’, ‘equipment cluster’, ‘diet cluster’, and ‘execute method cluster’. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.
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