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RISS 인기검색어
- 검색키워드
- 저자 : Jui-Ling Yu (검색결과 3 건)
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Viability and Functions of Alginate-microencapsulated Islets Isolated from Neonatal Pigs
Lin, Yi-Juain,Wang, Jui-Ping,Chung, Yu-Tung,Sun, Yu-Ling,Chou, Yu-Chi Asian Australasian Association of Animal Productio 2007 Animal Bioscience Vol.20 No.5
Patients with Type I diabetes mellitus have been treated with porcine insulin for several decades and pigs have recently been deemed an ideal source of microencapsulated islet cells for clinical xenotransplantation. In this study, neonatal pigs were anesthetized and sacrificed prior to a pancreatectomy. Islet cells were isolated from pancreas via collagenase digestion. Islet cells were separated and collected by hand under microscopic guidance. These cells were suspended in 1.4% sodium alginate solution and encapsulated by dropping them into 1.1% calcium chloride solution and in which the round gel in size was 250-400 ${\mu}m$ in diameter. Viability of the microencapsulated islet cells cultured in medium at $37^{\circ}C$ was assessed by MTT assay. Furthermore, insulin released in response to glucose challenge was investigated using an enzyme-linked immunosorbent assay. Secretion of insulin was low in response to the basal glucose solution (4.4 mM) in medium and was significantly higher in response to the high glucose solution (16.7 mM). The viability of microencapsulated islet cells did not differ significantly over a period of 7 days; that is, the increasing pattern of insulin concentration in the culture medium after glucose stimulation interval day was similar throughout the 7 days cultivation. In summary, experimental evidences indicated that the effects of alginate-microencapsulation prolonged survival of the neonatal porcine islets in vitro cultures and the insulin response to glucose of the islets was maintained.
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Anomaly Detection Using LibSVM Training Tools
Jung-Chun Liu,Chu-Hsing Lin,Jui-Ling Yu,Wei-Shen Lai,Chia-Han Ho 보안공학연구지원센터 2008 International Journal of Security and Its Applicat Vol.2 No.4
Intrusion detection is the means to identify the intrusive behaviors and provide useful information to intruded systems to respond fast and to avoid or reduce damages. In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool. By using this tool, we get rid of numerous and complex operations and do not have to use external tools for finding parameters as needed by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach.
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Genetic Algorithm for Shortest Driving Time in Intelligent Transportation Systems
Chu-Hsing Lin,Jung-Chun Liu,Chia-Han Ho,Jui-Ling Yu,Wei-Shen Lai 보안공학연구지원센터 2009 International Journal of Hybrid Information Techno Vol.2 No.1
The route guidance system, which provides driving advice based on traffic information about an origin and a destination, has become very popular along with the advancement of handheld devices and the global position system. Since the accuracy and efficiency of route guidance depend on the accuracy of the traffic conditions, the route guidance system needs to include more variables in calculation, such as real time traffic flows and allowable vehicle speeds. As variables considered by the route guidance system increase, the cost to compute multiplies. Since handheld devices have limited resources, it is not feasible to use them to compute the exact optimal solutions in real time systems by some well-known algorithm, such as the Dijkstra’s algorithm, which is usually used to find the shortest path with a map of reasonable numbers of vertices. To solve this problem, we propose to use the genetic algorithm to alleviate the rising computational cost. We use the genetic algorithm to find the shortest time in driving with diverse scenarios of real traffic conditions and varying vehicle speeds. The effectiveness of the genetic algorithm is clearly demonstrated when applied on a real map of modern city with very large vertex numbers.
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