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Alokali Kiba,Dipankar Saha,Bhrigu Kumar Das 한국실험동물학회 2023 Laboratory Animal Research Vol.39 No.3
Background: Globally, medicinal plants are used to treat diseases like diabetes. The present study evaluates the possible antioxidant, acute oral toxicity, the in-vitro and in-vivo antidiabetic potential of the hydro-ethanolic leaf extract of Koenigia polystachya (HELeKP) against beta-cell damage in experimentally induced diabetes mellitus. The DPPH (2,2-diphenyl-1-picrylhydrazine), ABTS [2,2′-azino bis-(3-ethylbenzothiazoline-6-sulfonic acid)], H2O2 (Hydrogen peroxide), superoxide radical scavenging activity and NO (Nitric oxide) assay estimated the in-vitro antioxidant assay of HELeKP. The acute oral toxicity study was evaluated per the OECD (Organization for Economic Cooperation and Development) test guidelines 425. Diabetes was stimulated in rats with a single dose of Streptozotocin (STZ), and after confirmation of diabetes, HELeKP was given orally for 21 days. Blood/serum samples were gathered and examined for biochemical changes, while tissue samples were evaluated for histopathological alterations. Results: The IC50 value of the HELeKP for all the anti-oxidant assays confirms the free radical scavenging activity. The data on acute oral toxicity revealed that the HELeKP used in the study was comparatively very safe. The outcomes of the in-vivo study suggested that the extract significantly reduced (p < 0.001) the fasting glucose level in STZ-induced diabetic rats. Furthermore, the lipid profile level was significantly normalized (p < 0.01, p < 0.001) in diabetic rats. The histopathological observation of the pancreas in HELeKP-treated rats showed significant beta-cell restoration. Conclusions: Based on the outcomes of this study, the HELeKP-treated rats have significant free radical scavenging and anti-diabetic potential. Therefore, it can be recommended as a beneficial functional vegetable for consumption.
Seabed Sediment Classification Algorithm using Continuous Wavelet Transform
Lee, Kibae,Bae, Jinho,Lee, Chong Hyun,Kim, Juho,Lee, Jaeil,Cho, Jung Hong Korean Society of Ocean Engineers 2016 Journal of advanced research in ocean engineering Vol.2 No.4
In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.
이기배(Kibae Lee),이지현(Jeehyun Lee),배진호(Jinho Bae),정명준(Myoung Jun Cheong),이종현(Chong Hyun Lee) 대한전자공학회 2017 대한전자공학회 학술대회 Vol.2017 No.6
In this paper, we propose passive sonar target signal separation algorithm using SDAE(Stacked Denoising Auto-Encoder). By using the proposed algorithm based on SDAE along with Fast ICA(Independent Component Analysis) algorithm, we obtained 4~5dB improved SNR of target signal. Also, we proposed separation algorithm using SDAE and filter bank designed by harmonic relation, which showed superior performance to existing BSS algorithms.