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Intelligent Digital Redesign of Uncertain Nonlinear Systems : Global approach
성화창(Hwachang Sung),주영훈(Younghoon Joo),박진배(Jinbae Park),김도완(Dowan Kim) 한국지능시스템학회 2005 한국지능시스템학회 학술발표 논문집 Vol.15 No.2
This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, we prove the effectiveness and stabilization of the proposed intelligent digital redesign method by applying the chaotic Lorentz system.
군견에서 분리한 Enterococcus faecalis, E. faecium의 항생제 내성 연구
박경완 ( Kyoungwan Park ),주영훈 ( Younghoon Joo ),조성범 ( Sungbum Cho ),방기만 ( Kiman Bang ),고명식 ( Myeongsik Ko ),유건주 ( Gunju Yoo ) 국군의무사령부 2017 대한군진의학학술지 Vol.48 No.1
Objective: Investigate antimicrobial resistance of enterococci isolated from various group of military working dogs(MWDs) to understand their characteristics and the differences between isolates from different groups. Methods: Fecal samples of military working dogs were collected from military working dog training center located in Chuncheon, Korea. Samples were collected by using sterile material and transported to laboratory within 12hrs. Species was confirmed by polymerase chain reaction. Antimicrobial susceptibility test was performed on E. faecalis, E. faecium, and other unidentified Enterococcus spp. isolates using disk diffusion method against 12 antibiotics. Results: First, in cohort study, we collected 193 feces samples from three group of puppy dogs and 200 Enterococcus spp. (149; 74.50% E. faecalis, 45; 22.50% E. faecium, 6; 3.00% other Enterococcus spp.) were isolated from this samples. All 200 isolates were susceptible to ampicillin, amoxicillin-clavulanic acid, chloramphenicol, imipenem. Antimicrobial resistance against aminoglycosides and ciprofloxacin of the isolates was increased with growth of the puppy dogs. In cross sectional study, total 137 samples including 127 feces and 10 soil samples were collected. 44 E. faecalis, 50 E. faecium, and 19 species unidentified Enterococcus were isolated from those samples. In case of refresher training dogs, the isolation rate of E. faecalis was high shortly after entering training center, but the isolation rate of E. faecium tended to increase immediately before leaving. Most antimicrobial resistance rates were higher in enterococci isolated from MWDs served MWDs training center than in enterococci isolated from refresher training MWDs served other field troops. Conclusion: Continuous monitoring of antimicrobial resistance is needed to consider the possibility of exposure to antibiotic-resistant bacteria in soldiers who live with dogs and select appropriate antibiotics for MWDs.
이산 Particle Swarm Optimization을 위한 다 수준 양자화 기법
송화창(Hwachang Song),라이언 디올라타(Ryan Diolata),주영훈(Younghoon Joo) 한국지능시스템학회 2009 한국지능시스템학회 학술발표 논문집 Vol.19 No.1
본 논문은 이산 PSO 문제에서 연속 값을 갖는 중간 해를 다중 값의 이산변수로 변환하는 기법을 설명하고자 한다. 본 논문은 다 수준 이산화를 위하여 지수 2의 멱급수로써 sigmoid 함수를 다 수준으로 분할하며, 이를 PSO의 이동 방정식에서 결정된 연속 값을 갖는 각 particle의 중간 해에 대한 이산화에 적용한다. 본 알고리즘을 배전시스템의 Photovoltaic 시스템 할당 문제에 적용하여 수치적 결과를 획득하고, GA 기법을 이용한 결과와 비교하여 그 적용가능성을 설명하고자 한다.
2019~2021년 육군 군부대 말라리아의 역학적 특성: 매개체 감시활동을 중심으로
최지완 ( Jeewan Choi ),김유진 ( Yujin Kim ),하범만 ( Beomman Ha ),신현일 ( Hyunil Shin ),이버들 ( Buddle Lee ),주영훈 ( Younghoon Joo ),김재형 ( Jaehyung Kim ) 국군의무사령부 2022 대한군진의학학술지 Vol.53 No.1
Objective: Military malaria patients continuously occur despite the preventive efforts, and the ROK Army started the malaria vector surveillance program co-operated with the Korea Disease Control and Prevention Agency (KDCA) since 2019. This study was performed to investigate malaria-epidemiological characteristics through malaria-vector surveillance during 2019∼2021. Method: Anopheles spp. were collected from the surveillance military units for 22 weeks from May to September during 2019∼2021. The ratio of Anopheles spp. was calculated, and Anopheles spp. infected with Plasmodium vivax were identified by nested polymerase chain reaction (nPCR). Correlations between the number of Anopheles spp. and the number of military patients were analyzed using the Pearson correlation test. Results: A total of 5,513 Anopheles spp. were collected, 2,866 in 2019, 1,500 in 2020, and 1,147 in 2021. The ratio of Anopheles spp. in total mosquitoes was 62.4%, and numbers of infected Anopheles spp. were 29 pools (17 in 2019, 11 in 2020, and 1 in 2021). There was a strong positive correlation between the number of Anopheles spp. and the number of military malaria-patients by year, but there was no correlation by month except for Paju-si in 2020. Conclusion: Military units had a higher ratio of Anopheles spp. than civilian region, and infected Anopheles species were also continuously collected, so it was necessary to comply with malaria prevention rules including chemoprophylaxis. From our study, we concluded that malaria vector surveillance is crucial and should be continued, and through malaria-vector surveillance, it could be possible to predict the occurrence of military malaria-patients.
권오국,박진배,주영훈,장욱 연세대학교 산업기술연구소 1998 논문집 Vol.30 No.1
This paper proposed the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behavior and design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical examples are provided to show the advantages of the proposed method.