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RISS 인기검색어
- 검색키워드
- 저자 : Zhen-Shu Mi (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Zhen-Shu Mi,Yong-Gi Kim 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.3
This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.
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Mi, Zhen-Shu,Bukhari, Ahmad C.,Kim, Yong-Gi Hindawi Limited 2014 Mathematical problems in engineering Vol.2014 No.-
<P>The autonomous underwater vehicle (AUV) and the problems associated with its safe navigation have been studied for the last two decades. The real-time underwater obstacle recognition procedure still has many complications associated with it and the issue becomes worse with vague sensor data. These problems can be coped with the merger of a robust classification mechanism and a domain knowledge acquisition technique. In this paper, we introduce a hybrid mechanism to recognize underwater obstacles for AUV based on fuzzy domain ontology and support vector machine (SVM). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years and is a new generation learning system based on recent advances in statistical learning theory. The amalgamation of fuzzy domain ontology with SVM boosts the performance of the obstacle recognition module by providing the timely semantic domain information of the surrounding circumstances. Also the reasoning ability of the fuzzy domain ontology can expedite the obstacle avoidance process. In order to evaluate the performance of the system, we developed a prototype simulator based on OpenGL and VC++. We compared the outcomes of our proposed technique with backpropagation algorithm and classic SVM based techniques.</P>
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Mi, Zhen-Shu,Kim, Yong-Gi Korean Institute of Intelligent Systems 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.3
This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.
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Yifan Wang,Hao Wei,Zhen Song,Liqun Jiang,Mi Zhang,Xiao Lu,Wei Li,Yuqing Zhao,Lei Wu,Shuxian Li,Huijuan Shen,Qiang Shu,Yicheng Xie 고려인삼학회 2024 Journal of Ginseng Research Vol.48 No.1
Background: Lung inflammation occurs in many lung diseases, but has limited effective therapeutics. Ginseng andits derivatives have anti-inflammatory effects, but their unstable physicochemical and metabolic propertieshinder their application in the treatment. Panaxadiol (PD) is a stable saponin among ginsenosides. Inhalationadministration may solve these issues, and the specific mechanism of action needs to be studied. Methods: A mouse model of lung inflammation induced by lipopolysaccharide (LPS), an in vitro macrophageinflammation model, and a coculture model of epithelial cells and macrophages were used to study the effectsand mechanisms of inhalation delivery of PD. Pathology and molecular assessments were used to evaluate efficacy. Transcriptome sequencing was used to screen the mechanism and target. Finally, the efficacy andmechanism were verified in a human BALF cell model. Results: Inhaled PD reduced LPS-induced lung inflammation in mice in a dose-dependent manner, includinginflammatory cell infiltration, lung tissue pathology, and inflammatory factor expression. Meanwhile, the dose ofinhalation was much lower than that of intragastric administration under the same therapeutic effect, which maybe related to its higher bioavailability and superior pharmacokinetic parameters. Using transcriptome analysisand verification by a coculture model of macrophage and epithelial cells, we found that PD may act by inhibitingTNFA/TNFAR and IL7/IL7R signaling to reduce macrophage inflammatory factor-induced epithelial apoptosisand promote proliferation. Conclusion: PD inhalation alleviates lung inflammation and pathology by inhibiting TNFA/TNFAR and IL7/IL7Rsignaling between macrophages and epithelial cells. PD may be a novel drug for the clinical treatment of lunginflammation.
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