• Journal of Ginseng Research
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• v.27 no.3
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• pp.135-140
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• 2003

North American ginseng (Panax quinquefolius L.) was analysed for total ginsenosides and ten major ginsenosides (R$_{0}$ , Rb$_1$, Rb$_2$, Rc, Rd, Re, Rf, Rg$_1$, pseudoginsenoside F$_{11}$ and gypenoside XVII), and variations in ginsenoside content with age of plant (over a four-year-period) and geographic location (Ontario versus British Columbia) were investigated. In the roots the total ginsenoside content increased with age up to 58-100 mgㆍg$^{-1}$ dry weights in the fourth year, but in leaves it remained constant over time. Roots and leaves, moreover, had different proportions of individual ginsenosides. The most abundant ginsenosides were Rb$_1$ (56mgㆍg$^{-1}$ for Ontario; 37mgㆍg$^{-1}$ for British Columbia) and Re (21mgㆍg$^{-1}$ for Ontario; 15 mgㆍg$^{-1}$ for British Columbia) in roots, and Rd (28-38 mgㆍg$^{-1}$ ), Re (20-25 mgㆍg$^{-1}$ ), and Rb$_2$ (13-19 mgㆍg$^{-1}$ ) in leaves. Measurable quantities of Rf were found in leaves (0.4-1.8 mgㆍg$^{-1}$ ) but not in roots or stems. Our results show that ginsenoside profiles in general, and Rf in particular, could be used for chemical fingerprinting to distinguish the different parts of the ginseng plant, and that ginseng leaves could be valuable sources of the ginsenosides Rd, Re, and Rb$_2$.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.113-120
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• 2017

In recent years, indoor localization has been researched for the improvement of its localization accuracy capability in Wi-Fi environment. The fingerprint and RF propagation models has been the main approach in determining indoor positioning. With the use of fingerprint, a low-cost, versatile localization system can be achieved without the use of external hardware. However, only a few research have been made on virtual access points (VAPs) among indoor localization models. In this paper, the idea of indoor localization system using fingerprint with the addition of VAP in Wi-Fi environment is discussed. The idea is to virtually add APs in the existing indoor Wi-Fi system, this would mean additional virtually APs in the network. The experiments of the proposed algorithm shows the positive results when 2VAPs are used compared with only APs. A combination of 3APs and 2VAPs in the 3rd case had the lowest average error of 3.99 among its 4 scenarios.