• 제목/요약/키워드: LMBP

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LMBP를 이용한 주거용 부하의 동특성 모델링 (Dynamic Modeling of Residential Load Using LMBP)

  • 이종필;임재윤;김성수;지평식
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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    • pp.212-213
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    • 2006
  • Load models are important for improving the accuracy of stability analysis and power flow studies. Load characteristics change for different voltages and frequencies. In this research, ANN is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Experiments and modeling results are presented in conclusions.

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웨이블렛 변환과 LMBP를 이용한 대뇌출혈성 병변 인식 시스템 (Intracranial Hemorrhagic Lesion Feature Extraction System Of Using Wavelet Transform and LMBP)

  • 정유정;정채영
    • 한국정보과학회:학술대회논문집
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    • 한국정보과학회 2002년도 가을 학술발표논문집 Vol.29 No.2 (2)
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    • pp.625-627
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    • 2002
  • 본 논문에서는 의료영상 인식 기술 중 인식률이 뛰어나고 신뢰성 있는 대뇌출혈성 병변인식 시스템을 구현하기 위하여 신호처리 분야에서 주로 사용되는 Wavelet 변환과 신경망 기법을 이용하고자 한다. 그러나 신경망 기법에서 주로 사용되는 비선형 최적화기법인 Gradient descent BP는 최적화 문제점을 해결하기에는 수렴속도가 느리기 때문에 적합하지 않다. 따라서 본 논문에서는 기존 Gradient descent BP를 보완한 Levenberg-Marquardt Back-Propagation을 대뇌출혈성 병변인식에 적용하여 구현함으로써 총 50개의 패턴 중 45개의 영상이 인식에 성공하였고 전체 평균 인식률은 각각 90%와 87%의 인식률을 보였다.

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A Morphometric Aspect of the Brachial Plexus in the Periclavicular Region

  • Lee, Jung-Pyo;Chang, Jae-Chil;Cho, Sung-Jin;Park, Hyung-Ki;Choi, Soon-Kwan;Bae, Hack-Gun
    • Journal of Korean Neurosurgical Society
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    • 제46권2호
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    • pp.130-135
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    • 2009
  • Objective: The purpose of this study was to determine the normal morphometric landmarks of the uniting and dividing points of the brachial plexus (BP) in the periclavicular region to provide useful guidance in surgery of BP injuries. Methods: A total of 20 brachial plexuses were obtained from 10 adult, formalin-fixed cadavers. Distances were measured on the basis of the Chassaignac tubercle (CT), and the most lateral margin of the BP (LMBP) crossing the superior and inferior edge of the clavicle. Results: LMBP was located within 25 mm medially from the midpoint in all subjects. In the supraclavicular region, the upper trunk uniting at 21$\pm$7 mm from the CT, separating into divisions at 42$\pm$5 mm from the CT, and dividing at 19$\pm$4 mm from the LMBP crossing the superior edge of the clavicle. In the infraclavicular region, the distance from the inferior edge of the clavicle to the musculocutaneous nerve (MCN) origin was 49$\pm$12 mm, to the median nerve origin 57$\pm$7 mm, and the ulnar nerve origin 48$\pm$6 mm. From the lateral margin of the pectoralis minor to the MCN origin the distance averaged 3.3$\pm$10 mm. Mean diameter of the MCN was 4.3$\pm$1.1 mm (range, 2.5-6.0) in males (n = 6), and 3.1$\pm$1.5 mm (range, 1.6-4.0) in females (n = 4). Conclusion: We hope these data will aid in understanding the anatomy of the BP and in planning surgical treatment in BP injuries.

전통 발효식품으로부터 분리된 Weisella sp. strain YSK01에 의한 저분자 Biopolymer 발효생산 공정 및 생성물의 특성 (Characterization and Production of Low Molecular Weight of Biopolymer by Weisella sp. strain YSK01 Isolated from Traditional Fermented Foods)

  • 조현아;김남철;유선균
    • 한국응용과학기술학회지
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    • 제39권5호
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    • pp.632-643
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    • 2022
  • Although probiotics have been shown to improve health when consumed, recent studies have reported that they can cause unwanted side effects due to bacterial-human interactions. Therefore, the importance of prebiotics that can form beneficial microbiome in the gut has been emphasized. This study isolated and identified bacteria capable of producing biopoymer as a candidate prebiotic from traditional fermented foods. The isolated and identified strain was named WCYSK01 (Wissella sp. strain YSK01). The composition of the medium for culturing this strain was prepared by dissolving 3 g K2HPO4, 0.2 g MgSO4, 0.05 g CaCl2, 0.1 g NaCl in 1 L of distilled water. The LMBP(low molecular weight biopoymers) produced when fermentation was performed with sucrose and maltose as substrates were mainly consisted of DP3 (degree of polymer; isomaltotriose), DP4 (isomaltotetraose), DP5 (isomaltopentaose), and DP6 (isomaltoheptaose). The optimization of LMBP (low molecular weight of biopolymer) production was performed using the response surface methodology. The fermentation process temperature range of 18 to 32℃, the fermentation medium pH in the range of 5.1 to 7.9. The yield of LMBP production by the strain was found to be significantly affected by q fermentation temperature and pH. The optimal fermentation conditions were found at the normal point, and the production yield was more than 75% at pH 7.5 and temperature of 23℃.

추계학적모형과 신경망모형을 연계한 병렬저수지군의 유입량산정 (Streamflow Estimation using Coupled Stochastic and Neural Networks Model in the Parallel Reservoir Groups)

  • 김성원
    • 한국수자원학회논문집
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    • 제36권2호
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    • pp.195-209
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    • 2003
  • 본 연구에서는 낙동강 상류유역의 병렬 다목적댐군인 안동 및 임하다목적 댐의 장기간 유입량을 산정하는데 공간추계 신경망모형이 사용되었다. 공간추계 신경망모형은 역전파 알고리즘으로 LMBP와 BFGS-QNBP를 각각 사용하였다. 공간추계 신경망모형의 구조는 입력층, 은닉층 및 출력층의 3개의 층과 차례대로 8-8-2개의 노드로 구성되어 있다. 입력층 노드는 안동 및 임하다목적 댐의 월평균유입량, 월면적강우량, 월별 증발접시 증발량과 월평균기온으로 구성되어 있으며, 자료시계열은 시간적으로 차이가 있다. 공간추계 신경망모형의 훈련을 위하여 추계학적 모형중 하나인 PARMA(1,1)에 의해서 훈련자료를 모의발생시켰으며, 모의발생된 자료는 공간추계 신경망모형의 훈련에 사용되었다. 훈련을 통하여 공간추계 신경망모형의 매개변수인 최적연결강도와 편차를 산정하였다. 산정된 매개변수는 안동 및 임하다목적 댐의 실측자료를 이용하여 공간추계 신경망모형의 검증에 이용되었으며, 통계분석과 수문곡선의 비교를 통하여 우수한 결과를 나타내었다. 따라서 공간추계 신경망모형은 낙동강 상류유역의 병렬저수지군의 장기간 연계운영기법 개발을 위하여 기초적인 자료를 제공하고, 용수분배 및 관리에 도움을 줄 것이다.

신경회로망 부하모델을 이용한 과도안정도 해석기법 (A Method of Transient Stability Analysis Using ANN)

  • 이종필;임재윤;김성수;지평식
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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    • pp.329-331
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    • 2006
  • Load models are important for improving the accuracy of stability analysis. Load characteristics are changed for voltage and frequency condition. In this research, ANN with LMBP learning rule is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Constructed ANN load model are applied to transient stability analysis.

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뉴로-퍼지 추론 시스템 기반 주거용 부하의 모델링 기법 (Residential Load Modeling Method Based on Neuro-Fuzzy Inference System)

  • 지평식;이종필;이대종;임재윤
    • 전기학회논문지P
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    • 제60권1호
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    • pp.6-12
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    • 2011
  • In this study, we proposed a residential load modeling method based on neuro-fuzzy inference system by considering of various harmonics. The developed method was implemented by using harmonic information, fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with a conventional method based on neural networks.

인공신경회로망을 이용한 소형 모터의 조립 불량 판별 시스템 개발 (Development of A Fault Diagnosis System for Assembled Small Motors Using ANN)

  • 이상민;조중선
    • 한국정밀공학회지
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    • 제18권11호
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    • pp.124-131
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    • 2001
  • Fault diagnosis of an assembled small motor relies usually on human experts hearing ability. The quality of diagnosis depends, however, heavily on physical conditions of the human experts. A fault diagnosis system for assembled small motors is developed using artificial neural network (ANN) in this paper. It is consisted of sound sampling device and fault diagnosis software package. Six parameters are defined to characterize the sampled sound waves. The Levenberg-Marquardt Backpropagation (LMBP) Algorithm is used to diagnose the fault of assembled small motors. Experimental results for more than two hundred small motors verify the performance of the developed system.

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Shalt-Term Hydrological forecasting using Recurrent Neural Networks Model

  • Kim, Sungwon
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2004년도 학술발표회
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    • pp.1285-1289
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    • 2004
  • Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

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과도안정도 해석을 위한 지능형 부하모델의 새로운 접근법 (Nobel Approaches of Intelligent Load Model for Transient Stability Analysis)

  • 이종필;임재윤;지평식
    • 전기학회논문지P
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    • 제57권2호
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    • pp.96-101
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    • 2008
  • The field of load modeling has attracted the attention since it plays an important role for improving the accuracy of stability analysis and power flow estimation. Also, load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, precision load modeling technique and reasonable application method is needed for more accurate power system analysis. In this paper, we develop an intelligent load modeling method based. on neural network and application techniques for power system. The proposed method makes it possible to effectively estimate the load model for nonlinear models as well as linear models. Reasonable application method is also proposed for stability analysis. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.