• Journal of Power Electronics
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• 제19권6호
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• pp.1566-1574
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• 2019

Maximum power point tracking (MPPT) under the partial shading condition is a challenging research topic for photovoltaic systems. Shaded photo-voltaic module result in complex peak patterns on the power versus voltage curve which can misguide classical MPPT algorithms. Thus, various kinds of global MPPT algorithms have been studied. These have typically consisted of partial shading detection, global peak search and MPPT. The conventional partial shading detection algorithm aims to detect all of the occurrences of partial shading. This results in excessive execution of global peak searches and discontinuous operation of the MPPT. This in turn, reduces the achievable power for the PV module. Based on a theoretical investigation of power verse voltage curve patterns under various partial shading conditions, it is realized that not all the occurrences of partial shadings require a global peak search. Thus, an intelligent partial shading detection algorithm that provides exact identification of global peak search necessity is essential for the efficient utilization of solar energy resources. This paper presents a new partial shading determinant algorithm utilizing adaptive threshold levels. Conventional methods tend to be too sensitive to sharp shading patterns but insensitive to smooth patterns. However, the proposed algorithm always shows superb performance, regardless of the partial shading patterns.

• 한국환경보건학회지
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• 제30권1호
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• pp.15-21
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• 2004

In an attempt to analyze the characteristics of domestic pathogenic strains of S. pneumoniae, the basic epidemiological charactristics of pathogenic strains such as their serotypes and frequency of penicillin resistance, and pattern of chromosomal DNA from PFGE(pulsed-field gel electrophoresis) were observed. For this study,56 strains of S. pneumoniae isolated from inpatients and outpatients in the four domestic university hospitals were collected from January to December in 1998. Among those strains, a total of 56 pathogenic strains from blood(39 isolates), cerebrospinal fluid(8 isolates) and other specimen(9 isolates) were selected and isolated. The penicillin resistance frequency of those 56 strains was identified with disk diffusion method with 66.1％. From the invasive strains, predominant serotypes were isolated in the order of 19F(12.5％), 23F(10.7%), 14(10.7%) and 9V(10.7％), totalling 45 percent. This experiment also used PFGE patterns to compare the correlations among genetic subtypes in several serotypes. The DNA fragments digested with Sma I and Apa I were resolved by PFGE. The PFGE patterns digested with Sma I were better than Apa I for analysis. In the DNA fragments digested with Sma 1, PFGE analysis of 56 S. pneumoniae isolates showed 25 different patterns. As a result, serotype was on the whole correlated to PFGE pattern on the ground that each different PFGE pattern by serotype was observed. This study can be utilized not only fur the study of incidence trend of domestic pneumococcal diseases but also as a useful basic data for the development of identification tool and treatment.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 한국음향학회지
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• 제19권8호
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• pp.63-67
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• 2000

본 논문에서는 화자인식의 성능향상을 위한 dynamic time warping (DTW) 기반의 문맥 제시형 화자인식에 대해 연구하였다. 화자인식에 있어 중요한 요소인 화자의 특성을 잘 반영할 수 있는 참조패턴을 생성하기 위해 유전자 알고리즘을 적용하였다. 또한, 문맥 종속형과 문맥 독립형 화자인식의 단점을 개선하기 위해 문맥 제시형 화자인식을 수행하였다. Clos set에서 화자식별과 open set에서 화자확인 실험을 하였으며 실험결과 기존 방법의 참조패턴을 이용하였을 경우보다 유전자 알고리즘에 의한 참조패턴이 인식률과 인식속도 면에서 우수함을 보였다.

• 대한의생명과학회지
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• 제8권1호
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• pp.39-46
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• 2002

Amplified fragment length polymorphism (AFLP) is a recently developed PCR-based high resolution fingerprinting method that is able to generate complex banding patterns which can be used to delineate intraspecific genetic relationships among bacteria. In this study, we have modified and evaluated a PCR-based technique, amplified fragment length polymorphism (AFLP) analysis, for use in fingerprinting strains of Staphylococcus aureus. Single-enzyme amplified fragment length polymorphism (SE-AFLP) analysis was used to perform strain identification of Staphylococus aureus. By careful selection of AFLP primers, it was possible to obtain reproducible and sensitive identification to strain level. AFLP fingerprinting of 5 reference strains of Staphylococcus aureus and 65 strains of Staphylococcus aureus that were isolated from food sources of different area and diverse genomic types of Staphylococcus aureus were recognized. As a result of this study, we found that the AFLP patterns of Staphylococcus aureus isolated from Seoul, Taejeon and Gwang-Ju indicated the close relation with genetic similarity. The main purpose of this study was to find an alternative and reliable fingerprinting method to study the overall genetic diversity, using Staphylococcus aureus species as an example, and observed if the method can be successfully applied to all staphylococcal species.

• Journal of Microbiology
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• 제35권3호
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• pp.172-176
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• 1997

The Mycobacterium tuberculosis clinical isolates in Korea, showing different drug resistances, were analyzed by comparing large restriction fragment (LRF) patterns produced y digestion of genomic DNA with infrequent-cutting endonucleases of SpeI, AsnI and pulsed-field gel electrophoresis (PFGE). SpeI and AsnI allowed with AsnI and SpeI, strains yielded an absolutely identical pattern for Korean type's mycobacteria even though they showed different drug resisstance. However, when three M. tuberculosis strains, showing drug resistance, were digested with XbaI, patterns were different from those of the other M. tuberculosis strians which are susceptible to drugs. This stuyd reveals that the comparison of chromosomal restriction patterns is very useful as an additional aid for the differentiation and identification of M. tuberculosis strains showing drug resistances.

• 제어로봇시스템학회논문지
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• 제12권10호
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• pp.968-974
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• 2006

We are moving into the era of ubiquitous computing. Ubiquitous Sensor Network (USN) is a base of such computing paradigm, where recognizing the identification and the position of objects is important. For the object identification, RFID tags are commonly used. For the object positioning, use of sensors such as laser and ultrasonic scanners is popular. Recently, there have been a few attempts to apply RFID technology in robot localization by replacing the sensors with RFID readers to achieve simpler and unified USN settings. However, RFID does not provide enough sensing accuracy for some USN applications such as robot navigation, mainly because of its inaccuracy in distance measurements. In this paper, we describe our approach on achieving accurate navigation using RFID. We solely rely on RFID mechanism for the localization by providing coordinate information through RFID tag installed floors. With the accurate positional information stored in the RFID tag, we complement coordinate errors accumulated during the wheel based robot navigation. We especially focus on how to distribute RFID tags (tag pattern) and how many to place (tag granularity) on the RFID tag-floor. To determine efficient tag granularities and tag patterns, we developed a simulation program. We define the error in navigation and use it to compare the effectiveness of the navigation. We analyze the simulation results to determine the efficient granularities and tag arrangement patterns that can improve the effectiveness of RFID navigation in general.

• Fisheries and Aquatic Sciences
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• 제3권2호
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• pp.83-87
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• 2000

We have investigated lectin binding patterns in order to apply binding records of previous laboratory experiments to field settings before the first ourbreaks of harmful algal bloom (HAB). Although cells were grown under different conditions, the binding patterns were the same as in the control. In addition, culture days was not associated with the binding patterns, when compared with the control. In nature, this results suggest that ECA, HPA and WGA lectin are able to discriminate between C. polykrikoides and G. impudicum, as well as ECA and SBA have a capability as a tool for differentiating between C. polyrikoides and G. catenatum, although these species are closely similar under the light microscope fiexed with Lugol solution.

• 한국지능시스템학회논문지
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• 제21권4호
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• pp.407-413
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• 2011

본 연구에서는 가정용 전력 모니터링 시스템을 구현하고 실험가구에 적용하여 평가하였으며, 평가과정에서 확보한 기기별 전력 사용 패턴 정보를 이용하여 자동 기기 식별 알고리즘을 개발하였다. 실험가구에 적용해본 결과, 기기별 전력사용 정보와 월별 예상 사용량 정보가 전력 소비 절감에 도움이 된다는 응답을 얻을 수 있었다. 그리고 시스템을 보다 편리하게 사용하기 위해서는 설치의 편의성과 UI를 개선해야한다는 응답을 얻었다. 본 연구에서는 UI 개선을 위하여 일반냉장고, TV, 전기밥솥, 김치냉장고, 세탁기를 자동으로 식별하는 알고리즘을 구현하였다. 자동 장치 식별 알고리즘은 전력 모니터링 과정에서 수집한 전력 소비 패턴을 관찰하여 Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO), Duty Cycle(DC) 등 4가지 특징을 규정하여 이용하였으며, 특징을 적용하는 시간 구간은 기기가 동작하는 시간이 25% 이상이 되는 2시간 길이의 구간을 이용하였다. 제안된 알고리즘은 테스트 set에 동일한 기기를 포함하는 경우 82.1%의 성능을 얻을 수 있었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권9호
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• pp.603-614
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• 2013

모바일 디바이스의 발달은 사용자가 언제 어디서나 원하는 서비스에 접근하고, 정보를 소비할 수 있는 환경을 마련했다. 이에 맞춰 다양한 연구들이 모바일 사용자의 정보 접근성을 향상 시키기 위한 개인화 방법을 제안해 왔다. 하지만, 이와 같은 개인화는 사용자 개인과 관련된 정보를 요구하기에, 사용자 정보에 대한 보안과 관련된 우려를 낳고 있다. 이를 해결할 수 있는 효과적인 방법 중 하나로 사용자 정보를 사용자의 온라인 혹은 오프라인 상의 행동 패턴으로부터 추정하는 것을 들 수 있다. 본 논문에서는 SNS(Social Network Service) 상에서의 사용자 패턴과 사용자 간 물리적인 근접성 패턴을 분석하여 사용자 개인의 정보와 타 사용자와의 사회 관계정보를 식별하는 사용자 정보 식별 시스템을 제안하고자 한다. 제안한 시스템은 SNS 텍스트와 GPS 데이터에 기반한 POI(Point of Interest) 패턴으로부터 사용자의 나이, 성별 등 개인정보를 식별하고, 사용자 GPS 데이터를 이용하여 얻어진 사용자 간 근접성 패턴을 이용하여 두 사용자 간의 가족, 동료 등 관계 정보를 추정한다. 각각의 사용자 식별 모듈은 해당 데이터의 특성을 고려하여 SNS 데이터의 노이즈와 사용자 GPS 데이터의 손실을 감안함으로써 더 정확한 사용자 식별 성능을 보이도록 설계되었다. 이를 검증하기 위한 실험에서 제안한 시스템은 기존의 방법에 비해 더 나은 성능을 보였으며, 이는 본 논문에서 제안하는 방법이 사용자 데이터의 특성을 효과적으로 반영하고 있음을 의미한다.